Western-OC2-Lab/Intrusion-Detection-System-Using-Machine-Learning
Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)
Implements three distinct IDS architectures: a tree-based supervised learner, a multi-tiered hybrid system combining signature-based and anomaly-based detection with k-means clustering for zero-day attacks, and LCCDE—a decision ensemble selecting class-specific leader models from XGBoost/LightGBM/CatBoost. Targets vehicular networks (CAN-bus, V2X) and general IoT environments, evaluated on Car-Hacking and CICIDS2017 datasets. Integrates Bayesian optimization (TPE and Gaussian Process variants) for hyperparameter tuning across supervised and unsupervised learners.
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Aug 06, 2025
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