AI-Powered Hybrid Network Intrusion Detection System: Real-Time Traffic Monitoring and Intelligent Threat Identification

Title: AI-Powered Hybrid Network Intrusion Detection System: Real-Time Traffic Monitoring and Intelligent Threat Identification

Original Author: Muhammad Abdullah, Faizan Ali Source: GitHub (Project Link: https://github.com/abdullahhussain706/ai-powered-nids) Publication Time: 2026-06-11

Core Overview: This project proposes a hybrid AI-powered NIDS integrating signature detection and anomaly detection, supporting real-time traffic analysis, machine learning-based anomaly identification, and a desktop monitoring panel. It aims to balance known threat detection and unknown attack discovery, providing a practical security solution for small and medium-sized network environments.

In the digital age, network threats are complex and ever-changing. Traditional NIDS face a dilemma: signature detection accurately identifies known attacks but is powerless against unknown ones; anomaly detection can discover new attacks but is prone to false positives. How to balance the two to build a high-precision system is the core challenge, and this hybrid NIDS is an innovative solution to this problem.

The core innovation is the hybrid detection engine:

1. Signature Detection Module: Based on a predefined rule base, it identifies known attacks such as SQL injection, XSS, and port scanning;
2. Anomaly Detection Module: Learns normal traffic patterns through machine learning to identify unknown threats like zero-day attacks;
3. Fusion Decision Mechanism: Intelligently combines the results of the two modules to maintain a high detection rate while reducing the false positive rate.

Tech Stack: Python as the core, integrating Scapy (packet capture and analysis), Scikit-learn (machine learning), SQLite (storage), PyQt/Tkinter (desktop interface). System Module Architecture: nids-desktop/ ├── core/ (Detection engine core) ├── ml/ (Machine learning pipeline) ├── ui/ (Desktop UI) ├── services/ (Background services) ├── rules/ (Signature rule base) ├── data/ (Packet dataset) ├── database/ (SQLite database) ├── utils/ (Utility functions) └── run.py (Main entry point)

Real-Time Monitoring: The desktop panel displays traffic capture status, threat event list, attack statistical distribution, and system performance metrics. Alerts and Logs: When suspicious activity is detected, an alert is generated immediately and recorded in the SQLite database, including information such as timestamp, attack type, source/destination IP, and severity, facilitating analysis and forensics.

Applicable Scenarios:

1. University lab network monitoring;
2. Small and medium-sized enterprise cybersecurity;
3. Cybersecurity teaching demonstration. Deployment Advantages:

• Modular architecture, easy to extend and maintain;
• Lightweight design, low hardware requirements;
• Open-source and free, reducing costs;
• Customizable rules and algorithms.

Future Plans: Short-term: Web-based monitoring panel, introduction of deep learning models, cloud deployment support; Long-term: Distributed IDS architecture, threat intelligence integration, automated response. Summary: This project provides a practical and scalable security solution for small and medium-sized networks, balancing detection capability and false positive control. It is suitable for learners, IT administrators, and developers for research and reference.