Canstralian/CySec_Known_Exploit_Analyzer

CySec Known Exploit Analyzer

Overview

• The CySec Known Exploit Analyzer is developed to:
  • Detect and assess known cybersecurity exploits.
  • Identify vulnerabilities and exploit attempts in network traffic.
  • Provide real-time threat detection and analysis.

Model Details

• Type: Neural Network
• Input:
  • Network traffic logs
  • Exploit payloads
  • Related security information
• Output:
  • Classification of known exploits
  • Anomaly detection
• Training Data:
  • Based on the cysec-known-exploit-dataset
  • Includes real-world exploit samples and traffic data.
• Architecture:
  • Custom Neural Network with attention layers to identify exploit signatures in packet data.
• Metrics:
  • Accuracy
  • F1 Score
  • Precision
  • Recall

Getting Started

Installation

1. Clone the repository: git clone https://huggingface.co/Canstralian/CySec_Known_Exploit_Analyzer
2. Navigate to the directory: cd CySec_Known_Exploit_Analyzer
3. Install the necessary dependencies: pip install -r requirements.txt

Usage

• To analyze a network traffic log: python analyze_exploit.py --input [input-file]
• Example Command: python analyze_exploit.py --input data/sample_log.csv

Model Inference

• Input: Network traffic logs in CSV format
• Output: Classification of potential exploits with confidence scores

License

• This project is licensed under the MIT License.

Datasets

• The model is trained on the cysec-known-exploit-dataset, featuring exploit data from actual network traffic.

Contributing

• Contributions are encouraged! Please refer to CONTRIBUTING.md for details.

Contact

• For inquiries or feedback, please open an issue or contact [email protected].