IoT Malware Detection and Mitigation in AMQP Simulated Environment

Abstract

Internet of Things(IoT) devices are increasing rapidly and providing an infrastructure to connect and share information. Attackers are targeting the IoT network and sending malicious Traffic to IoT devices. Devices share large volumes of data, so malware is a security issue. IoT devices work in resource-constrained environments, and malware changes the traffic pattern. Due to that, it is a challenging situation to detect malware. An optimized ensemble learning-based approach is applied to detect the malware by analyzing the behavior. Training models employ the NF-BoT-IoT dataset to understand benign and attack traffic patterns. Multiple machine learning algorithms were evaluated on a given dataset, and observed that optimized ensemble learning performs best with an outstanding accuracy of 0.9915 and a precision of 0.9937. Another phase of the model mitigates malware by blocking attack IPs of Traffic flagged as malicious by the detection phase. The model's performance is evaluated in a simulated environment of advanced message queuing protocol(AMQP) using RabbitMQ broker. Future research directions will assist in further research work in enhancing the security of IoT infrastructure.