Trusting Machine Learning Algorithms in Predicting Malicious Nodes Attacks

Abstract

Identifying network attacks is a very crucial task for Internet of things (IoT) security. The increasing amount of IoT devices is creating a massive amount of data and opening new security vulnerabilities that malicious users can exploit to gain access. Recently, the research community in IoT Security has been using a data- driven approach to detect anomaly, intrusion, and cyber-attacks. However, getting accurate IoT attack data is time-consuming and expensive. On the other hand, evaluating complex security systems requires costly and sophisticated modeling practices with expert security professionals. Thus, we have used simulated datasets to create different possible scenarios for IoT data labeled with malicious and non-malicious nodes. For each scenario, we tested off a shelf machine learning algorithm for malicious node detection. Experiments on the scenarios demonstrate the benefits of the simulated datasets to assess the performance of the ML algorithms.