Investigate Unsolicited Traffic on IoT Devices Using Machine Learning

The significant growth and use of IoT devices, especially in smart homes, increase the risk of cyber-attacks compromising these devices and their services, resulting in data breaches and privacy invasion. Machine learning technology has been considered a sufficient security solution in IoT due to its effectiveness in detecting unsolicited traffic on an IoT network, ensuring the confidentiality, integrity, and availability of IoT devices, and, most importantly, protecting the users’ privacy. In this research, different classification-based machine learning algorithms were leveraged to detect and classify different types of network traffic on the IoT network. At the same time, the effectiveness of these classification machine learning algorithms, Decision Tree (J48), Bayes Net, and Naive Bayes were conducted on our smart home dataset. Before the implementation of this algorithm, a man-in-the-middle attack was introduced on the IoT network while the network traffic was equally captured in the process. Overall, the algorithms successfully evaluated the captured network traffic, detected the introduced MITM attack on IoT devices, and classified the traffic into solicited and unsolicited traffic. According to the findings, the Naive Bayes algorithm outperformed the others with an accuracy of 99.95%.