# cyber-threat-detector-v1

## Overview
This model is specialized in detecting malicious intent within command-line strings and network log entries. It helps security analysts flag potentially harmful activity in real-time.

## Model Architecture
- **Base Model:** RoBERTa-base
- **Fine-tuning:** Trained on custom cybersecurity datasets including obfuscated scripts and SQL injection attempts.

## Intended Use
- Security Information and Event Management (SIEM) integration.
- Automated log auditing.
- DevSecOps pipeline monitoring.

## Limitations
- May produce false positives on complex, valid administrative scripts.
- Only supports text-based logs; does not analyze binary packets.

## Example Code
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("cyber-threat-detector-v1")
model = AutoModelForSequenceClassification.from_pretrained("cyber-threat-detector-v1")

log_line = "SELECT * FROM users WHERE id = '1' OR '1'='1';"
inputs = tokenizer(log_line, return_tensors="pt")
outputs = model(**inputs)
prediction = torch.argmax(outputs.logits)
print("Threat Detected" if prediction == 1 else "Safe")