IoT Security Using Machine Learning Methods with Features Correlation

Abstract

The Internet of Things (IoT) is an innovative technology that makes our environment smarter, with IoT devices as an integral part of home automation. Smart home systems are becoming increasingly popular as an IoT service in the home that connects via a network. Due to the security weakness of many devices, the malware is targeting IoT devices. After being infected with malicious attacks on smart devices, they act like bots that the intruders can control. Machine learning methods can assist in improving the attack detection process for these devices. However, the irrelevant features raise the computation time as well as affect the detection accuracy in the processing with many features. We proposed a machine learning-based IoT security framework using feature correlation. The feature extraction scheme, one-hot feature encoding, correlation feature selection, and attack detection implement an active detection mechanism. The results show that the implemented framework is not only for effective detection but also for lightweight performance. The proposed system outperforms the results with the selected features, which have almost 100% detection accuracy. It is also approved that the proposed system using CART is more suitable in terms of processing time and detection accuracy.