---
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
---

# Canstralian/CySec_Known_Exploit_Analyzer

## Overview
The **CySec Known Exploit Analyzer** is a model designed to detect and analyze known cybersecurity exploits. This model was built to assist in identifying vulnerabilities and exploit attempts in network traffic by leveraging machine learning algorithms. It is designed for real-time detection and analysis of potential threats.

## Model Details
- **Type:** Neural Network
- **Input:** Network traffic logs, exploit payloads, or relevant security data
- **Output:** Classification of known exploits, anomaly detection
- **Training Data:** Trained on the **cysec-known-exploit-dataset**, which includes real-world exploit samples and traffic data.
- **Architecture:** Custom Neural Network with attention layers for detecting exploit signatures in packet data.
- **Metrics:** The model was evaluated using accuracy, F1 score, precision, and recall to measure its performance.

## Getting Started

### Installation
To clone the repository and install necessary dependencies:
```bash
git clone https://huggingface.co/Canstralian/CySec_Known_Exploit_Analyzer
cd CySec_Known_Exploit_Analyzer
pip install -r requirements.txt

Usage

To analyze a network traffic log:

python analyze_exploit.py --input [input-file]

Example

# Example command to analyze a sample log
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. See the LICENSE.md file for more details.

Datasets

The model was trained using the cysec-known-exploit-dataset, which consists of exploit data collected from real-world network traffic.

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

Contact

For any questions or feedback, feel free to open an issue or reach out to [distortedprojection@gmail.com].