darkknight25/WAF_DETECTION_DATASET

WAF Detection Dataset Overview This dataset is designed for identifying and fingerprinting Web Application Firewalls (WAFs) through advanced, unconventional payloads. It supports cybersecurity researchers, penetration testers, and data scientists in analyzing WAF behavior, detection techniques, and bypass strategies. The dataset includes 650 entries, each representing a unique WAF interaction with carefully crafted malicious payloads. Dataset Structure The dataset is in JSONL (JSON Lines) format, where each line is a valid JSON object with the following fields:

waf_id: Unique identifier for the entry (integer, 1–650).
target: Target URL (e.g., https://secure.network.com).
waf: WAF provider (e.g., Cloudflare, AWS WAF, Imperva).
status_code: HTTP status code returned (e.g., 403, 406).
headers: Key-value pairs of response headers (e.g., {"Server": "cloudflare"}).
payload: Malicious payload used (e.g., obfuscated SQL injection, XSS).
technique: Detection method (e.g., Error Code + Header, JS Payload Blocked).
description: Brief description of the payload and WAF detection.

Example Entry

{
  "waf_id": 451,
  "target": "https://secure.network.com",
  "waf": "Cloudflare",
  "status_code": "403",
  "headers": {"Server": "cloudflare", "CF-Ray": "6e8f2d4b"},
  "payload": "uNiOn/*%0d%0a*/SeLeCt/*%0a%0d*/1,2,3,null,null,null--%0a%0c",
  "technique": "Error Code + Header",
  "description": "SQL injection with multiline comments, nulls, and form feed padding blocked by Cloudflare header"
}

Key Features

Diverse WAF Providers: Includes 34 WAFs, such as Cloudflare, AWS WAF, FortiWeb, Imperva, Akamai, Sucuri, F5 BIG-IP, and more.
Sophisticated Payloads: Features deeply obfuscated SQL injections, multi-layered encoded XSS, nested command injections, and protocol manipulations with randomized padding.
Realistic Scenarios: Simulates real-world WAF interactions with unique domains, realistic headers, and varied detection techniques.
Comprehensive Techniques: Covers error codes, headers, cookies, and payload-specific blocks for robust fingerprinting.

Usage

Download: Obtain the dataset (waf_detection_dataset.jsonl) from the repository. Parse: Use a JSONL parser (e.g., Python’s json module) to process the file.

import json
with open('waf_detection_dataset.jsonl', 'r') as f:
    for line in f:
        entry = json.loads(line)
        print(entry['waf'], entry['payload'])

Analyze: Leverage the dataset for WAF fingerprinting, machine learning model training, or penetration testing research. Test: Use payloads responsibly in controlled environments with permission to evaluate WAF configurations.

Applications

WAF Fingerprinting: Identify WAF presence and type based on response patterns.
Penetration Testing: Develop and test WAF bypass techniques.
Machine Learning: Train models for automated WAF detection and classification.
Security Research: Study WAF behavior against advanced attack vectors.

Contribution Guidelines

Add Entries: Submit new entries with unique payloads or WAFs via pull requests.
Validate Format: Ensure JSONL syntax and field consistency.
Ethical Use: Only contribute payloads tested in authorized environments.
Issues: Report errors or suggestions via the issue tracker.

License This dataset is licensed under the MIT License. See LICENSE for details.

Disclaimer This dataset is for educational and research purposes only. Do not use payloads in unauthorized systems or networks. The authors are not responsible for misuse. Contact For questions or feedback, open an issue or contact [email protected].