This document discusses developing a network forensic mechanism for detecting botnet activities in IoT networks using machine learning techniques. It proposes using four machine learning classification algorithms (Decision Tree C4.5, Association Rule Mining, Artificial Neural Network, Naive Bayes) trained on network flow identifiers to detect botnet attacks in IoT networks. The algorithms would classify network traffic as legitimate or anomalous and link identified attacks to their origins to track botnet activities. A review of related work found existing network forensic tools rely on signature-based approaches that struggle to detect new botnet methods, while previous machine learning studies still produced high false alarm rates and could not fully track botnet paths. The proposed approach aims to more effectively and efficiently detect bot

Towards Developing Network Forensic Mechanism For Botnet Activities in The Iot Based On Machine Learning Techniques PDF

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