In this project, I developed a machine learning model to detect intrusion in IoT networks using the botnet dataset and the ToN dataset.
The Botnet dataset helps us detect the following classes: DDoS, DoS, Reconnaissance, Normal & Theft. The Ton dataset helps us detect the following classes: normal, scanning, ransomware, backdoor, ddos, xss, password, injection, dos, & mitm.
The IoT_ML_with_Bot_Dataset.ipynb notebook shows the training of several machine learning algorithms with the Botnet dataset.
The IoT_ML_with_Ton_Dataset.ipynb notebook shows the training of several machine learning algorithms with the Ton dataset.
The IoT_ML_with_Hybrid_model.ipynb shows the training of a Hybrid machine learning with the Ton dataset.
The IoT_ML_explain_with_LIME_Bot_dataset.ipynb shows the training of several machine learning algorithms with the Botnet dataset. It also uses explainable AI (LIME) to explain the decisions of the machine learning model.