---
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](#datasets)
  - 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](LICENSE.md).

## 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 [distortedprojection@gmail.com](mailto:distortedprojection@gmail.com).