Machine Learning-Based Intrusion Detection System: Safeguarding Network Security with Random Forests and Neural Networks

This article introduces an open-source machine learning intrusion detection system (IDS) that integrates random forest, decision tree, and neural network models to real-time identify malicious traffic such as DoS attacks, probe attacks, remote-to-local (R2L), and local privilege escalation (U2R). The project aims to address the limitations of traditional rule-based IDS in handling new attack types, providing an intelligent solution for network security protection, suitable for enterprise defense, research testing, and teaching scenarios.

In the digital age, network threats are becoming increasingly complex, and traditional rule-based IDS struggle to handle new attack patterns. The rise of machine learning technology has brought revolutionary changes to network security, enabling automatic learning of attack features for accurate identification. This project is maintained by kashish2012 and published on GitHub (repository name: ML-Based-Intrusion-Detection-System, link: https://github.com/kashish2012/ML-Based-Intrusion-Detection-System) on June 12, 2026.

Core Models

1. Random Forest: Integrates multiple decision trees, balances accuracy and anti-overfitting capabilities, suitable for processing high-dimensional traffic features;
2. Decision Tree: Provides interpretable classification rules, facilitating security analysis and compliance audits;
3. Neural Network: Uses nonlinear modeling to capture complex patterns, with strong generalization ability for new variant attacks.

Supported Attack Types

• "DoS": Exhausts target resources to prevent legitimate access;
• "Probe": Scans port services to collect vulnerability information;
• "R2L": Gains unauthorized local access remotely;
• "U2R": Escalates local privileges to administrator level.

Real-Time Detection

The system captures network interface data via packet sniffing, extracts features such as protocol type, connection duration, and data transfer volume, inputs them into the model for real-time prediction, and can issue alerts immediately to shorten response time.

Application Scenarios

• Enterprises: Deploy at key nodes to complement traditional firewalls;
• Researchers: Provide an experimental framework to test algorithm performance on different datasets;
• Education: Serve as a teaching case for network security and machine learning.

The system implementation involves:

1. Data Preprocessing: Cleaning, normalization, feature encoding;
2. Feature Engineering: Extract statistical and temporal features;
3. Model Training and Validation: Ensure model performance;
4. Handling Imbalanced Data: Since normal traffic is far more than attack traffic, sampling strategies or loss function adjustment are needed.

As attack methods evolve, machine learning-based IDS has become an important part of network security protection. This project demonstrates the combination of classic ML algorithms and actual security needs, providing valuable references for building intelligent and reliable security lines, and helping the intelligent transformation of network security.