An Anomaly-Based Intrusion Detection System (AIDS) built with a Random Forest classifier on the CICIOT23 dataset. This project automates the full ML pipeline to detect anomalous IoT network traffic with 99.76% accuracy.
SecureIoT–AI evaluates how DNNs and CNNs generalize to unseen IoT cyber-attacks under severe class imbalance using GAN-based augmentation, SMOTE, and class weighting.
Intrusion Detection in CICIoT2023 using Logistic Regression, Adaboost Classifier, Gradient Boosting And MLP Model
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