Abstract
Classic security methods become less effective against the Internet of Things (IoT) cyber-attacks, such as cryptography. An urgent need for real-time and lightweight detection of cyber-attacks is needed to secure IoT networks. This demand is achieved by a reliable and efficient intrusion detection system (IDS) that can meet IoT environments' high scalability and dynamicity. Herein, we analyzed the traffic and features of commonly used and recently published datasets for IoT networks. Furthermore, we proposed an ensemble feature selection method. Also, we studied the effects of traffic heterogeneity levels and time-window size on several classification methods to justify the detection model selection. Regarding the BotNet-IoT dataset, we noticed that few features play a critical role in IDS performance, and larger time-windows were slightly better than the shorter time-windows. Furthermore, we found that PCA classifier performance was significantly affected by traffic heterogeneity. On the other hand, the Boosted Tree showed the best and the most stable performance among all the considered classification methods.
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