AI-Driven Network Intrusion Detection System Based on Python and Machine Learning

Project Basic Information

• Original Author/Maintainer: Dhanushreeushetty
• Source Platform: GitHub
• Original Title: AI-Driven-Network-Intrusion-Detection-System
• Original Link: https://github.com/Dhanushreeushetty/AI-Driven-Network-Intrusion-Detection-System
• Release Date: 2026-06-02

Core Overview

This project is an AI-driven Network Intrusion Detection System (NIDS) combining Python programming and machine learning technology. It supports PCAP packet analysis, anomaly detection, brute-force attack and DoS attack simulation, and can generate CSV reports and visual charts. It aims to address new network threats that traditional rule-based IDS struggle to handle.

In today's digital age, network security has become a core issue that enterprises and organizations cannot ignore. As network attack methods become increasingly complex, traditional rule-based Intrusion Detection Systems (IDS) are no longer able to cope with new threats. This project presents a modern solution that can automatically analyze network traffic and identify suspicious activities, providing a new technical path for network security protection.

Main Functions

1. PCAP Packet Analysis: Reads PCAP files generated by tools like Wireshark, extracting key features such as IP addresses, ports, protocols, and packet sizes.
2. Anomaly Traffic Detection: Uses IP-based packet counting to identify abnormal patterns of large numbers of packets sent in a short time (e.g., DDoS precursors).
3. Port Scan Detection: Analyzes connection attempts from a single source IP to multiple destination ports to identify scanning behavior.
4. AI Anomaly Detection: Adopts the Isolation Forest algorithm, which can efficiently detect anomalies without labeled data and has interpretability.
5. Alerts and Reports: Generates structured alerts containing timestamps, attack types, and source IPs, and outputs reports like network_features.csv and ids_results.csv.
6. Data Visualization: Uses Matplotlib to generate traffic analysis charts, assisting in anomaly identification and trend tracking.

Tech Stack

• Scapy: Used for PCAP file parsing and packet operations
• Pandas: Data processing and feature engineering
• Matplotlib: Traffic visualization
• Scikit-Learn: Provides the Isolation Forest algorithm

Project Structure

• project.py: Core logic of the main intrusion detection system
• bruteforce.py: Brute-force attack simulation script
• dos.py: DoS attack simulation script
• requirements.txt: List of dependency libraries

The project provides bruteforce.py and dos.py attack simulation scripts, which can be used in controlled environments for:

1. Cybersecurity education: Observing attack behaviors and system responses
2. System testing: Verifying the accuracy and false positive rate of the IDS
3. Rule tuning: Optimizing detection thresholds and feature selection
4. Incident response drills: Training team response capabilities

Note: The scripts can only be used in authorized test environments and must comply with legal and ethical norms.

1. Real-time Network Monitoring: Extend from offline PCAP analysis to real-time network interface monitoring
2. Streamlit Dashboard: Build an interactive web interface to support real-time monitoring and alert management
3. Deep Learning Integration: Introduce models like LSTM autoencoders and Transformers to improve the detection capability for complex attacks
4. Automated Alert Notifications: Support multi-channel notification mechanisms such as email, Slack, and Webhook

Applicable Scenarios

• Academic research: Teaching example for network security courses
• Small businesses: Basic network monitoring solution with limited budget
• Penetration testing: Assisting red teams to verify blue team detection capabilities
• Proof of concept: Enterprises evaluating the feasibility of AI-driven cybersecurity solutions

Practical Recommendations

• Verify in an isolated test environment first before deploying to production
• Regularly update training data to adapt to new attack types
• Combine with other security tools to form multi-layered defense
• Establish clear alert response processes and responsibility division

This project demonstrates the practical application of machine learning technology in the field of cybersecurity. By using the Isolation Forest algorithm to implement unsupervised anomaly detection, it lowers the deployment threshold. The complete analysis workflow, attack simulation functions, and visual outputs make it an excellent starting point for learning and practicing AI applications in network security. As network threats evolve, AI-driven IDS will play a more important role, and this open-source project provides a practical reference implementation for developers and cybersecurity professionals.