This project is an Intrusion Detection System (IDS) using machine learning (ML) and deep learning (DL) to detect network intrusions. It leverages the CICIDS2018 dataset to classify traffic as normal or malicious. Key features include data preprocessing, model training, hyperparameter tuning, and Docker containerization for scalable deployment.

Code for preprocessing, feature engineering, and training six classifiers on CICIDS2017 and 2018. Implements static and adaptive ensembles, including confidence-based weighting and meta-learning, to boost intrusion detection accuracy and robustness.