Cyberthreat Hunting Using LLM

This project presents a comprehensive approach to network security analysis using Large Language Models (LLMs). The system leverages both fine-tuning techniques and prompting strategies to detect and analyze network threats, providing detailed reports and recommendations for security professionals.

Project Overview

The system utilizes state-of-the-art Large Language Models to analyze network traffic data, identify potential security threats, and provide detailed analysis and recommendations. It employs multiple approaches including:

• Fine-tuning: Custom model adaptations for specialized network security tasks
• Few-shot prompting: Using limited examples to guide the model
• Zero-shot prompting: Leveraging the model's inherent capabilities without examples

The project includes both the AI models and a user-friendly web interface for security analysts.

Key Features

• Real-time network traffic analysis
• Detection of common attack patterns (DDoS, SQL injection, etc.)
• Detailed analysis report generation
• Comparison of different LLM approaches for security analysis
• User-friendly web interface for monitoring and analysis

Model Approaches

Fine-tuning Approach

Custom fine-tuning of various LLM models (Mistral, Llama 3) for specialized network security analysis tasks.

Prompting Techniques

• Few-shot prompting: Using limited examples to guide the model for specific security analysis tasks
• Zero-shot prompting: Leveraging the model's inherent capabilities without examples

Performance Comparison

The chart above shows the comparison of detection accuracy between different approaches. Fine-tuning consistently outperforms prompting techniques across various attack types.

Detailed comparison between fine-tuning and prompting techniques across multiple metrics.

User Interface

The system provides a comprehensive web interface for security analysts to:

• Monitor network traffic in real-time
• View detected threats and their analysis
• Get detailed recommendations for security actions

Example Outputs

Analysis Response

Example of the system's analysis for a detected threat.

DDoS Attack Detection

Detailed analysis of a DDoS attack detection with preventive measures.

Graphical Analysis

Visual representation of attack patterns and security metrics.

Prompt Engineering

The system employs carefully crafted prompts to guide the LLM analysis process:

Example of a prompt template used for network security analysis.

Project Structure

├── Code/
│   ├── models/
│   │   ├── fine-tune/    - Fine-tuned models (Mistral, Llama 3)
│   │   ├── few-shots/    - Few-shot prompting implementations
│   │   └── zero-shot/    - Zero-shot prompting implementations
│   └── frontend-backend/
│       ├── backend/      - API and server implementation
│       └── mini-pro/     - Web frontend interface
├── Research Paper/       - IEEE research publication
├── Report/               - Project documentation
└── Video Implementation/ - Demo videos

Technologies Used

• AI Models: Mistral, Llama 3.2, Llama 3.1
• Backend: Node.js, Python
• Frontend: React, Next.js
• Data Processing: PyTorch, TensorFlow

Research Publication

This project has been documented in an IEEE research paper & project report that details the methodology, implementation, and comparative analysis of different LLM approaches for network security.