Shypd.aiThe largest open index of AI toolsIntrusion-Detection-System-Using-Machine-Learning is an AI Agents & Automation tool that provides open-source code for developing intrusion detection systems (IDS) using various machine learning algorithms. It supports tree-based, unsupervised, and ensemble learning methods for cybersecurity applications.
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This repository offers open-source code for developing Intrusion Detection Systems (IDS) using a range of machine learning algorithms. It's designed for general IDS and anomaly detection applications, particularly in the context of the Internet of Vehicles (IoV). The project includes implementations of tree-based algorithms like Decision Tree, Random Forest, XGBoost, LightGBM, and CatBoost, as well as unsupervised learning with k-means, and ensemble methods such as stacking and the proposed LCCDE. It also incorporates hyperparameter optimization techniques like Bayesian optimization. The code is accompanied by published research papers detailing three specific IDS models: a tree-based IDS, MTH-IDS (a multi-tiered hybrid IDS), and LCCDE (a decision-based ensemble framework). Datasets like CICIDS2017 and CAN-intrusion are used for experimentation, making it a valuable resource for cybersecurity researchers and developers.
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