AI-Powered Intrusion Detection System (AI-PIDS) 🚀 A real-time, AI-driven intrusion detection system leveraging machine learning for high-accuracy threat detection.
📌 Overview AI-PIDS is an advanced Intrusion Detection System (IDS) that utilizes machine learning (XGBoost) to analyze network traffic and detect malicious activity. It processes live packet captures, extracts key network features, and classifies them in real time.
🔹 Key Features: ✅ Real-time Packet Capture & Analysis using pyshark ✅ AI-Powered Threat Detection with XGBoost ✅ Optimized Preprocessing for handling large datasets (3GB+) ✅ Scalable & GPU Compatible (CUDA Support for Faster Execution) ✅ Logging & Alert System to flag potential intrusions
│── data/ # Data folder (CSV files NOT included) │ ├── references.md # Dataset & resource links │── models/ # Trained ML models │── scripts/ # Helper scripts │── src/ # Core AI detection source code │── .gitignore # Ignoring large files (CSV, logs, etc.) │── README.md # Project documentation │── REFERENCES.md # Dataset sources & references │── requirements.txt # Required dependencies
🔹 Step 1: Clone the Repository sh Copy Edit git clone https://github.com/your-username/AI-PIDS.git cd AI-PIDS 🔹 Step 2: Install Dependencies Ensure you have Python 3.8+ installed. Then, run:
sh Copy Edit pip install -r requirements.txt 🔹 Step 3: Ensure Wireshark & TShark are Installed AI-PIDS requires Wireshark and TShark for live network traffic analysis.
🔹 Step 4: Run the AI-PIDS System sh Copy Edit python src/main.py
AI-PIDS uses the CIC-IDS2017 dataset for training. 📄 More details and additional datasets are available in REFERENCES.md.
Main Dataset Used: CIC-IDS2017 (Network Intrusion Detection Dataset)
CSE-CIC-IDS2018 (Latest Version for Advanced Threat Detection)
🛡 Security & Best Practices Run with Proper Permissions to analyze network traffic. Ensure Model is Updated regularly for higher accuracy. Use a Secure Environment to avoid unauthorized access. 🛠 Technologies Used Component Technology Programming Language Python 3.8+ Machine Learning XGBoost Data Processing Pandas, NumPy Network Capture Pyshark (Wireshark & TShark) Deployment Docker (Optional) 📢 Contributing We welcome contributions! You can help by: ✅ Reporting Issues ✅ Suggesting Enhancements ✅ Submitting Pull Requests
📜 License This project is licensed under the MIT License.
✨ Improvements Made ✔ Professional Formatting – Clear sections and markdown styling ✔ Detailed Documentation – Explains every step for new users ✔ Enhanced Readability – Code blocks, icons, and tables for clarity ✔ Security Best Practices – Important considerations before deployment
Let me know if you need further refinements! 🚀