Bot Detection in an IoT Environment using Machine Learning algorithms and Network Traffic Analysis

Abstract

Internet of Things (IoT) is one of the fastest expanding technologies today. The number of IoT-connected devices will reach 24.1 billion by 2030. These devices are used everywhere throughout the globe. However, IoTs are being an attractive target to adversaries attacks due to their poor security standards. One famous enemy for IoT devices is Botnet malware. The Botnet aims for weak devices of IoT and compromises them in order to initialize malicious activities. Unfortunately, the infected devices do not show any compromise indicators, making it very challenging to detect their existence. Therefore, this research proposes a novel approach for detecting IoT’s Botnet attack using Machine learning algorithms. The tool uses Network-based traffic, with three ML algorithms: Random Forest, Naive Bayes, and Decision Tree. The tool can convert raw PCAP files to CSV with the same structure as the used dataset to quickly detect any IoT network traffic file. It uses a unique approach that uses eight models (a model is assigned for each attack). This approach allows the tool to be very accurate and fast regarding detection. The proposed tool was tested and evaluated, and the results show that the approach is very accurate in detecting botnet attacks in IoT traffic.