Unsupervised Anomaly Based Botnet Detection in IoT Networks

Anomaly-based detection of the IoT botnets with emphasis on feature selection is elaborated in this paper. Due to the rapid growth of the Internet of Things technology, the number of vulnerable devices that become a part of a botnet has grown significantly. The detection of such malicious traffic is essential for taking timely countermeasures. While the idea of anomaly-based attack detection is not new and has been extensively studied, much less attention has been paid to dimensionality reduction in learning models induced for IoT networks. In this paper, we showed that it is possible to induce high accurate unsupervised learning models with reduced feature set sizes, which enables to decrease the required computational resources. Training one common model for all IoT devices, instead of dedicated model for each device, is another design option that is evaluated for resource optimization.