metadata
model_name: Canstralian/CySec_Known_Exploit_Analyzer
tags:
- cybersecurity
- exploit-detection
- network-security
- machine-learning
license: mit
datasets:
- cysec-known-exploit-dataset
metrics:
- accuracy
- f1
- precision
- recall
library_name: transformers
language:
- en
model_type: neural-network
base_model:
- replit/replit-code-v1_5-3b
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
- Clone the repository:
git clone https://huggingface.co/Canstralian/CySec_Known_Exploit_Analyzer - Navigate to the directory:
cd CySec_Known_Exploit_Analyzer - 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].