Machine Learning and Cybersecurity

Machine Learning
and Cybersecurity
AUTHORS
Micah Musser
Ashton GarriottHYPE AND REALITY
JUNE 2021

Center for Security and Emerging Technology 2Established in January 2019, the Center for Security and
Emerging Technology (CSET) at Georgetown’s Walsh
School of Foreign Service is a research organization fo-
cused on studying the security impacts of emerging tech-
nologies, supporting academic work in security and tech-
nology studies, and delivering nonpartisan analysis to the
policy community. CSET aims to prepare a generation of
policymakers, analysts, and diplomats to address the chal-
lenges and opportunities of emerging technologies. CSET
focuses on the effects of progress in artifi cial intelligence,
advanced computing, and biotechnology.
CSET.GEORGETOWN.EDU | CSET@GEORGETOWN.EDU
Machine Learning
and Cybersecurity JUNE 2021
AUTHORS
Micah Musser
Ashton GarriottHYPE AND REALITY
ACKNOWLEDGMENTS
For their comments, discussion, and review, we would like to thank
John Bansemer, Drew Lohn, Ben Buchanan, Cindy Martinez, Perri
Adams, Jim Richberg, Chris Rohlf, Jamie Butler, and the reviewers
from the MITRE Corporation: Mike Hadjimichael, George Roelke,
Scott Musman, Roshan Thomas, Sam Visner, Malachi Jones, Andy
Applebaum, Andres Molina-Markham, Kristin Esbeck, Lashon
Booker, Anne Townsend, and Frank Posluszny. For their assistance
editing and formatting this report, we would like to thank Alex
Friedland and Melissa Deng. Any errors that remain are the fault of
the authors alone.
AUTHORS
Micah Musser is a Research Analyst for the CyberAI team at CSET,
where Ashton Garriott is a former Semester Research Analyst.
PRINT AND ELECTRONIC DISTRIBUTION RIGHTS
© 202 1 by the Center for Security and Emerging Technology.
This work is licensed under a Creative Commons Attribution-
NonCommercial 4.0 International License.
To view a copy of this license, visit:
https://creativecommons.org/licenses/by-nc/4.0/.
DOCUMENT IDENTIFIER
doi: 10.5 1 593/2020CA004
Cover photo: https://www.flickr.com/photos/
byrawpixel/39726353 1 10.
Center for Security and Emerging TechnologyiEXECUTIVE SUMMARY
INTRODUCTION
1 | A FOUR -STAGE MODEL OF CYBERSECURITY
2 | TRADITIONAL MACHINE LEARNING AND CYBERSECURITY
Spam Detection
Intrusion Detection
Malware Detection
3 | CYBERSECURITY AND THE CUTTING EDGE OF AI RESEARCH
Prevention
Detection
Response and Recovery
Active Defense
CONCLUSIONS
APPENDIX
ENDNOTESIII
V
1
5Contents
13
297
7
9
15
19
22
25
37
39
Center for Security and Emerging Technologyiv
Center for Security and Emerging Technologyiiihe size and scale of cyber attacks has increased in recent years
as a result of a number of factors, including the increasing nor-
malcy of cyber operations within international politics, the grow-
ing reliance of industry on digital infrastructure, and the difficulties of
maintaining an adequate cybersecurity workforce. Many commentators
across government, media, academia, and industry have wondered how
cybersecurity professionals might be able to adapt machine learning for
defensive purposes. Could machine learning allow defenders to detect
and intercept attacks at much higher rates than is currently possible?
Could machine learning–powered agents automatically hunt for vul-
nerabilities or engage an adversary during an unfolding attack? Should
policymakers view machine learning as a transformational force for
cyber defense or as mere hype?
This report examines the academic literature on a wide range of appli-
cations combining cybersecurity and artificial intelligence (AI) to provide
a grounded assessment of their potential. It breaks cybersecurity practice
into a four-stage model and examines the impact that recent machine
learning innovations could have at each stage, contrasting these applica-
tions with the status quo. The report offers four conclusions:
• Machine learning can help defenders more accurately detect and
triage potential attacks. However, in many cases these technolo-
gies are elaborations on long-standing methods—not fundamen-
tally new approaches—that bring new attack surfaces of their own.Executive Summary
T
Center for Security and Emerging Technologyiv• A wide range of specific tasks could be fully or partially automated with the
use of machine learning, including some forms of vulnerability discovery,
deception, and attack disruption. But many of the most transformative of
these possibilities still require significant machine learning breakthroughs.
• Overall, we anticipate that machine learning will provide incremental ad-
vances to cyber defenders, but it is unlikely to fundamentally transform the
industry barring additional breakthroughs. Some of the most transformative
impacts may come from making previously un- or under-utilized defensive
strategies available to more organizations.
• Although machine learning will be neither predominantly offense-biased
nor defense-biased, it may subtly alter the threat landscape by making cer-
tain types of strategies more appealing to attackers or defenders.
This paper proceeds in four parts. First, it introduces the scope of the research
and lays out a simplified, four-stage schema of cybersecurity practice to frame the
different ways that future machine learning tools could be deployed. Second, it
contextualizes recent machine learning breakthroughs and their implications for cy -
bersecurity by examining the decades-long history of machine learning as applied
to a number of core detection tasks. The third and most substantial section examines
more recent machine learning developments and how they might benefit cyber
defenders at each stage of our cybersecurity schema, and considers whether these
newer machine learning approaches are superior to the status quo. Finally, a con-
cluding section elaborates on the four conclusions mentioned above and discusses
why the benefits of machine learning may not be as transformative in the immediate
future as some hope, yet are still too important to ignore.
Center for Security and Emerging TechnologyvIntroduction
s a typical internet user goes about her day, she will be quietly
protected by a bewildering number of security features on her
devices—some obvious, others less so. If she uses Gmail, Google
will automatically scan every email that arrives in her inbox to determine if
it is spam, and if the email contains attachments, those will also be scanned
to determine if they contain malware.1 Whether she uses Chrome, Firefox,
or Safari to browse the web, her browser will analyze every website she
visits and attempt to alert her if a site is malicious.2 If she uses an antivirus
product—among the most common of which are Microsoft Windows De -
fender, Symantec, and ESET—then the files on her device will be regularly
scanned to check for potentially malicious files.3
All of these services utilize machine learning to protect users from cyber
attacks. Over the past decade, the role of machine learning in cybersecu-
rity has been gradually growing as the threats to organizations become
more serious and as the technology becomes more capable. The increas-
ing commonality of cyber operations as a geopolitical tool means that
many organizations risk being targeted by well-resourced threat actors
and advanced persistent threats.4 At the same time, the supply of trained
cybersecurity professionals struggles to meet the growing need for exper-
tise.5 And to add to the sense of risk, a nearly endless stream of publica-
tions—including a recent report released by CSET—speculates about the
ways AI will be used to further intensify cyber attacks in the near future.6
In this climate, researchers, policymakers, and practitioners alike have
found themselves wondering if machine learning might provide the means
to turn the tide in the war against cyber attacks. Popular media outlets
frequently write about the possibility that machine learning could vastly A
Center for Security and Emerging Technologyviimprove attack detection, while many in the defense community talk about future AI
systems that could hunt for and dispatch intruders autonomously.7 “AI” has become
a mainstay in cybersecurity marketing materials, with advertisements comparing
AI products to everything from the immune system to swarms of organisms.8 Taking
these claims at face value, a policymaker paying loose attention to the state of AI
research might conclude that the total transformation of the cybersecurity industry at
the hands of machine learning algorithms is at most a few years away.
So it might surprise that same policymaker to learn that machine learning sys-
tems have been commonly used for a number of key cybersecurity tasks for nearly
20 years. While the breakthroughs of the last five years have rightfully drawn atten-
tion to the field of AI and machine learning (ML) research, it is easy to forget that the
algorithms behind these advances have in many cases been around for decades.9
Learning this, that same policymaker might look at the spate of recent high-profile
hacks and feel tempted to dismiss the potential of machine learning altogether. After
all, if machine learning has slowly been making its way into the cybersecurity indus-
try for decades, yet the rate and scale of cyber attacks hasn’t meaningfully gone
down, why should anyone believe that a transformation is just around the corner?
Part of the difficulty in evaluating machine learning’s potential for cybersecurity
comes from the fact that different parties have different standards for success. For
our purposes, it is useful to make a distinction between two types of standards that
we might use to evaluate the impact of a new technology: counterfactual standards
and historical standards.
Using counterfactual standards means that we ask the question: Where would
we be without machine learning, all else being equal? Approaching the question
from this angle should give us a great deal of appreciation for machine learning:
given that the number of events that signal a possible cyber attack has grown from
several hundred to several million per day in some industries, it’s surprising that most If machine learning has slowly been making its way into
the cybersecurity industry for decades, yet the rate and
scale of cyber attacks hasn’t meaningfully gone down,
why should anyone believe that a transformation is just
around the corner?
Center for Security and Emerging Technologyviicompanies aren’t entirely consumed by the task of keeping their IT infrastructure
secure.10 In no small part, this success is thanks to ML systems that can automatical-
ly screen out potential attacks, generate alerts on suspicious behavior, or perform
some rudimentary analysis of anomalous activity.
Counterfactual standards are what matter to the cybersecurity practitioner—the
person who knows the threat landscape and has to respond to it one way or an-
other. But policymakers also need insight into a set of more general questions: Is the
world getting more secure or less? How worried should we be over the long-term
about the shortage of cybersecurity talent? Can machine learning make us less
vulnerable to our adversaries in absolute terms? To answer these questions, we need
to use historical standards. Rather than comparing the world with machine learning
to a hypothetical world without it, we should compare the past to the present to the
(most likely) future.
That machine learning can offer significant benefits to cybersecurity practitioners
is broadly—though not universally—agreed upon.11 Whether these benefits will
amount to a “transformation” in cybersecurity is more contested. For the purposes
of this paper, “transformative impact” refers to an impact that makes a difference by
historical—and not merely counterfactual—standards. In the context of cybersecuri-
ty, this means that a technology should do more than help defenders simply keep up
in the face of growing threats: to be transformative, the technology should bring the
total number of threats requiring a human response down (and keep them down), or
it should meaningfully alter cybersecurity practice.
Ultimately, it is more important for policymakers to understand how machine
learning will transform cybersecurity than it is to quibble about whether it will bring
changes. For that reason, this paper explores the potential impact that a wide range
of ML advances could have on cybersecurity. We try to simultaneously present the
reader with a healthy dose of skepticism regarding some of the most-hyped ML That machine learning can offer significant benefits to
cybersecurity practitioners is broadly—though not univer -
sally—agreed upon. Whether these benefits will amount
to a “transformation” in cybersecurity is more contested.
Center for Security and Emerging Technologyviiiapplications in cybersecurity, while also drawing attention to some potential appli-
cations that have garnered less popular attention. And, in keeping with our empha-
sis on historical standards for success, we try to contextualize the recent growth of
interest in machine learning for cybersecurity by examining how it has already been
deployed in the past.
This is a large task, and unfortunately not everything relevant to machine
learning’s growing role in cybersecurity can be covered in this report. Our focus
is squarely on the technical—what machine learning could do. We sidestep many
important but non-technical issues, such as privacy or legal concerns surrounding
cybersecurity data collection, as well as the economics of implementing newer ML
methods. We also adopt a general model of cybersecurity and avoid organizations
that may have unique cybersecurity needs or goals, such as companies that rely on
industrial control systems.
This report is structured as follows: We begin in the first section by introducing a
four-stage model of cybersecurity practice that we will return to in later sections as
a way to schematize different ML applications. The second section, “Traditional Ma-
chine Learning and Cybersecurity,” discusses how decades-old ML methods have
been widely studied for three core cybersecurity tasks: spam filtering, intrusion de -
tection, and malware detection. Providing this historical context can help the reader
understand which ML applications are genuinely new innovations, and which ones
are merely extensions of long-standing trends in cybersecurity. In the third section,
“Cybersecurity and the Cutting Edge,” we turn our attention to newer advances in
ML research and examine how these methods might generate new types of applica-
tions at each part of our four-stage model of cybersecurity. This section emphasizes
lines of academic research that seem promising and may translate into fully func-
tional products within the near- to medium-term. The final section presents conclu-
sions that appear to follow from this research, along with some additional analysis.
Center for Security and Emerging Technology1he most well-known schema for conceptualizing cybersecurity is
the Cybersecurity Framework designed by the National Institute
of Standards and Technology (NIST).12 This framework broadly
breaks cybersecurity into five functions to help defenders assess their
level of risk: identify, protect, detect, respond, and recover. This paper
uses an adapted version of the NIST model that emphasizes four slight-
ly different categories: prevention, detection, response and recovery,
and active defense. There are two primary reasons to deviate from the
NIST framework. First, while there are many instances of machine learn-
ing being used to detect cyber attacks, the use of ML in the other four
categories of the NIST framework is still rather immature, which justifies
grouping multiple categories together for analysis. Second, “active de -
fense”—which we elaborate on below—is a growing area of interest that
is conceptually distinct from the other categories for both technical and
operational reasons; it therefore merits its own discussion. A Four-Stage Model
of Cybersecurity1
T
Center for Security and Emerging Technology2In this model, prevention refers to all the actions that security experts take to
minimize the vulnerabilities of their organization. At a minimum, prevention re -
quires that defenders make secure decisions regarding network configurations and
user privileges, and that they maintain an active knowledge of their networks and
software dependencies, patching known vulnerabilities in a timely fashion. For
software companies, prevention includes the work of evaluating one’s own prod-
ucts for vulnerabilities, preferably before a product is brought to market. For mid- or
large-sized organizations, prevention often requires frequent audits of one’s overall
security posture as well as periodic penetration testing, in which benign attacks are
launched against a network to discover and expose weaknesses. This category
roughly corresponds to the NIST categories of identification, which emphasizes as-
set management and security strategy, and protection, which focuses on the techno-
logical and policy controls that are used to secure assets.
No amount of anticipatory work can make an organization immune from cyber
attacks. When attacks do occur, detection systems are needed to quickly alert
defenders so that they can respond. A comprehensive set of detection systems must
monitor everything from network traffic to email attachments and employee be -
havior in order to identify anomalies and potential threats. Because attackers often
evade discovery for months after breaching a network, effective detection systems
can make an enormous difference to an organization’s ability to limit the impact of
cyber attacks.13 TABLE 1
A Comparison of Our Model and the
NIST Cybersecurity Framework
NIST FUNC TIONS OUR MODEL
Identif y
PreventionTABLE 1
Summary evaluations of GP T-3 performance on six
disinformation-related tasks.
Protect
Detect Detection
Respond
RecoverResponse and R ecovery
Active Defense N/A
Center for Security and Emerging Technology3Once an attack is detected, security professionals must determine how to re -
spond and recover. For some types of threats, the response is straightforward: in the
case of spam detection, for instance, an email service simply redirects mail that is
likely illegitimate into a spam folder. For many other types of attacks, however, the
appropriate response is far from clear. In the face of an ongoing attack, cyberse -
curity personnel must often decide whether to shut down machines, sequester parts
of their network, or otherwise take steps that could significantly disrupt an organi-
zation’s operations. An effective response must identify the scale and scope of an
attack, thwart the attacker’s access, and fully eliminate any foothold the attacker
might have. After doing so, it is important to restore the system to its original state
prior to the attack.
The triad of prevention, detection, and response and recovery forms the core
of cybersecurity. For most organizations, performing these tasks well is the height
of good cybersecurity practice. However, for organizations facing attacks from
well-resourced threat actors, compliance with pre-existing frameworks may not
be sufficient. These organizations must also ensure that they can flexibly adapt to
changes in the threat landscape.
To account for actions that allow organizations to respond more flexibly to
sophisticated threats, this report includes the additional stage of active defense. This
term is used analogously to the way the SANS Institute has used it: as a spectrum of
activity that includes annoyance, attribution, or outright counter-attack.14 Active de -
fense can be thought of as an “other” category that includes any attempt to delib-
erately engage or study external actors rather than simply responding to problems
as they arise. This category can be broken down into a few more clearly defined
subcategories, of which this report emphasizes three: (1) deception, or attempts to
mislead and slow down adversaries; (2) threat intelligence, or attempts to actively
study potential adversaries to better anticipate their actions; and (3) attribution, or
attempts to connect multiple events to a single entity that can then be studied in more
detail.\* Active defense, done well, can allow defenders to stay ahead of their ad-
versaries and can potentially create disincentives against attacking in the first place.
Section 3 will examine each of these four components of cybersecurity and ask
how newer ML architectures may play a role in augmenting current practices. But
before turning towards the future of machine learning in cybersecurity, it is important
\*This breakdown does not include any activities that would fall into the category of outright counter-
attack. Because this is a much more difficult subject with an often unclear status of legality, we do not
explore the possibility of using machine learning for counter-attack in this paper.
Center for Security and Emerging Technology4to get some sense of the past, because ML–based cyber tools are not as new as
many assume. In the next section, this report examines three ML applications that
have been studied for several decades: spam detection, intrusion detection, and
malware detection.
Center for Security and Emerging Technology5lthough the last decade has seen major advances in AI and ML
research, the use of ML methods for cybersecurity has a long
history. Until recently, these applications fell almost entirely into
the detection stage of cyber defense, with the most significant attention
paid to spam detection, intrusion detection, and malware detection. This
section discusses how simpler, longstanding ML approaches have his-
torically been adapted for these three applications. Figure 1 presents a
rough timeline of major developments in the cyber threat landscape and
how machine learning has evolved to combat them.
“Traditional machine learning” here refers to the broad set of de -
cades-old algorithms that were dominant in ML research until the major,
predominantly deep learning–driven advances of the past 5–10 years.\*
These methods are typically divided into one of two categories: supervised
learning and unsupervised learning. In supervised learning, labeled data-
sets are used to train a model to classify new inputs, while in unsupervised
learning, unlabeled datasets are examined to identify underlying patterns
within the data. Either approach can be useful for cyber defenders looking Traditional Machine
Learning and
Cybersecurity2
A
\* Some examples of traditional machine learning algorithms include naive Bayes
classifiers, random forests, k-means clustering, logistic regression models, and support
vector machines. It is worth noting that despite the name we use, “traditional machine
learning” is not itself a stagnant field; XGBoost, for instance, is a popular and newer
gradient boosting algorithm, but one that simply represents a better optimization of older,
more traditional approaches.
Center for Security and Emerging Technology6to detect potentially malicious traffic. If well-labeled data on previous attacks exists,
supervised methods can be used to detect future attacks by matching malicious traf-
fic to a known profile, while unsupervised methods can be used to identify attacks
merely because they are anomalous and out of place.†
To show the history of how these traditional machine learning approaches have
been used in cybersecurity, we turn first to the example of spam detection.
\*See the note on page 10 below for a definition of polymorphic and metamorphic viruses.
†This distinction is not as clear-cut as it seems; in some cases, unsupervised learning can be used to
match attacks to known profiles and supervised learning can be used to detect anomalies. For the
most part, however, the way we have made the distinction corresponds to the way that supervised
and unsupervised algorithms have been studied for detection purposes.FIGURE 1
A Timeline of Machine Learning Developments
for Three Major Cybersecurity Tasks\*
2010s 2000s 1990sSPAM
DETE CTIONFIGURE 1
A Timeline of Machine Learning Developments for Three Major Cybersecurity Tasks\*INTRUSIO N
DETE CTIONMALW ARE
DETE CTION1978: First spam email
1980: First intrusion
detection systems
1986: Anomaly
detection systems
combine exper t rules
and statistical analysis
Early 1980s: First
viruses found "in the
wild"
Late 1980s: First
antivirus companies
founded Pre-1990s
Spam continues to worsen
due to growth in email
1996: First spam blockers
Early 1990s: Neur al
networks for anomaly
detection first pr oposed
1999: DARP A creates
datasets to study intrusion
detection systems
Early 1990s: First
polymorphic viruses
1996: IBM begins studying
machine learning for
malware detection 2002: Machine learning
methods first pr oposed
for spam detection
2003: First attempts to
regulate spam in the
United States
Machine learning fur ther
studied as a possible t ool
for misuse-based and
anomaly-based intrusion
detection
Early 2000s: First
metamorphic viruses
Wide number of tr aditional
machine learning methods
studied to detect malware Machine learning spam
detection widely
embedded in email
services
Emergence of deep
learning-based classifiers
Late 2010s: Emergence
of large-scale,
cloud-based intrusion
detection systems
Deep learning studied for
intrusion detection
Rise of " next-gen "
antivirus detection
Emergence of
ML-focused antivirus
companies
Center for Security and Emerging Technology7 Center for Security and Emerging TechnologySPAM DETECTION
Machine learning has been a major part of spam detection since the very early
2000s, and many of these early ML methods are still used today. Before the in-
troduction of ML techniques, spam detection relied on blocklists that screened out
mail from (known) malicious IP addresses or on keyword detection that blocked
emails containing hand-curated lists of spammy terms like “free” or “sexy.” Unfor-
tunately, because these methods were applied indiscriminately, they could often
block legitimate emails. To address this problem, computer scientists began to
propose machine learning–based solutions around the turn of the century.15
These early methods were relatively straightforward: First collect a large body of
emails, label them as either spam or legitimate, and split them into individual words.
For each word, calculate the probability that an email was spam if it contained that
word. When a new email arrives, the probabilities associated with each of its words
could be used to calculate the risk that the email was spam, and emails with risk
scores above a preset threshold could be automatically blocked.
The core elements of spam detection have not changed much since the early
2000s, though researchers have made improvements. Better spam classifiers can
be built by extracting more technical details from mail headers, such as IP addresses
and server information, or by treating words that appear in a subject line differently
from words that appear in the body of an email.16 Better algorithms can be used that
recognize phrases or synonyms rather than treating all words as independent of one
another.17 Some companies have developed extremely complex spam detectors that
can, among other things, track a user’s past email interactions to flag anomalous
contacts or use deep learning models to determine whether or not branded emails
are sent from authentic companies.18
Nonetheless, even the most advanced spam classifiers used by companies like
Google have mostly developed out of a slow process of elaboration and evolution
from these earlier methods. Of course, even moderate improvements in accuracy
can matter a great deal to massive companies responsible for protecting billions of
emails. But it would be an error to portray recent innovations in ML spam detection
systems as a fundamental transformation of past practice: in reality, machine learn-
ing has been central to the task for nearly two decades.
INTRUSION DETECTION
Intrusion detection systems attempt to discover the presence of unauthorized
activities on computer networks, typically by focusing on behavior profiles and
searching for signs of malicious activity. Intrusion detection systems are typically
classified as either misuse-based or anomaly-based. In misuse-based detection,
Center for Security and Emerging Technology8attacks are identified based on their resemblance to previously seen attacks,
whereas in anomaly-based detection, a baseline of “normal” behavior is con-
structed and anything that does not match that baseline is flagged as a potential
attack. Both methods can make use of different ML methods.
The simplest forms of misuse-based detection rely on known indicators of com-
promise to detect previously encountered threats. For instance, if an organization
has seen malware that attempts to contact a specific website, cyber defenders could
write a simple detection system which provides an alert any time a machine on the
network attempts to contact that website. Misuse-based detection—especially when
based on simple methods like these—typically has high processing speeds and
low false positive rates, which allows it to quickly and accurately identify malicious
events. However, this form of detection can only monitor for known threats, provid-
ing little meaningful protection against novel attacks.
Machine learning can be used to automate some forms of misuse-based detec-
tion by allowing a system to “learn” what different types of attacks look like. If many
(labeled) examples of past attacks are available, a supervised learning classifier
can be trained to identify the tell-tale signs of different types of attacks, without the
need for humans to generate specific lists of rules that would trigger an alert.\* Since
at least 1999, researchers have attempted to generate network traffic profiles of
different types of attacks so that ML classifiers can learn how to identify previously
seen attacks.19 This research was initially pushed by DARPA, and the results suggest-
ed that machine learning could prove competent at misuse-based detection.20
Although it is tempting to think that newer ML methods—like the rise of deep
learning—have enabled dramatically more powerful detection tools, one review of
several dozen experimental results in 201 8 suggested that deep learning is not reli-
ably more accurate at misuse-based detection than decades-old ML approaches.21
Because no one type of model is consistently best for misuse-based detection, re -
searchers often find that the most successful machine learning systems are ensemble
models, or models that classify new inputs by utilizing multiple classifiers that “vote”
on a classification.22 The relatively common use of this method avoids overreliance
on any specific model—which may have its own blind spots—but it also indicates
that no single architecture is clearly superior to the rest.
\*Some researchers tend to define misuse-based detection more restrictively as being strictly based
on curated lists of indicators of compromise rather than probabilistic models. Under this definition,
ML cannot be used to perform misuse-based detection, since ML is by its very nature probabilistic.
However, many ML classifiers resemble misuse-based detection systems in that they use labeled
examples of previous attacks to detect future attacks, struggle to generalize to zero-day attacks,
and do not seek to identify a baseline model of “normal” behavior. We therefore think it makes the
most sense to categorize ML classifiers with these characteristics under the label of “misuse-based
detection.”
Center for Security and Emerging Technology9In contrast to misuse-based detection, anomaly-based detection flags suspi-
cious behavior without making specific comparisons to past attacks. This type of
detection system is more likely to use unsupervised learning methods to cluster “nor-
mal” traffic within a network and alert as suspicious any activity which deviates from
that pattern. In theory, anomaly-based detection can identify novel attacks—one of
the most difficult aspects of cybersecurity. To enable this type of capability, research
in this area has focused on finding ways to appropriately baseline “normal” traffic
for a given network, since even normal traffic can be highly variable, and a poorly
tuned intrusion detection system will generate many false positives that are expen-
sive to investigate.23
Unfortunately, although anomaly-based detection can be highly effective
when tracking an individual machine or user, it often struggles to effectively identify
suspicious behavior across a network. In addition, a long-standing complaint re -
garding anomaly detection systems has been their tendency to generate many false
positives, which speaks to the difficulty of defining “normal” traffic strictly enough
to detect any anomalies but loosely enough that no legitimate behavior is flagged
as anomalous.24 Moreover, changes in an organization’s standard procedures can
dramatically undermine the usefulness of anomaly detection, at least until new base -
lines are learned—a lesson that many businesses discovered last year when millions
of employees suddenly began working from home in response to COVID-19.25
Because of the difficulties associated with anomaly detection, many organizations
use it only as a complement to more standard misuse-based detection systems.
This discussion illustrates that machine learning already has a long and multi-
faceted history in the field of intrusion detection. Different ML methods have been
adapted to multiple types of intrusion detection, in a research process dating back
over two decades. Moreover, empirical studies and the continued importance of
ensemble models speak to the fact that newer innovations have not fully displaced
these older models. As with spam detection, it would be a mistake to think that
the developments of the past decade of ML research, specifically the rise of deep
learning, have entirely transformed intrusion detection—though in Section 3, we
will return to this topic to discuss some of the ways in which intrusion detection has
meaningfully been changed by newer ML innovations.
MALWARE DETECTION
While intrusion detection systems monitor a system or network’s behavior to identi-
fy signs that a network is under attack, malware detection systems examine specif-
ic files to determine if they are malicious. The simplest forms of malware detection
found in early antivirus products would monitor machines for specific indicators of
compromise, such as exact filenames or signatures (which are specific sequences
Center for Security and Emerging Technology10of bytes or strings taken from the contents of a file). By maintaining long lists of
malware signatures and conducting regular scans, these antivirus products tried to
determine if any files on a machine were associated with these known definitions.
Unfortunately, these detection methods can be easily evaded by polymorphic
or metamorphic viruses—types of malware that change their own code each time
they propagate—thereby ensuring that different versions will have different sig-
natures.\* By some estimates, in 201 8 up to 94 percent of malicious executables
exhibited polymorphic traits.26 While traditional detection techniques can still be
adapted for the detection of polymorphic or metamorphic malware—for instance,
by looking at the sequence of actions the malware will take when executed rather
than by matching based on raw code—these traditional methods become increas-
ingly complex and computationally intensive as attackers improve.27
Machine learning, however, excels at identifying shared features between
samples that can’t be classified using simple rules. As early as 1996, researchers
at IBM began to explore the use of neural networks to classify boot sector viruses,
a specific type of virus that targets a machine’s instructions for booting up.28 Addi-
tional research throughout the early 2000s explored the use of statistical models
and standard ML classifiers for detecting malware.29 Recent years have seen an
explosion of interest in malware detection methods that utilize newer, deep learn-
ing–based approaches, methods which come with many advantages, such as the
ability to extract relevant features from raw data with less human guidance.30 Yet, as
with intrusion detection, some experimental results indicate that—at least on some
datasets—decades-old ML classifiers remain on par with more advanced deep
learning methods when trained on the same data.31
Notably, many techniques that do not use machine learning have remained
effective at detecting and analyzing malware, despite the rise of polymorphic and
metamorphic viruses. For instance, cyber defenders may execute unrecognized files
in sandboxes—isolated virtual environments where a file can be allowed to execute
without any ability to interact with real systems and data.32 The sandbox collects
information about the file, such as what kinds of functions it tries to execute, to deter -
mine if it is malicious. This method allows an antivirus product to detect polymorphic
or metamorphic code without relying on machine learning and underscores the fact
that machine learning is by no means the only way for cyber defenders to respond
\*Polymorphic code can refer to any type of code which appears in multiple forms, for instance,
because attackers have created multiple variants or because the code can encrypt itself using a
different key each time it propagates. By contrast, metamorphic code actually rewrites its underlying
code while preserving the same functionality.
Center for Security and Emerging Technology11to the intelligent evolution of cyber attacks. Even sandboxes, however, can be aug-
mented with machine learning to identify files that resemble past malware but that
do not necessarily try to execute the exact same behavior.33
Throughout this section, we have emphasized two major points. First, there is a
multi-decade-long history of researchers applying traditional ML techniques to ma-
jor cybersecurity tasks, though with a very heavy emphasis on detection tasks. And
second, though more powerful methods exist today, they typically represent natural
evolutions from more traditional approaches. These facts are important to keep in
mind when determining just how “transformative” recent ML advances will be for
cybersecurity.
Center for Security and Emerging Technology12
Center for Security and Emerging Technology13lthough many decades-old ML approaches in cybersecurity
remain competitive with more recent algorithms, the recent
spike in machine learning interest is driven by some genuinely
impressive breakthroughs. In recent years, AI innovations have led to
self-driving cars, accurate language translation, and better-than-human
video game playing. Although the possibility of transferring these suc-
cesses into the cyber domain is uncertain, there is strong reason to think
that—could this transfer be achieved—machine learning might become a
major aid for cyber defenders.
This section explores how newer ML architectures might be applied to
cybersecurity. It is organized around the four-stage model of cybersecu-
rity introduced earlier and discusses potential ML applications for each
stage in turn. As it proceeds, it pays particular attention to four types of ML
methods that have been responsible for many of the AI breakthroughs of
the past five years: 1) deep learning; 2) reinforcement learning; 3) gen-
erative adversarial networks (GANs); and 4) massive natural language
models.\* This section assumes a basic familiarity with these concepts;
readers without a background in ML should refer to the appendix for a
brief description of each type of model. Of these breakthroughs, the devel-
opment of deep learning systems has in many ways been the most funda-
mental. It is the combination of deep learning architectures with other types
of approaches—such as GANs, reinforcement learning systems, or natural Cybersecurity and
the Cutting Edge of
AI Research3
A
\* Cutting-edge language models are impressive in their own right, but they have also
drawn attention to the applications of simpler forms of natural language processing.
Many of the natural language tools we discuss in this section are not particularly
complicated, but they do represent a growing interest in the question of how linguistic
data might be better leveraged for cyber defense.
Center for Security and Emerging Technology14language models—that has enabled most of the progress of the past half-decade,
as visualized in Figure 2.
FIGURE 2
The Relationship Between Cutting-Edge AI ArchitecturesFIGURE 2
The Relationship Between Cutting-Edge AI Ar chitectures
Deep learning is a
gener al Al architecture
modelled off of neur al
networks. It can be
adapted for many tasks
...
... from reinforcement
learning systems where
Al agents learn how to
inter act with their
envir onments ...
... to GANs, where a
gener ator learns how to
produce outputs that can
fool a discriminat or ...
... to massive natur al
language models that
can perform a wide
range of language-
related tasks.DECISION INPUTS
ACTION SELEC TION
FEEDBACKENVIRONMEN T AGENT
DISCRIMINA TOR
DECISION
FEEDBACKFAKE DA TAREAL-WORL D
DATA
GENERA TOR
TEXT
CORPUS
WORD
EMBEDDINGSHIDDEN
LAYERSTEXT COMPLETIO N
TRANSLA TION
QUESTION ANSWERIN G
SENTIMENT ANAL YSIS

Center for Security and Emerging Technology15At the beginning of our discussion of each stage, we provide a graphic listing
a few potential ML tools that could be leveraged by defenders at that stage. These
graphics also list the type of ML architecture that implementations of each tool might
rely on and a rough assessment of how significantly ML can transform the task itself.
All of these assessments are meant to be very general and draw from the discussions
that follow.
PREVENTION
FIGURE 3
AI Applications for Prevention\*
\*NLP refers to natural language processing. This figure lists only the most common and most
specific underlying technology for each application; a deep learning-based GAN, for instance,
would be simply listed as “GAN.”FIGURE 3
AI Applications for Prevention
PREVENTION DETE CTIONRESPONSE AND
RECOVER YACTIVE
DEFENSE
FuzzingCYBERSECURITY TASK SIGNIFICANCE
 OF TASK UNDERL YING
TECHNOL OGY OF NEW
AI APPLICA TIONSTRANSFORMA TIVE
POTENTIAL
OF NEW AI
Pentesting
Bug Triage and
Classification
Vulnerability Severity
Assessment Deep Learning
Reinforcement
Learning
NLP, Traditional
ML Methods
NLP, Traditiona l
ML Methods High
High
Medium
MediumMedium-High
Medium-High
Medium
Medium-Low
Center for Security and Emerging Technology16The first stage of our cybersecurity model is prevention—the work that defenders
do to find and patch vulnerabilities in order to eliminate potential weaknesses. There
has long been interest in building tools that can autonomously find and patch new
vulnerabilities, but machine learning has only recently emerged as a plausible way
of doing so. As recently as the 2016 DARPA-sponsored Cyber Grand Challenge,
the most promising routes for automated vulnerability discovery still relied on care -
fully coded systems without the use of ML methods. Though several teams at that
competition attempted to use machine learning to identify software vulnerabilities,
Mayhem—the winning program—ultimately made no use of ML in its vulnerability
discovery modules.
Instead, Mayhem used two more common vulnerability discovery methods to
identify potential weaknesses. First, a symbolic execution engine analyzed pro-
grams by building up representations of their execution logic, thereby learning how
potentially crash-inducing behavior could be triggered. Second, it made use of
fuzzers, which feed many modified and semi-random inputs to a program to deter-
mine if they cause any unexpected states that can be exploited.34 In Mayhem, both
of these elements were coded using traditional, rules-based systems, which were
combined with modules that could autonomously write and deploy patches for
discovered vulnerabilities.
The Cyber Grand Challenge resulted in several significant innovations in the
field of automated vulnerability discovery. That no team at the time made heavy use
of machine learning demonstrates that machine learning is far from the only way to
build autonomous cyber tools.\* At the same time, ML research has made significant
strides since 2016, and experts disagree about whether participants in a new Cyber
Grand Challenge would find more use for machine learning today.35
Consider fuzzers. In recent years, researchers studying fuzzers have increas-
ingly explored deep learning as a means to more efficiently learn from successful
inputs and find a larger number of vulnerabilities over time.36 Deep learning–based
fuzzers are often more efficient than older models; as one example, the deep learn-
ing–based program NeuFuzz was able to find three times as many crash-causing
inputs as a modified version of a leading open source fuzzer across a variety of file
types.37 Outside of academia, Microsoft has also studied the use of deep learning
to augment its own fuzzers.38
Fuzzers look for vulnerabilities in code, but cyber defenders can also use pene -
tration testing (or pentesting for short) to look for publicly known vulnerabilities and
\*Mayhem did use machine learning in other areas. For instance, the team found that machine
learning could be used to generate realistic-seeming attack traffic, which could mislead other teams
and cause them to waste resources. While intriguing, this application has less relevance outside of
the 2016 competition, with its unique scoring system and capture-the-flag setup.
Center for Security and Emerging Technology17insecure configurations in networks. In a pentest, experienced hackers systemati-
cally probe a network for vulnerabilities to identify potential weaknesses. For large
organizations, pentests can be expensive, potentially costing tens of thousands of
dollars and consuming weeks of employee time. Automated tools such as the open-
source program Metasploit can partially offset these costs, but these tools simply
run through a list of pre-selected, known exploits to determine if any machines on a
network are vulnerable to them; they are not capable of strategizing how they use
resources.39
Recently, researchers have studied how reinforcement learning can allow cyber
defenders to build AI agents that conduct pentests more strategically.40 Some re -
searchers have demonstrated that reinforcement learning–based agents can devise
plausible strategies for a variety of capture-the-flag style simulations, and can do
so reasonably quickly if they first watch a few examples of human-led pentesting.41
These types of reinforcement learning–based approaches allow automated tools
to search a network for vulnerabilities much faster than rules-based pentesting tools
like Metasploit.42
These successes require caveats, especially due to the high computational cost
of reinforcement learning in complex environments. Studies exploring the use of re -
inforcement learning for pentesting typically rely on small environments—often simu-
lated networks of around ten machines—with a limited number of exploits provided
to the program. As either the complexity of the environment or the number of actions
available to the program increases, reinforcement learning can quickly become
computationally prohibitive.43 This problem is difficult but not fully intractable: in oth-
er contexts, researchers have developed models that can efficiently narrow down
the number of options that a program must consider.44 If researchers could develop
computationally feasible methods of simulating complex networks and efficiently
choosing among many options, reinforcement learning programs could become
important aids for pentesters, just as Metasploit was adopted as a major tool in the
pentester’s toolkit in previous decades. But it is also worth emphasizing that of all the
technologies we discuss in this paper, this is the one that is perhaps most obviously
of interest to attackers as well—indeed, attackers have often been observed using
pentesting tools developed for legitimate use to instead compromise their targets’
networks.45
Beyond finding vulnerabilities with autonomous fuzzers and pentesters, machine
learning may soon be able to provide tools that can help defenders allocate their
time and attention to the most pressing vulnerabilities. Although there are many other
ways that machine learning could be adapted for preventative purposes, we men-
tion two other applications here: analysis of new bug reports and severity assess-
ment for identified vulnerabilities.
Center for Security and Emerging Technology18Since at least 2005, researchers have explored ways in which machine learn-
ing can be used to automatically analyze bug reports. ML systems might, for exam-
ple, direct bugs to the software engineer most able to address them, label malware
characteristics based on human-generated reports, or predict the likelihood that a
new bug could present a security vulnerability.46 Though much of the research in this
area has yielded results that are insufficiently accurate for operational use, Micro-
soft recently demonstrated that machine learning can successfully use bug report
titles to classify bugs as security-relevant or non-security-relevant. The Microsoft
model was trained on more than a million actual Microsoft bug reports, suggesting
that some automated forms of bug report analysis may be feasible for software
companies with access to large amounts of training data.47
Once vulnerabilities have been identified, defenders have limited time and
resources to patch them. One prominent—though somewhat controversial—met-
ric that helps defenders determine the severity of a vulnerability is the Common
Vulnerability Scoring System (CVSS), which relies upon expert analysis to assign
severity scores to new vulnerabilities. Some researchers think that machine learning
and text mining could be used to automate the process of assigning CVSS scores
by intelligently interpreting vulnerability descriptions.48 Other researchers have
used data about attacks observed in the wild to build machine learning systems
that predict the likelihood of some vulnerabilities actually being exploited.49 There
is some evidence that when these machine learning–based risk assessments are
used in conjunction with the CVSS, organizations can achieve similar levels of risk
remediation for significantly less effort by prioritizing their remediation efforts on
the vulnerabilities that are most likely to be exploited.50 Taken together, the use of
machine learning for improved fuzzing, pentesting, bug report analysis, and se -
verity assessment could allow organizations to improve their ability to identify and
prioritize vulnerabilities.
Center for Security and Emerging Technology19DETECTION
FIGURE 4
AI Applications for Detection
Detection remains, at least among many popular-facing venues, the key place
where deep learning and newer ML methods are thought to be potentially transfor-
mative forces.51 Unfortunately, as of yet, deep learning has not provided the revo-
lutionary breakthroughs that many have hoped for. While sufficiently large models
do tend to perform incrementally better than simpler models—especially at large
enough scales—these improvements are at times outweighed by the growing number
of threats facing most organizations. The bottom line: despite the fundamental role that
deep learning has played in the ML advances of the last half-decade, many cyberse -
curity companies continue to make significant use of simpler models today.
One problem with ML-based detection, sometimes overlooked in popular
coverage, is that ML systems are vulnerable to several classes of attack that do not
apply to other types of detection systems. The process by which many ML sys-
tems reach decisions can often be poorly understood and highly sensitive to small
changes that a human analyst would view as trivial, which often makes it possible FIGURE 4
AI Applications for Detection
PREVENTIONRESPONSE AND
RECOVER YACTIVE
DEFENSE
Accur ate DetectionCYBERSECURITY TASK SIGNIFICANCE
 OF TASK UNDERL YING
TECHNOL OGY OF NEW
AI APPLICA TIONSTRANSFORMA TIVE
POTENTIAL OF
NEW AI
Alert Prioritization
Adversarial Hardening
of Detection Systems Deep Learning
Deep Learning
GANsHigh
Medium-High
Medium-High Low
Medium
Medium-Low DETE CTION
Center for Security and Emerging Technology20for attackers to find “adversarial examples”—slightly altered inputs that dramatically
change a model’s response despite being undetectable to a human.52 The use of ML
models also opens up new avenues of attack: the model itself must be kept secure,
but defenders must also make sure that their data is not poisoned and that the
(typically open source) algorithms and statistical packages they use have not been
tampered with.53
In addition, while machine learning is sometimes presented as an objective
process of “learning patterns from data,” in reality the design of ML systems is often
the result of many judgment calls. In one somewhat infamous example, the security
firm Cylance deployed an ML-based malware detection product, only for a group
of white hat hackers to discover that if a short 5MB string were attached to any
malicious file, the file would bypass the detection system nearly 90 percent of the
time.54 It seems that Cylance had built an ML malware detection system that worked
relatively well, only to discover that it also blocked a number of legitimate games
from being downloaded. In response, the firm added on a second ML system that
calculated how similar a file was to any file on a whitelist of approved games,
and which let high-scoring files through the system, even if the first ML model had
identified them as potentially malicious. It was that second model that the white hat
hackers were able to target: by adding on a string of code from the popular game
Rocket League, they were able to create malware that Cylance’s detection system
failed to flag.
This anecdote is not necessarily a story about ML’s flaws so much as it is a story
about the difficulties of implementing ML systems in real-world environments. Cy -
lance’s core malware detection algorithm seems not to have been vulnerable to
this “universal bypass”; rather, it was the designers’ decision to add on a second
component in order to avoid blocking popular video games that introduced the vul-
nerability. Ultimately, the use of machine learning can bring several types of vulner-
abilities, whether because those weaknesses are inherent to the ML approach itself
(as is the case with data poisoning threats and adversarial examples), or because
wrangling the ML model into a deployable product leads developers to inadver-
tently add in further vulnerabilities.
None of this means that newer ML innovations are unimportant at the detection
stage. For one, while older models can typically only provide rudimentary assess-
ments of the severity of different types of alerts, deep learning can allow more
sophisticated forms of analysis, which can help defenders prioritize more serious
threats. This would provide an enormous benefit to cyber defenders, because many
detection systems today generate so many false alerts that it is nearly impossible
for human analysts to investigate them all. Some organizations are already making
Center for Security and Emerging Technology21use of tiered systems, in which deep learning models analyze alerts from first-level
detection systems to determine which potential threats require the highest priority.55
These types of systems are most useful for large organizations, since they require not
only a great deal of data about “normal” traffic, but also many examples of unique
attacks.
In addition, while adversarial examples are specifically crafted to fool ML mod-
els, the rise of other types of ML systems—most notably GANs—that can generate
synthetic and realistic-looking data poses a threat to both ML and rules-based forms
of attack detection. Researchers have already demonstrated that GANs can devel-
op attack strategies that slip by existing intrusion detection systems, whether or not
they are machine learning–based.56 But GANs can also help to harden ML systems
against these types of more elaborate threats: when intrusion detection systems
are trained using a GAN to anticipate potential adversarial attacks, their accuracy
against adversarial attacks is greatly improved.57 GANs can also augment exist-
ing datasets by generating new data that can be reincorporated into a classifier’s
training data. GAN-augmented datasets of spam and malware have been used by
some research teams to create classifiers that perform better than those trained only
on real-world data.58
Even though we have been somewhat pessimistic regarding the potential of
deep learning to transform detection capabilities, the rise of adversarial attacks and
the ever-increasing scale of threats means that defenders cannot afford to ignore
advances in machine learning. Because GANs allow attackers to subvert both ML-
and rules-based detection systems, doing nothing is not an adequate response, and
the best defense will likely rely at least in part upon the use of ML methods. And yet,
even here it is important to emphasize that some researchers do not believe that the
ML field has made meaningful progress in making ML systems robust to adversarial
examples.59 While adversarial training remains critical for defensive systems, it may
not be enough to effectively counter the rising threat of adversarial attacks—another
reason to be skeptical that machine learning can provide a silver bullet for cyber
defenders.
Center for Security and Emerging Technology22RESPONSE AND RECOVERY
While there is a great deal of research on ML-driven detection systems, more
ambitious proposals posit AI systems that might one day move through networks
autonomously, patching vulnerabilities and fighting dynamically against attackers.
After all, in recent years researchers have developed ML systems that can master
competitive games as diverse as Go or StarCraft.60 But as complex as these games
are, they are comparatively more structured and easier to model than the nebulous
world of cybersecurity. Furthermore, in most of the other stages of cybersecurity, ML
progress has come from automating discrete and self-contained (yet increasingly
complex) tasks, rather than from attempting to build ML systems that can operate
fully autonomously. Response and recovery, by contrast, is a dynamic and contin-
uous process that is not as easily broken into discrete components, which makes it
much more difficult to build ML tools that can adequately automate human deci -
sion-making.
In recent years, some authors have begun to identify more targeted roles that
AI/ML systems could play in the response and recovery process. A 2020 report
from the National Science & Technology Council, for instance, identifies at least two FIGURE 5
AI Applications for Response and RecoveryFIGURE 5
AI Applications for Response and Reco very
PREVENTIONRESPONSE AND
RECOVER YACTIVE
DEFENSE
Adversary EngagementCYBERSECURITY TASK SIGNIFICANCE
 OF TASK UNDERL YING
TECHNOL OGY OF NEW
AI APPLICA TIONSTRANSFORMA TIVE
POTENTIAL OF
NEW AI
Moving Target Defense Reinforcement LearningHigh
High Medium-Low
Medium-High DETE CTION
Reinforcement Learning
Center for Security and Emerging Technology23concrete ways in which AI could aid the response and recovery process: by accu-
rately categorizing ongoing attacks and selecting an appropriate initial response
strategy, or by automating the decision to isolate machines from a network or im-
pose user restrictions to contain infections.61 The first type of tool could allow cyber
defenders to automate their initial responses to a wide variety of common types
of attacks, while the second type would be generally useful in containing security
breaches from spreading to other parts of a network.\*
Despite being identified as a potential area of opportunity, we are not aware of
any significant research demonstrating the viability of a ML system that could auto-
mate a wide range of responses to multiple types of attacks.† In part, this absence is
likely due to the fact that such a goal remains too broad to be effectively automated
by current ML methods. At the same time, there have been some attempts to further
break this goal down, with the intention of building ML systems that can intelligent-
ly respond to specific types of attacks. As perhaps the most high-profile example,
DARPA is currently sponsoring a project, ongoing since 2017 , to create autonomous
systems that can respond to botnets. If successful, this program would result in au-
tonomous systems that can identify botnet-infected machines, select publicly-known
vulnerabilities to deploy against them in order to remove malware, and move later-
ally to neutralize adjacent compromised machines.62
There has been more progress in building ML models that can learn to isolate
potentially compromised machines from a network in order to contain a security
breach. This type of application is an example of moving target defense, a de -
fensive strategy in which defenders try to impede attackers by dynamically re -
structuring parts of their IT infrastructure in response to an attack. Reinforcement
learning in particular has been useful in exploring this topic. Several researchers
have attempted to model network systems under attack in order to test the ability of
reinforcement learning agents to respond. In one example, researchers allowed a
defending agent with imperfect knowledge of an attacker’s actions to reimage—or
restore to factory settings—machines which it suspected may have become infected.
By imposing a cost upon the defender for each reimage it made, the researchers
demonstrated that a reinforcement learning–based defender could learn the appro-
\*In Figure 5, we refer to the first application as “adversary engagement,” which would be the
ultimate goal of an ML system that can understand ongoing attacks and respond accordingly. The
second goal is a simple example of moving target defense.
† Here it is worth again reiterating that the focus of this paper is specifically on machine learning and
not on other types of technological advances that might be called “AI.” There have been more efforts
to address this sort of problem using non-ML methods, but we do not comment on the success of
these efforts here.
Center for Security and Emerging Technology24priate cost-benefit tradeoff and make useful decisions regarding when to reimage.63
Similar approaches have allowed reinforcement learning agents to learn when to
isolate potentially infected nodes within a constrained network or to develop game
theoretic strategies for adaptively responding to adversaries.64
These results are promising, because they suggest that machine learning could
be useful for automating tactics like moving target defense or for providing respons-
es to some types of threats, such as botnets. But it remains far from certain that such
tools will become broadly useful. The examples mentioned above were the result
of extremely simplified simulations of real networks, and as with pentesting, it is not
clear that the complexity of the simulation can be reasonably scaled up without
becoming too computationally intensive to allow for real-time decision-making.
Moreover, machine learning continues to be most successful in contexts with rela-
tively clearly-defined problems and outcomes. Although it may become increasing-
ly useful as a way to implement specific defensive strategies in response to various
threats, we do not foresee it becoming capable in the short- to medium-term of fully
automating the work that human analysts do when investigating the sources of a
compromise and formulating strategic responses.
Center for Security and Emerging Technology25ACTIVE DEFENSE
FIGURE 6
AI Applications for Active Defense
Organizations with significant cybersecurity needs must often take proactive
steps to shape their cybersecurity strategies in response to new threats. This report
groups these actions under the broad framework of “active defense.” Though this
term has a specific national security definition, it is here used analogously to the
way it has been used by researchers at the SANS Institute: as a spectrum of activity
ranging from annoyance to attribution to outright counterattacks.65 This section sets
aside the issue of outright counter-attack and instead focuses on three less legally
fraught tactics: deception, threat intelligence, and attribution. The wide range of
potential AI applications across these three tactics suggests that active defense may
be one of the stages of our cybersecurity model with comparatively much to gain
from recent ML breakthroughs. FIGURE 6
AI Applications for Active Defense
PREVENTION DETE CTIONRESPONSE AND
RECOVER YACTIVE
DEFENSE
Deceptive Document
Gener ationCYBERSECURITY TASK SIGNIFICANCE
 OF TASK UNDERL YING
TECHNOL OGY OF NEW
AI APPLICA TIONSTRANSFORMA TIVE
POTENTIAL OF
NEW AI
Dynamic Honeypotting
Automated Phishing
Response
Dark Web Threat
IntelligenceNLP, GANs
Reinforcement
Learning
NLP, Reinforcement
Learning
NLP Medium-Low
Medium
Low
MediumHigh
Medium-High
Medium-High
Medium
Medium
Medium-LowMedium-Low
LowAttack Clustering
for Attribution
Code
De-Anonymization NLP, Traditional ML
Methods
NLP
Center for Security and Emerging Technology26Deception
One of the most basic active defense measures available to cyber defenders is
deception: intentionally faking something to mislead and slow down attackers.
Though this strategy seems simplistic, it can have major operational benefits. In
the lead-up to the 2017 French presidential election, for instance, the Macron
campaign—aware that it was being targeted by Russian hackers—chose to forge
internal emails full of misleading or outlandish content. Russian hackers could not
easily distinguish real information from forgeries, and although they eventually
chose to leak everything, this strategy effectively allowed the Macron campaign
to minimize the public perception of the issue.66 Deployed well, deceptive behav -
ior can also cause attackers to second-guess themselves or otherwise manipulate
their attention towards unproductive activities.67
Generating realistic-looking documents or activity profiles is an area where
machine learning excels. The rise of massive natural language models like GPT-3
will likely make the process of generating deceptive textual documents—like the
Macron campaign’s fake emails—relatively easy to automate. While this technology
has an enormous potential for misuse, defenders could plausibly use it for deceptive
purposes, generating waves of deceptive internal documents to mislead attackers.
The use of other types of ML models—like GANs—could be used for non-textual
deception. For instance, researchers have shown that it is possible to use a GAN
to generate realistic network data that might be used to confuse attackers.68 (An
analogous type of forgery was used by the winning team of the 2016 Cyber Grand
Challenge, which used machine learning to create traffic that looked like an attack
and fooled opposing teams into wasting time trying to stop it.69)
Beyond forgeries, the honeypot—a security mechanism designed to lure cyber
attackers to attractive-seeming but fake targets—is a long-standing deceptive tool
for network defenders. Honeypots can offer “bait” in the form of fake files and data,
run scripted interactions to thwart adversaries, or mislead attackers into thinking that
they have been more successful than they have been. However, honeypots often
require manual configuration, deployment, and maintenance to look like realistic
production networks long enough to keep the attackers engaged, which can be
costly and makes them difficult for smaller organizations to maintain.
Since at least the early 2000s, researchers have explored how machine
learning could be used to create more realistic and dynamic honeypots, includ-
ing through the use of relatively simple clustering methods.70 In more recent years,
researchers have experimented with reinforcement learning–based honeypots that
can learn to engage attackers for as long as possible, while also ideally tricking
them into downloading custom software onto their own machines.71 It would be
premature to say that the use of reinforcement learning is a game-changer in this
Center for Security and Emerging Technology27area, but it may at least make these types of honeypots more accessible to a larger
number of organizations by automating much of the complexity of maintaining a
dynamic honeypot.
Threat Intelligence
Gathering threat intelligence about potential adversaries can theoretically al-
low defenders to anticipate attacks and build stronger defenses, though for most
organizations, the labor-intensive nature of collecting, processing, and analyzing
threat intelligence makes it cost-prohibitive. But because finding patterns in data
is a core strength of machine learning, some researchers have explored how ML
and text mining can be used to improve threat intelligence analysis. For instance,
ML methods can be used to cluster dark web users, or text mining methods could
be leveraged to automatically collect, classify, and analyze posts on dark web fo-
rums and marketplaces, allowing researchers to identify zero-day exploits before
they are deployed.72 It is not difficult to imagine a fully automated ML system that
could be given a company’s name or a list of its products and told to search for
mentions of potential vulnerabilities on the dark web, analyze them, and generate
a report describing anticipated vectors of attack.
Other tools introduced as deceptive tactics could be repurposed to collect threat
intelligence about potential adversaries. Data collected from honeypot systems, for
instance, can be analyzed and collated to determine if an organization is facing a
persistent threat actor, which can then inform security strategies.73 Even something
as simple as a phishing email could become an opportunity to collect threat intelli-
gence about an adversary. Some researchers have proposed systems that can pro-
duce misleading responses to phishing emails to elicit information from attackers, for
instance by appearing willing to give money to a scam but asking for the attackers’
routing numbers, which can then be traced and identified.74 In fact, this is a major
line of research within the Active Social Engineering Defense initiative at DARPA,
which is currently attempting to build systems that can detect social engineering
attacks and respond autonomously to trick adversaries into revealing identifying
information.75 These tactics cross the line between threat intelligence collection and
outright attribution, the third active defense tactic discussed in this section.
Attribution
When facing an attack from an advanced persistent threat, federal agencies and
cybersecurity companies will typically make an effort to attribute the attack to a
specific adversary. Doing so enables federal actors to determine what type of
response is warranted, and it helps cybersecurity companies determine the likely
motivations of hostile actors in order to best defend their customers. Other com-
panies are less likely to engage in attribution work. While there is some strategic
Center for Security and Emerging Technology28value to being able to share precise information about adversaries with other
organizations, the fact that most corporations cannot legally respond in kind to
an adversary severely reduces the value of a successful attribution, particularly
considering how difficult the process can be.
When trying to attribute multiple attacks, the simplest approaches rely on
manual analysis of attack indicators obtained through network logs, firewalls, or
honeypots. However, because most persistent cyber attackers can avoid leaving
obvious patterns, attribution also relies on human analysis and an understanding
of potential adversaries and their overarching goals; attribution therefore relies not
only on technical but also strategic indicators and, potentially, knowledge about
relevant geopolitical situations. This is a level of analysis that machine learning is not
well-equipped to carry out, so it remains unlikely that machine learning will be able
to successfully automate the full process of attribution.
Nonetheless, machine learning may still be able to assist in the attribution
process. For example, if research analysts first extract high-level descriptions of an
attack—including the tactics used, the country of attack, and so forth—ML methods
may be able to cluster this information to identify similar attacks.76 Natural language
processing may help attribution systems automatically extract attribution-relevant
information from blogs, research papers, and incident response reports, reducing
the amount of work that humans need to do to make a successful attribution.77
Applying ML methods to an attacker's source code, when available, may offer
a different path to attribution. ML methods have already shown the ability to distin-
guish and de-anonymize authors based upon their distinctive writing styles.78 In re -
cent years, some researchers have demonstrated that human generated code works
in a similar way: different programmers make unique choices as they code, which
allows machine learning to create digital fingerprints that can be used to identify
coders.79 By comparing to databases of malicious code, machine learning may
soon be able to cluster bits of code written by the same coder, providing one more
clue in the attribution process. Attackers might always further muddy the waters by
repurposing code from other sources, and the fact that advanced persistent threats
typically contain numerous independent coders makes digital fingerprinting difficult.
But the possibility is nonetheless intriguing.
Some of these applications are speculative. There are notable limitations with re -
lying on ML techniques for attribution; for example, threat data can often be incon-
sistent if it is collected from multiple security companies. Still, integrating high-level
cyber threat behavior with machine learning is an understudied area, and further
research may allow federal agencies and cybersecurity companies to augment their
attribution toolkit, while also making some simpler forms of attribution available to
other types of organizations.
Center for Security and Emerging Technology29Conclusions
his report has summarized a range of potential ways AI could be
used to aid cyber defenders as well as some of the limitations of
current techniques. We offer four major conclusions for what this
means for the actual practice of cybersecurity:
1. At the detection stage, ML will allow for continued improvements
over existing detection tools, especially for companies with exten -
sive big data capabilities. However, these developments should be
viewed as incremental advances that introduce new attack surfac-
es of their own.
2. At the prevention, response and recovery, and active defense
stages, the use of ML is less commonplace but is making progress.
Some of the most exciting advances will still require major ML
breakthroughs in order to be fully deployable.
3. Overall, we anticipate that gains from ML will continue to be in-
cremental rather than transformative for the cybersecurity industry.
Barring major ML breakthroughs, the most transformative impacts
may come from ML’s ability to make previously un- or under-uti-
lized defensive strategies more accessible to many organizations.
4. At the present time, machine learning is unlikely to fundamentally
shift the strategic balance of cybersecurity towards attackers or
defenders. However, for specific applications, the introduction of
machine learning techniques may be more useful to one side or the
other. T
Center for Security and Emerging Technology30In focusing on the underlying technical capabilities of machine learning, this
report has set aside many other issues, such as whether or not defenders will suc-
cessfully implement ML systems and fully leverage their potential. These conclusions
are preliminary, not predictive: whether they hold true will depend on how attackers
and defenders choose to invest in and deploy this technology.
ML CAN IMPROVE DETECTION, BUT IT BRINGS RISKS OF ITS
OWN
Machine learning has played an increasingly large role within attack detection
for several decades, and the coming years will likely make it even more import-
ant. Among other things, this is due to the development of better algorithms and
the use of greater computational power.
But classification—whether of specific attack patterns or “anomalies” more
generally—is a task where quality data makes or breaks the model. Because of
the critical importance of data for training classification algorithms, the benefits of
improved detection systems will be most easily leveraged by companies with the
ability to collect the most cyber data. This is one area where defenders have an
asymmetric advantage relative to attackers: defenders can collect and store far
more data about their own networks than attackers can, which makes it possible for
defenders to continually improve their defenses.
Nonetheless, these benefits are likely to be incremental for three reasons. First,
because the size and scale of cyber attacks continues to rise, it is unclear how
important the gains from better ML models will be. Slow and steady improvement in
algorithms and data collection abilities may only be enough to keep pace with the
threat landscape, without allowing defenders to get ahead.
Second, attackers have an asymmetric advantage of their own: an attack only
has to work once, while a defensive strategy has to work all the time. Considering
that attackers can use GANs to generate malicious traffic that resembles benign
traffic enough to evade many types of ML-powered and rules-based defenses, the
defender’s data advantage may not be enough to protect them from the offensive
use of ML methods.80 Further, because most network data tends to be highly vari-
able even under normal conditions, building algorithms that can reliably identify
malicious anomalies without flagging legitimate behavior is a difficult task. This
increases the chances that motivated attackers—especially those using heuristic or
ML tactics to camouflage themselves within a network’s background traffic—will
continue to find ways to slip past even the best detection systems.
Finally, the benefits of newer ML-based detection systems may be less long-last-
ing than many hope, since ML models bring new attack avenues of their own. These
Center for Security and Emerging Technology31attacks can be internal to the ML algorithms themselves, which rely on datasets
that attackers could poison and which attackers can often circumvent using subtly
altered adversarial examples.81 Moreover, the decisions of ML models can also be
opaque, and when they behave in strange or unproductive ways, defenders may be
tempted to make adjustments that introduce additional vulnerabilities, as appears to
have been the case in the Cylance model discussed above.
None of this is meant to trivialize the important role that machine learning can
play in improving detection systems. Even if machine learning cannot vastly improve
baseline detection abilities, deep learning models in particular may be useful for
performing rudimentary triage of alerts in order to identify the behaviors that are
most likely indicative of malicious activity. But most of these developments represent
incremental improvements that require the context of the above caveats. While the
developments are important, we do not view them as likely to significantly transform
the nature of threat detection.
PROGRESS EXISTS AT OTHER STAGES BUT MAY STILL AWAIT
MAJOR BREAKTHROUGHS
While machine learning has already been widely deployed for various types of
detection, it remains rarer in the other stages of the cybersecurity model. None -
theless, researchers have slowly been exploring the use of machine learning for a
diverse range of applications. Table 2 summarizes each of the major ML applica-
tions for each stage that this report has covered.
At the prevention stage, ML systems could one day allow for improved fuzz-
ing systems that software developers can use to detect vulnerabilities before they
are exploited, or for pentesting aids that can more effectively search a network for
vulnerabilities than current tools can. ML systems may also allow defenders to more
accurately identify potential threats by triaging bug reports and attempting to auto-
matically predict the potential severity of a newly discovered vulnerability.
At the response and recovery stage, there has been some progress in building
reinforcement learning systems that are capable of learning when to sequester po-
tentially compromised machines from a network in order to limit an attacker’s lateral
movement. There has also been growing interest in the use of machine learning
to automate defensive strategies, as demonstrated by DARPA’s ongoing project
to build autonomous systems that can neutralize compromised devices within an
attacking botnet.
Center for Security and Emerging Technology32At the active defense stage, there is a broad and increasing number of appli-
cations for which machine learning could be adapted to help defenders deceive,
analyze, and attribute attackers. Dynamic honeypots, deceptive document gener-
ation, and phishing response may soon leverage reinforcement learning and/or
advanced natural language models. In addition, advances in natural language pro-
cessing may improve threat intelligence collection from dark web sources. Finally,
clustering methods and advanced code analysis may allow for more sophisticated
attribution methods, including some that may become available to organizations
that have not traditionally invested in attribution work.
Major advances within these applications could meaningfully transform cyber-
security by automating many tasks that typically consume a cyber defender’s time
or by adding new streams of information and analysis to the defender’s situational
awareness. Nonetheless, these advances may still require significant breakthroughs
of uncertain likelihood. For instance, the computational intensity of reinforcement
learning in highly complex environments continues to make it difficult to build ML
systems that can handle the complexity of cybersecurity networks. Building deploy -
able reinforcement learning–based cyber responders that could fully mimic human
expertise would require major breakthroughs in the ability of researchers to effi-TABLE 2
Summary of AI Applications
PREVENTION DETECTION
Fuzzing Accurate DetectionTABLE 2
Summary of AI Applications
Pentesting
Bug Triage and
ClassiﬁcationAlert Prioritization
Vulnerabilit y Severit y
AssessmentAdversarial Hardening
of Defense SystemsRESPONSE AND REC OVERYA CTIVE DEFENSE
Adversar y Engagement
Moving Target DefenseDeceptive Document
Generation
Dynamic Honeypot ting
Dark W eb Threat Intelligence
Collection
Attack Clustering for At tribution
Code De -AnonymizationAutomated P hishing R esponse
Center for Security and Emerging Technology33ciently represent all relevant network data. Machine learning will likely become an
increasingly important part of each of these stages in the cybersecurity model, but
transforming the work of the cyber defender may still be out of reach for the near- to
medium-term.
EXPECT ML GAINS TO BE INCREMENTAL RATHER THAN
TRANSFORMATIONAL
Machine learning can offer a great deal to cyber defenders across all four stages
of the cybersecurity model used in this paper. Nonetheless, whether or not it is
a truly “transformational” technology depends very heavily on what standards
are used to assess its impact. There is no doubt that machine learning can make
significant improvements on a variety of cybersecurity technologies. If it were not
for machine learning, defenders today would likely be consumed with low-level
analysis that is more often delegated to automated systems which frequently make
heavy use of ML systems. This is especially true of a task like spam detection—
which would likely be all but impossible at scale without machine learning—but it
is also true of intrusion and malware detection.
By counterfactual standards, then, machine learning is indeed a transforma-
tive technology: without it, cybersecurity today would have a very different—and
very worse—record of success. But this does not mean that machine learning is a
truly transformational technology by historical standards. While machine learning
successfully automates many low-level tasks today, the rapidly growing number of
threats and sophisticated attacks facing most organizations means that ML-based Major advances within these applications could mean-
ingfully transform cybersecurity by automating many
tasks that typically consume a cyber defender’s time
or by adding new streams of information and analysis
to the defender’s situational awareness. Nonetheless,
these advances may still require significant break -
throughs of uncertain likelihood.
Center for Security and Emerging Technology34detection systems have mostly only allowed cyber defenders to keep pace with
the threat landscape. So far, machine learning has not transformed the core type
of work that cyber defenders are required to perform, nor has it made the current
cybersecurity skills shortage significantly less worrisome.82
By historical standards, we conclude that machine learning is not presently a
transformational technology. This does not mean that ML is unimportant for cyber
defenders. Continued investment in both researching and implementing promising
ML-cyber tools should be an important area of focus for cyber defenders, and should
be promoted by the federal government wherever possible. But policymakers should
not anticipate that developing better ML tools is a replacement for addressing other,
more fundamental cybersecurity problems, such as encouraging cybersecurity work-
force development or enforcing secure practices across federal agencies.
While we do not think that machine learning is poised to transform the cyberse -
curity industry, this requires two caveats: First, fundamental breakthroughs in ML re -
search may significantly change the threshold of possibility. For instance, if research-
ers are able to develop computationally simpler methods of simulating complicated
networks, reinforcement learning–based tools may be able to take on significantly
more complex tasks than what is currently possible. While we do not currently an-
ticipate machine learning being able to automate many high-level decisions during
the incident response process, a sufficiently large breakthrough might change that
assessment and enable ML tools that could be given substantially more autonomy.
Second, not all the defensive strategies discussed in this report are equally
common, which means that machine learning may be able to have a larger im-
pact on some areas than others—especially where it can make previously un- or
under-utilized strategies more accessible for more organizations. For instance,
some survey results suggest that while upwards of 90 percent of organizations use
antivirus products, the number of organizations actively using honeypots or related Policymakers should not anticipate that developing better
ML tools is a replacement for addressing other, more
fundamental cybersecurity problems, such as encouraging
cybersecurity workforce development or enforcing secure
practices across federal agencies.
Center for Security and Emerging Technology35technologies may be much lower—potentially as low as 25 percent.83 Improvements
in the use of machine learning for malware detection are useful and important—but
they are unlikely to change many organizations’ underlying defensive strategies. By
contrast, if ML tools make it possible to easily deploy honeypots that can realistically
integrate into an existing network, many more organizations might use honeypots.
This would in some ways represent a larger shift in the threat landscape than
improvements in malware detection tools, because the widespread deployment of
a new strategy could have more significant impacts on attackers’ decision-making
processes than an incremental improvement on an existing strategy. As another ex-
ample, some researchers have explored how the presence of deceptive tactics such
as decoys can disorient attackers and shape their strategies.84 If ML makes it easy to
automatically create deceptive internal records or engage in moving target defense,
for instance, then widespread adoption of the technologies may meaningfully shape
attacker strategies, even if the ML models themselves are not particularly impressive.
The mere knowledge that such deceptive tactics are more commonly used may be
enough to influence the attacker’s decision-making.
ML APPLICATIONS PRESENT NO CLEAR-CUT ADVANTAGE TO
OFFENSE OR DEFENSE ON NET
Most of the ML applications this report has discussed are dual-use: they could be
used either by attackers or defenders. In some cases, the opportunity for attackers
is obvious, as in the cases of ML-powered fuzzing and pentesting. In other cases,
attackers might find creative ways to repurpose or circumvent defensive technol-
ogies. For instance, if machine learning is used to calculate the odds of an exploit
being used, attackers might simply incorporate this information into their decisions
regarding which exploits to develop, making the model increasingly useless as at-
tackers adapt and the training data becomes unrepresentative of actual realities.
Because of these dual-use considerations, ML will not give a clear-cut advan -
tage either to attackers or to defenders in the coming years. However, while ML will
not clearly benefit either attackers or defenders overall, certain specific applications
of ML may be either offense-biased or defense-biased.
For example, the rise of sophisticated natural language processing models is
likely to improve spearphishing abilities.85 With the ability to generate detailed, flu-
ent, and personalized text or voice recordings, attackers will be able to engage in
more effective social engineering campaigns. Although the same natural language
processing models can be adapted for defensive purposes—say, to build chatbots
that can mislead social engineers—these are reactive measures and should not
distract from the important takeaway: barring major developments, machine learn -
ing will allow attackers to conduct more effective social engineering campaigns in
Center for Security and Emerging Technology36the near future. Therefore, the use of machine learning for text generation is likely an
offense-biased technology.
At the same time, there is arguably a larger number of ML applications that may
be primarily useful for defenders. For instance, attack detection is a major focus of
ML research for cybersecurity, but attackers may have relatively little use for attack
detection technologies, since there is little need to detect an attack that you yourself
are responsible for.\* Similarly, the use of dynamic honeypots are more obvious-
ly relevant for organizations seeking to protect their networks than for attackers
hoping to penetrate them, which makes ML-based honeypots another generally
defense-biased technology. These are technologies where defenders may generally
gain more than attackers from increases in ML adoption.
SUMMARY
Policymakers and practitioners alike need to think about how machine learning
can alter specific tasks within cybersecurity, rather than talking in general terms
about how machine learning can alter cybersecurity as a whole. We have at-
tempted to provide greater clarity by surveying a range of potential machine
learning applications and the factors that continue to constrain their develop-
ment. In addition, by focusing on the continuity in the use of machine learning
for cybersecurity tasks dating back to the 1990s, we have aimed to demonstrate
that newer breakthroughs will bring evolutionary cybersecurity gains rather than
revolutionary changes to the industry.
It is possible that one day, machine learning tools will automate entire swathes
of our cybersecurity model at once, seamlessly performing the work of multiple
stages instead of functioning as tools that merely aid in already-existing, well-de -
fined tasks. But in the near- to medium-term future, this possibility is remote. It is far
more pragmatic for policymakers and practitioners to work towards a more nu-
anced understanding of the types of tasks that could benefit from machine learning
and the types of tasks that can’t. Effective policy will need to take these nuances
into account to promote useful types of research, enable organizations to take full
advantage of new machine learning tools, and help defenders prepare against their
constantly improving adversaries.
\*Attackers who study newer detection models may be able to find ways to craft adversarial attacks
that can bypass them, so progress in this area is only strictly defense-biased if it results in detection
systems that are less vulnerable to adversarial attacks. Since it is not clear that this is the case with
newer ML detection models, it is unclear whether current improvements in ML detection systems are
capable of shifting the overall strategic balance towards defenders.
Center for Security and Emerging Technology37Appendix
BASIC STRUC TURE ADVANTAGES
Deep learning is based on th e
use of ar tiﬁcial neural
networks and may include
hundreds or th ousands of
nodes wh ose structure can be
adapted for speciﬁc task s.APPENDIX
DISAD VANTAGES
DEEP LEARNING
REINFOR CEMENT
LEARNING
GENER ATIVE
ADVERSARIAL
NETWORKS (GANs)
MASSIVE NA TURAL
LANGUA GE MODEL SReinforcement learning
simulates an agent with
several available actions, an
environment, and a reward
function and allows th e agent
to experiment with the use of
different actions to learn
strategies that maximize
rewards.
A GAN includes tw o neural
networks: a generator that
uses random input to create
new inputs and a
discriminator that at tempts to
distinguish real from
generated inputs.
These models are not uniﬁed
by a single underlying
structure but are instead
characterized by their use of
massive models that can
include billions of parameters
in order to learn pa tterns
within human language.1) Performance can of ten
improve with more data, well
after per formance in simpler
models ﬂat tens
2) Can be used as an underlying
approach for most AI applications
3) Models can of ten
automatically learn to ex tract
relevant features from data1) Requires signiﬁcant processing
power to run and signiﬁcant
memor y to store due to model
size
2) Widely regarded as one of th e
most opaque t ypes of machine
learning
3) Beneﬁts of more data may be
difﬁcult to fully leverage in th e
cyber domain, which is of ten ve ry
secretiv e
1) Allows for AI agents that can
learn strategic thinking
2) Can be combined with deep
learning systems to improve
performance
3) Does not require historical
data if realistic environments can
be simulated1) Computational cost increases
extremely quickly wh en the
environment complexit y or
number of actions are increased
2) Difﬁcult to simulate realistic
yet computationally tractable
cyber net works
1) Can be combined with deep
learning systems to improve
performance
2) Can be used to augment
existing data sets or evade
existing classiﬁers1) Does not have
general-purpose applications
like deep learning
2) Typical caveats about th e
need for robust underlying data
apply
1) Increasingly capable of
automating te xt generation,
summarization, and translation,
among oth er task s
2) Could be used to automate
securit y task s that involve writ ten
text (e.g. generating bug repor ts)1) Cannot be built by individual
companies with out investing
millions of dollars; access will
depend on ho w corporate owners
choose to make them available
Center for Security and Emerging Technology26
Center for Security and Emerging Technology39Endnotes
1. For spam detection, see James Vincent, “Gmail Is Now Blocking 100 Million Extra Spam Messages Every
Day with AI,” The Verge, February 6, 2019, https://www.theverge.com/2019/2/6/1 82 1 3453/gmail-
tensorflow-machine-learning-spam-100-million. For malware detection, see Lily Hay Newman, “Gmail Is
Catching More Malicious Attachments With Deep Learning,” WIRED, February 25, 2020, https://www.
wired.com/story/gmail-catching-more-malicious-attachments-deep-learning/.
2. Stephan Somogyi and Allison Miller, “Safe Browsing: Protecting more than 3 billion devices worldwide,
automatically,” Google Security Blog, September 1 1, 2017 , https://security.googleblog.com/2017/09/
safe-browsing-protecting-more-than-3\_1 1.html.
3. Catalin Cimpanu, “Symantec, ESET, McAfee rank first in Windows anti-malware market share,” ZDNet,
November 1 8, 2019, https://www.zdnet.com/article/symantec-eset-mcafee-rank-first-in-windows-
anti-malware-market-share; Microsoft Defender ATP Research Team, “Windows Defender ATP Machine
Learning: Detecting New and Unusual Breach Activity,” Microsoft, August 3, 2017 , https://www.microsoft.
com/security/blog/2017/08/03/windows-defender-atp-machine-learning-detecting-new-and-unusual-
breach-activity/; Beth Stackpole, “Machine Learning: A Modern-Day Cyber Security Hero?” Broadcom,
June 4, 2019, https://symantec-enterprise-blogs.security.com/blogs/feature-stories/machine-learning-
modern-day-cyber-security-hero; Ondrej Kubovič et al., “ESET Advanced Machine Learning” (ESET,
November 2019), https://www.welivesecurity.com/wp-content/uploads/2019/1 1/ESET\_Advanced\_
Machine\_Learning.pdf.
4. See Ben Buchanan, The Hacker and the State: Cyber Attacks and the New Normal of Geopolitics
(Cambridge, MA: Harvard University Press, 2020).
5. Rob Sobers, “1 10 Must-Know1 34 Cybersecurity Statistics and Trends for 202 10,” Inside Out Security Blog on
Varonis, accessed June May 25 17 , 202 10, https://www.varonis.com/blog/cybersecurity-statistics/.
6. Ben Buchanan, John Bansemer, Dakota Cary, Jack Lucas, and Micah Musser, “Automating Cyber Attacks”
(Center for Security and Emerging Technology, November 2020), https://doi.org/10.5 1 593/2020CA002.
7. For commentary in popular media organizations, see Danny Palmer, “AI Is Changing Everything about
Cybersecurity, for Better and for Worse. Here’s What You Need to Know,” ZDNet, March 2, 2020, https://
www.zdnet.com/article/ai-is-changing-everything-about-cybersecurity-for-better-and-for-worse-heres-
what-you-need-to-know/; Lily Hay Newman, “AI Can Help Cybersecurity—If It Can Fight Through the
Hype,” WIRED, April 29, 201 8, https://www.wired.com/story/ai-machine-learning-cybersecurity/.
For perspectives on autonomous cyber defenders in the defense community, see Carlo Munoz, “DARPA
Sees ‘Rich Space’ for Advanced AI in Cyber Operations,” Janes, August 3, 2020, https://www.janes.
com/defence-news/news-detail/darpa-sees-rich-space-for-advanced-ai-in-cyber-operations; Paul
Théron and Alexander Kott, “When Autonomous Intelligent Goodware Will Fight Autonomous Intelligent
Malware: A Possible Future of Cyber Defense,” in Proceedings of the Military Communications Conference,
(MILCOM-2019, November 1 2-1 4, 2019), https://arxiv.org/ftp/arxiv/papers/191 2/191 2.01959.pdf.
8. “Darktrace Antigena: Product Overview,” Darktrace, accessed April 30, 202 1, https://www.darktrace.
com/en/resources/ds-antigena.pdf. “Project Blackfin,” F-Secure, accessed May 6, 202 1, https://www.f-
secure.com/en/about-us/research/project-blackfin.
9. Although there have been some improvements in the algorithms used to build ML systems, many researchers
agree that the advances of the last decade have had far more to do with increased data availability and
computing power, rather than improvements in algorithms. For a discussion of the various parts of the “AI
triad,” see Ben Buchanan, “The AI Triad and What It Means for National Security Strategy” (Center for
Security and Emerging Technology, August 2020), https://doi.org/10.5 1 593/2020002 1 .
Center for Security and Emerging Technology4010. Tami Casey, “Survey: 27 Percent of IT professionals receive more than 1 million security alerts daily,”
Imperva, May 28, 201 8, https://www.imperva.com/blog/27-percent-of-it-professionals-receive-more-
than-1-million-security-alerts-daily/.
11 . For a competing view, see David Brumley, “Why I’m not sold on machine learning in autonomous security,”
CSO Online, August 27 , 2019, https://www.csoonline.com/article/343408 1/why-im-not-sold-on-
machine-learning-in-autonomous-security.html.
1 2. “Framework for Improving Critical Infrastructure Cybersecurity: Version 1. 1,” National Institute of Standards
and Technology, April 16, 201 8, https://nvlpubs.nist.gov/nistpubs/CSWP/NIST.CSWP.0416201 8.pdf.
13 . “M-Trends 2020” (FireEye, 2020), 1 1–1 5, https://content.fireeye.com/m-trends/rpt-m-trends-2020.
1 4. Christopher Jarko, “Finding the Fine Line — Taking an Active Defense Posture in Cyberspace without
Breaking the Law or Ruining an Enterprise's Reputation” (SANS Institute, February 6, 2016), https://
www.sans.org/reading-room/whitepapers/legal/finding-fine-line-active-defense-posture-cyberspace-
breaking-law-ruining-enterprise%E2%80%99s-reputation-36807 .
15. Paul Graham, “A Plan for Spam,” August 2002, http://www.paulgraham.com/spam.html.
16. For an example of an automated tool that can perform feature extraction, see Wadi' Hijawi et al.,
“Improving Email Spam Detection Using Content Based Feature Engineering Approach,” in 20 1 7 IEEE
Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT) (ieeexplore.
ieee.org, 2017), 1–6.; for a discussion of how machine learning can be used to aid feature extraction, see
Melvin Diale, Turgay Celik, and Christiaan Van Der Walt, “Unsupervised Feature Learning for Spam Email
Filtering,” Computers & Electrical Engineering 7 4 (March 1, 2019): 89–104.
17. For a review of common machine learning methods used for spam detection, see Emmanuel Gbenga Dada
et al., “Machine Learning for Email Spam Filtering: Review, Approaches and Open Research Problems,”
Heliyon 5, no. 6 (June 2019): e01 802.
1 8. Anne Johnson and Emily Grumbling, “Implications of Artificial Intelligence for Cybersecurity: Proceedings of
a Workshop (2019)” (The National Academies Press, 2019), 25–26, https://doi.org/10. 17226/25488.
19. Much academic research on intrusion detection systems continues to rely on these datasets generated
between 1998 and 2000. A 201 8 review estimated that roughly 75% of the previous decade’s top-cited
papers on ML for intrusion detection relied on one of these datasets or their later variants. See H. Hindy et
al., “A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection Systems,”
IEEE Access 8 (2020): 104650–75. Unfortunately, there are few up-to-date public datasets available for
researchers to use in their research instead, which means that reliance on these datasets continues to be a
problem despite well-known flaws with this data. (For a discussion of some of the flaws in this data, see R.
Sommer and V. Paxson, “Outside the Closed World: On Using Machine Learning for Network Intrusion
Detection,” in 20 10 IEEE Symposium on Security and Privacy, 2010, 305–16.) This is a major area where
policymakers may be able to advance the research community’s ability to develop better methods by
funding the creation of newer datasets for academic use, though we do not develop this suggestion in the
present work.
20. Richard Lippmann et al., “The 1999 DARPA Off-Line Intrusion Detection Evaluation,” Computer Networks
34, no. 4 (October 1, 2000): 579–95.
21. Yang Xin et al., “Machine Learning and Deep Learning Methods for Cybersecurity,” IEEE Access 6 (201 8):
35365–8 1.
22. Accurate descriptions of model architectures in corporate products are difficult to find. However, as one
example for the claim that deep learning is not fully ubiquitous, consider Google Play Protect, Google’s
machine learning–based approach for detecting malicious apps in the Android store, which it calls “the most
widely deployed mobile threat protection service in the world.” (See “Google Play Protect: 2.5 Billion Active
Devices,” Android, accessed October 22, 2020, https://www.android.com/play-protect/.) As recently as
November 201 8, Google’s research blog claimed that Google Play Protect used both deep learning and
other, much simpler approaches such as logistic regression—a simple mathematical tool as old as the 19th
century. Mo Yu, Damien Octeau, and Chuangang Ren, “Combating Potentially Harmful Applications with
Machine Learning at Google: Datasets and Models,” Google Security Blog, November 1 5, 201 8, https://
security.googleblog.com/201 8/1 1/combating-potentially-harmful\_1 4.html.
Center for Security and Emerging Technology4123. Francesco Palmieri, Ugo Fiore, and Aniello Castiglione, “A Distributed Approach to Network Anomaly
Detection Based on Independent Component Analysis,” Concurrency and Computation: Practice &
Experience 26, no. 5 (April 10, 201 4): 1 1 1 3–29; Yu Gu, Andrew McCallum, and Don Towsley, “Detecting
Anomalies in Network Traffic Using Maximum Entropy Estimation,” Internet Measurement Conference,
2005, https://www.usenix.org/legacy/event/imc05/tech/full\_papers/gu/gu.pdf.
24. See, for instance, the discussion of false positives in Johnson and Grumbling, “Implications of Artificial
Intelligence for Cybersecurity: Proceedings of a Workshop (2019),” 23, 26–27.
25. Stephen Verbeke, “COVID-19’s Impact on Cybersecurity Incident Response,” Novetta, May 26, 2020,
https://www.novetta.com/2020/05/cyber-covid/.
26. Nicholas Duran et al., “201 8 Webroot Threat Report,” Webroot, 201 8, https://www-cdn.webroot.
com/93 1 5/2354/6488/201 8-Webroot-Threat-Report\_US-ONLINE.pdf.
27. Mustafa Irshad et al., “Effective Methods to Detect Metamorphic Malware: A Systematic Review,”
International Journal of Electronic Security and Digital Forensics 10, no. 2 (January 201 8): 1 38–54.
28. G. J. Tesauro, J. O. Kephart, and G. B. Sorkin, “Neural Networks for Computer Virus Recognition,” IEEE
Expert 1 1, no. 4 (August 1996): 5–6.
29. For research exploring the use of Hidden Markov models, see: Wing Wong and Mark Stamp, “Hunting
for Metamorphic Engines,” Journal in Computer Virology 2, no. 3 (December 1, 2006): 2 1 1–29.; for a
general summary of much of this research, see Muazzam Ahmed Siddiqui, Morgan Wang, and Joohan
Lee, “A Survey of Data Mining Techniques for Malware Detection Using File Features,” in ACM-SE 46:
Proceedings of the 46th Annual Southeast Regional Conference on XX (Auburn, Alabama: Association for
Computing Machinery, March 2008), https://doi.org/10. 1 1 45/1 593 105. 1 593239 .
30. Daniel Gibert, Carles Mateu, and Jordi Planes, “The rise of machine learning for detection and
classification of malware: Research developments, trends and challenges,” Journal of Network and
Computer Applications 1 53 (March 2020), https://doi.org/10. 1016/j.jnca.2019. 102526.
31. Hemant Rathore et al., “Malware Detection Using Machine Learning and Deep Learning,” arXiv [cs.CR]
(April 4, 2019), arXiv, http://arxiv.org/abs/1904.02441 .
32. Sophisticated malware has grown increasingly better at detecting that it is being run in sandboxes and
changing or stopping its own execution through a number of measures, such as delaying execution,
hardware detection, or requiring input from the user. See Thomas Roccia and Chintan Shah, “Evolution
of Malware Sandbox Evasion Tactics – A Retrospective Study,” McAfee, September 9, 2019, https://
www.mcafee.com/blogs/other-blogs/mcafee-labs/evolution-of-malware-sandbox-evasion-tactics-a-
retrospective-study/.
33. S. L. S. Darshan, M. A. A. Kumara, and C. D. Jaidhar, “Windows Malware Detection Based on Cuckoo
Sandbox Generated Report Using Machine Learning Algorithm,” in 20 16 1 1th International Conference
on Industrial and Information Systems (ICIIS), 2016, 534–39; Chani Jindal et al., “Neurlux: Dynamic
Malware Analysis Without Feature Engineering,” arXiv [cs.CR] (October 24, 2019), arXiv, https://doi.
org/10. 1 1 45/3359789.3359835 .
34. Thanassis Avgerinos et al., “The Mayhem Cyber Reasoning System,” IEEE Security & Privacy 16, no. 2
(March/April 201 8): 55–56.
35. It is worth noting that the head of the 2016 winning team, David Brumley, remains skeptical about the
use of AI in cybersecurity. See David Brumley, “Why I’m not sold on machine learning in autonomous
security,” CSO Online, August 27 , 2019, https://www.csoonline.com/article/343408 1/why-im-not-
sold-on-machine-learning-in-autonomous-security.html.
36. Yan Wang et al., “A Systematic Review of Fuzzing Based on Machine Learning Techniques,” PloS One
1 5, no. 8 (August 1 8, 2020): e02377 49.
37. Yunchao Wang et al., “NeuFuzz: Efficient Fuzzing With Deep Neural Network,” IEEE Access 7 (January
2019): 36340–52.
38. Mohit Rajpal, William Blum, and Rishabh Singh, “Not All Bytes Are Equal: Neural Byte Sieve for
Fuzzing,” arXiv [cs.SE] (November 10, 2017), arXiv, http://arxiv.org/abs/17 1 1.04596.
39. Metasploit-Framework (Github), accessed September 10, 2020, https://github.com/rapid7/metasploit-
framework.
Center for Security and Emerging Technology4240. Dean Richard McKinnel et al., “A Systematic Literature Review and Meta-Analysis on Artificial Intelligence
in Penetration Testing and Vulnerability Assessment,” Computers & Electrical Engineering 75 (May 1, 2019):
175–88.
41. Fabio Massimo Zennaro and Laszlo Erdodi, “Modeling Penetration Testing with Reinforcement Learning
Using Capture-the-Flag Challenges and Tabular Q-Learning,” arXiv [cs.CR] (May 26, 2020), arXiv,
http://arxiv.org/abs/2005. 1 2632.
42. Mohamed C. Ghanem and Thomas M. Chen, “Reinforcement Learning for Efficient Network Penetration
Testing,” Information–An International Interdisciplinary Journal 1 1, no. 1 (December 20, 2019): 6.
43. Jonathon Schwartz and Hanna Kurniawati, “Autonomous Penetration Testing Using Reinforcement
Learning,” arXiv [cs.CR] (May 1 5, 2019), arXiv, http://arxiv.org/abs/1905.05965 .
44. Demis Hassabis, The Power of Self-Learning Systems, Video (United States: Center for Brains, Minds &
Machines, March 2019), https://cbmm.mit.edu/video/power-self-learning-systems.
45. Catalin Cimpanu, “Cobalt Strike and Metasploit accounted for a quarter of all malware C&C servers
in 2020,” ZDNet, January 7 , 202 1, https://www.zdnet.com/article/cobalt-strike-and-metasploit-
accounted-for-a-quarter-of-all-malware-c-c-servers-in-2020/.
46. For an initial attempt to assign bug reports to developers, see John Anvik, Lyndon Hiew, and Gail C.
Murphy, “Who Should Fix This Bug?,” in Proceedings of the 28th International Conference on Software
Engineering, ICSE ’06 (New York, NY: Association for Computing Machinery, 2006), 361–70; for
an attempt to classify security and non-security bugs, see Shaikh Mostafa et al., “SAIS: Self-Adaptive
Identification of Security Bug Reports,” IEEE Transactions on Dependable and Secure Computing, 2019,
1–1. For automated generation of malware characteristics, see Swee Kiat Lim et al., “MalwareTextDB: A
Database for Annotated Malware Articles,” in Proceedings of the 55th Annual Meeting of the Association
for Computational Linguistics (Volume 1: Long Papers) (Vancouver, Canada: Association for Computational
Linguistics, 2017), 1 557–67.
47. Mayana Pereira and Scott Christiansen, “Identifying Security Bug Reports Based Solely on Report Titles
and Noisy Data,” Microsoft, March 6, 2020, https://docs.microsoft.com/en-us/security/engineering/
identifying-security-bug-reports.
48. Georgios Spanos, Lefteris Angelis, and Dimitrios Toloudis, “Assessment of Vulnerability Severity Using Text
Mining,” in Proceedings of the 2 1st Pan-Hellenic Conference on Informatics, PCI 2017 49 (New York, NY:
Association for Computing Machinery, 2017), 1–6.
49. Jay Jacobs et al., “Exploit Prediction Scoring System (EPSS),” arXiv [cs.CR] (August 1 3, 2019), arXiv,
http://arxiv.org/abs/1908.04856.
50. Jay Jacobs et al., “Improving Vulnerability Remediation through Better Exploit Prediction,” Journal of
Cybersecurity 6, no. 1 (September 1 4, 2020), https://doi.org/10. 1093/cybsec/tyaa01 5 .
51. See note 7 , above.
52. For a primer on this topic, see Tim G. J. Rudner and Helen Toner, “Key Concepts in AI Safety: Robustness
and Adversarial Examples” (Center for Security and Emerging Technology, March 202 1), https://doi.
org/10.5 1 593/20190041 .
53. For a primer on threats to AI systems, see Andrew Lohn, “Hacking AI” (Center for Security and Emerging
Technology, December 2020), https://doi.org/10.5 1 593/2020CA006.
54. “Cylance, I Kill You!” Skylight, July 1 8, 2019, https://skylightcyber.com/2019/07/1 8/cylance-i-kill-
you/.
55. Ram Shankar Siva Kumar, “Azure Sentinel Uncovers the Real Threats Hidden in Billions of Low Fidelity
Signals,” Microsoft, February 20, 2020, https://www.microsoft.com/security/blog/2020/02/20/
azure-sentinel-uncovers-real-threats-hidden-billions-low-fidelity-signals/. This is also an area of
active interest in the defense community; see Jason Miller, “Defense Innovation Unit Out to Prove AI,
Automation Can Keep Up With the Speed of Cyber,” Federal News Network, October 30, 2020,
https://federalnewsnetwork.com/ask-the-cio/2020/10/defense-innovation-unit-out-to-prove-ai-
automation-can-keep-up-with-the-speed-of-cyber/ and Lauren C. Williams, “AI Meets Cyber as Army
Tests Tactical Network Protection,” Federal Computer Week, November 4, 2020, https://fcw.com/
articles/2020/1 1/04/netmodx-army-tactical-test.aspx.
Center for Security and Emerging Technology4356. Zilong Lin, Yong Shi, and Zhi Xue, “IDSGAN: Generative Adversarial Networks for Attack Generation Against
Intrusion Detection,” arXiv [cs.CR] (September 6, 201 8), arXiv, http://arxiv.org/abs/1 809.02077; Aritran
Piplai, Sai Sree Laya Chukkapalli, and Anupam Joshi, “NAttack! Adversarial Attacks to Bypass a GAN Based
Classifier Trained to Detect Network Intrusion,” arXiv [cs.LG] (February 20, 2020), arXiv, http://arxiv.org/
abs/2002.08527; Weiwei Hu and Ying Tan, “Generating Adversarial Malware Examples for Black-Box
Attacks Based on GAN,” arXiv [cs.LG] (February 20, 2017), arXiv, http://arxiv.org/abs/1702.05983 .
GANs are not the only type of AI architecture that can be used to generate adversarial attacks to evade
malware detection or intrusion detection; see, e.g., Hyrum S. Anderson et al., “Learning to Evade Static PE
Machine Learning Malware Models via Reinforcement Learning,” arXiv [cs.CR] (January 30, 201 8), arXiv,
https://arxiv.org/abs/1 801.08917 .
57. Muhammad Usama et al., “Generative Adversarial Networks For Launching and Thwarting Adversarial
Attacks on Network Intrusion Detection Systems,” in 20 19 1 5th International Wireless Communications
Mobile Computing Conference (IWCMC) (Tangier, Morocco: 2019), 78–83.
58. Gray Stanton and Athirai A. Irissappane, “GANs for Semi-Supervised Opinion Spam Detection,” arXiv
[cs.LG] (March 19, 2019), arXiv, http://arxiv.org/abs/1903.08289; Hojjat Aghakhani et al., “Detecting
Deceptive Reviews Using Generative Adversarial Networks,” arXiv [cs.CR] (May 25, 201 8), arXiv, http://
arxiv.org/abs/1 805. 10364; Jin-Young Kim, Seok-Jun Bu, and Sung-Bae Cho, “Zero-Day Malware Detection
Using Transferred Generative Adversarial Networks Based on Deep Autoencoders,” Information Sciences
460–461 (May 1, 201 8), https://doi.org/10. 1016/j.ins.201 8.04.092.
59. See Florian Tramèr et al., “On Adaptive Attacks to Adversarial Example Defenses,” arXiv [cs.LG] (October 23,
2020), arXiv, https://arxiv.org/abs/2002.08347 .
60. See David Silver et al., “AlphaZero: Shedding new light on chess, shogi, and Go,” DeepMind, December
6, 201 8, https://deepmind.com/blog/article/alphazero-shedding-new-light-grand-games-chess-shogi-
and-go; The AlphaStar Team, “AlphaStar: Grandmaster level in StarCraft II using multi-agent reinforcement
learning,” DeepMind, October 30, 2019, https://deepmind.com/blog/article/AlphaStar-Grandmaster-
level-in-StarCraft-II-using-multi-agent-reinforcement-learning.
61. National Science and Technology Council, Artificial Intelligence and Cybersecurity: Opportunities and
Challenges: Technical Workshop Summary Report (Washington, DC: Executive Office of the President, March
2020), 6, https://www.nitrd.gov/pubs/AI-CS-Tech-Summary-2020.pdf.
62. Dustin Fraze, “Harnessing Autonomy for Countering Cyberadversary Systems (HACCS),” DARPA, accessed
October 22, 2020, https://www.darpa.mil/program/harnessing-autonomy-for-countering-cyberadversary-
systems. Note that this program has not yet concluded, making it difficult to ascertain its success and even, to a
certain extent, how its ultimate goals have developed over time.
63. Taha Eghtesad, Yevgeniy Vorobeychik, and Aron Laszka, “Adversarial Deep Reinforcement Learning
Based Adaptive Moving Target Defense,” arXiv [cs.CR] (November 27 , 2019), arXiv, http://arxiv.org/
abs/191 1. 1 1972.
64. Ahmad Ridley, “Machine Learning for Autonomous Cyber Defense,” The Next Wave, 201 8, 7–1 4; Thanh Thi
Nguyen and Vijay Janapa Reddi, “Deep Reinforcement Learning for Cyber Security,” arXiv [cs.CR] (July 2 1,
2020), arXiv, https://arxiv.org/abs/1906.05799.
65. Jarko, “Finding the Fine Line — Taking an Active Defense Posture in Cyberspace without Breaking the Law or
Ruining an Enterprise's Reputation.”
66. Heather A. Conley and Jean-Baptiste Jeangène Vilmer, “Successfully Countering Russian Electoral
Interference” (Center for Strategic and International Studies, June 2 1, 201 8), https://www.csis.org/analysis/
successfully-countering-russian-electoral-interference.
67. See, for instance, Robert Gutzwiller et al., “’Oh, Look, a Butterfly!’ A Framework for Distracting Attackers
to Improve Cyber Defense,” in Proceedings of the Human Factors and Ergonomics Society 20 18 Annual
Meeting, 272-276.
68. Markus Ring et al., “Flow-based network traffic generation using Generative Adversarial Networks,”
Computers & Security 82 (May 2019): 1 56-172.
69. National Academies of Sciences, Engineering, and Medicine, “Implications of Artificial Intelligence for
Cybersecurity: Proceedings of a Workshop” (Washington, DC: The National Academies Press, 2019), 2-5.
Center for Security and Emerging Technology4470. W. Z. Ansiry Zakaria and M. L. M. Kiah, “A Review on Artificial Intelligence Techniques for Developing
Intelligent Honeypot,” in 20 1 2 8th International Conference on Computing Technology and Information
Management (NCM and ICNIT), vol. 2 (ieeexplore.ieee.org, 201 2), 696–701; D. Fraunholz, M.
Zimmermann, and H. D. Schotten, “An Adaptive Honeypot Configuration, Deployment and Maintenance
Strategy,” in 20 1 7 19th International Conference on Advanced Communication Technology (ICACT)
(ieeexplore.ieee.org, 2017), 53–57.
71. Adrian Pauna and Ion Bica, “RASSH - Reinforced Adaptive SSH Honeypot,” in 201 4 10th International
Conference on Communications (COMM), 201 4, 1–6.
72. Kylie Foy, “Artificial Intelligence Shines Light on the Dark Web,” MIT News, May 1 3, 2019, https://news.
mit.edu/2019/lincoln-laboratory-artificial-intelligence-helping-investigators-fight-dark-web-crime-05 1 3;
Eric Nunes et al., “Darknet and Deepnet Mining for Proactive Cybersecurity Threat Intelligence,” arXiv [cs.
CR] (July 28, 2016), arXiv, http://arxiv.org/abs/1607.08583.
73. Daniel Fraunholz et al., “YAAS-On the Attribution of Honeypot Data,” IJCSA 2, no. 1 (2017): 3 1–48.
7 4. Adam Dalton et al., “The Panacea Threat Intelligence and Active Defense Platform,” arXiv [cs.CL] (April 20,
2020), arXiv, http://arxiv.org/abs/2004.09662.
75. Walter Weiss, “Active Social Engineering Defense (ASED),” DARPA, accessed October 22, 2020, https://
www.darpa.mil/program/active-social-engineering-defense.
76. Umara Noor et al., “A Machine Learning-Based FinTech Cyber Threat Attribution Framework Using High-
Level Indicators of Compromise,” Future Generations Computer Systems: FGCS 96 (July 1, 2019): 227–42;
Steve Miller and Tom Davenport, “Machine Learning Support for Cyber Threat Attribution at FireEye,”
FireEye, June 1 5, 2020, https://www.fireeye.com/blog/products-and-services/2020/06/machine-
learning-support-for-cyber-threat-attribution-at-fireeye.html.
77. Microsoft Defender Security Research Team, “From Unstructured Data to Actionable Intelligence: Using
Machine Learning for Threat Intelligence,” Microsoft, August 8, 2019, https://www.microsoft.com/security/
blog/2019/08/08/from-unstructured-data-to-actionable-intelligence-using-machine-learning-for-threat-
intelligence/.
78. Chen Qian, Tianchang He, and Rao Zhang, “Deep Learning Based Authorship Identification,” Department
of Electrical Engineering, Stanford University, 2017 , https://web.stanford.edu/class/archive/cs/cs224n/
cs224n. 1 17 4/reports/27601 85.pdf.
79. Louise Matsakis, “Even Anonymous Coders Leave Fingerprints,” WIRED, August 10, 201 8, https://www.
wired.com/story/machine-learning-identify-anonymous-code/.
80. See note 54, above.
81. See Lohn, “Hacking AI.”
82. The fact that machine learning has not, to date, meaningfully transformed the day-to-day of a typical
cybersecurity defender can be somewhat substantiated by examining hiring trends in the cybersecurity
industry. If ML were not merely a useful tool, but an innovation that was actively reshaping the life of cyber
defenders, we would expect to see a marked increase in demand for cyber defenders with technical
knowledge of ML systems. But, as other research from CSET has demonstrated, the increase in demand for
employees with even very limited knowledge of both cybersecurity and ML has grown only slowly over
the previous decade. See Cindy Martinez and Micah Musser, “U.S. Demand for Talent at the Intersection
of AI and Cybersecurity” (Center for Security and Emerging Technology, November 2020), https://doi.
org/10.5 1 593/2020CA009 .
83. Andrea Dominguez, “The State of Honeypots: Understanding the Use of Honey Technologies Today” (SANS
Institute, November 2017), https://www.sans.org/reading-room/whitepapers/detection/state-honeypots-
understanding-honey-technologies-today-38 165 .
84. See Gutzwiller, “’Oh, Look, a Butterfly!’”
85. Buchanan et al., “Automating Cyber Attacks.”
Center for Security and Emerging Technology57

CSET.GEORGETOWN.EDU | CSET@GEORGETOWN.EDU