NetHawk2 🦅 : Network Detection & Investigation Tool

Sharp, precise, and always watching the network.

🔍 Introduction

Cybersecurity threats are on the rise, and network monitoring is crucial for detecting and preventing malicious activity. This project is a Cyber-Forensics AI-powered tool that monitors live network traffic, detects anomalies using Machine Learning, and provides AI-driven forensic analysis of potential threats.

This tool is designed to help cybersecurity professionals, network administrators, and security enthusiasts analyze network activity in real-time. It features a Java-based GUI for visualization and a Python-powered backend for packet sniffing and threat analysis.

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🛠️ What Happens in the Project?

1. Live Packet Sniffing: Captures network packets using Scapy.
2. Machine Learning Classification: Analyzes packets and classifies them as benign or malicious.
3. Threat Investigation with AI: Uses OpenAI GPT-4 to generate an in-depth forensic report.
4. Real-Time Java GUI: Displays live network activity and allows users to analyze threats with a single click.

This seamless integration of packet analysis, AI-based threat detection, and an interactive GUI makes it a powerful network forensics tool.

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🚀 Tech Stack Used

• Python (Backend & Machine Learning)
  • Flask (API for communication with Java GUI)
  • Scapy (Network packet sniffing)
  • Scikit-learn (Machine Learning for anomaly detection)
  • OpenAI API (For AI-driven threat analysis)
• Java (Frontend GUI)
  • Swing (UI components for packet display & user interaction)
  • HTTP Requests (For fetching data from Python backend)
• Other Tools
  • Pickle (For storing ML models)
  • SQLite (For future data storage & logging capabilities)

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🏗️ How to Run the Project

1️⃣ Install Dependencies

Make sure you have Python 3.8+ and Java 8+ installed.

📌 Install Python Dependencies

Run the following command inside the project directory:

pip install -r requirements.txt

📌 Install Java Dependencies

No additional dependencies are required for Java, as we use built-in Swing components.

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2️⃣ Run the Python Backend

Open a terminal, navigate to the backend folder, and start the packet sniffer:

python backend/sniffer.py

This will start the Flask API and begin capturing network packets.

Then, start the AI-based threat analysis module:

python backend/threat_analysis.py

This enables GPT-4-powered forensic investigation.

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3️⃣ Run the Java GUI

Open a terminal, navigate to the frontend folder, and compile the Java program:

javac frontend/CyberForensicsGUI.java

Then, run the GUI:

java frontend.CyberForensicsGUI

The GUI will now display real-time packet monitoring and allow you to analyze threats.

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📌 Conclusion

This project provides a powerful, AI-enhanced cybersecurity tool for network traffic analysis and forensics. Whether you're a cybersecurity student, network engineer, or security enthusiast, this tool will help you understand how to detect, analyze, and mitigate network threats in real-time.

🔹 Future Improvements:

• WebSocket integration for instant updates 🔄
• Dark mode & better UI enhancements 🎨
• Threat logging & reporting system 📝

Feel free to contribute, improve, or customize this project! 🚀