Botnets Attack Detection Using Machine Learning Approach for IoT Environment

Abstract

Abstract The era of the Internet of Things (IoT) is very rapidly developing with millions of devices that are useful in the smart home, smart city, and many other smart systems for education, organization and so on. On the other side, attackers are mostly targeting these devices. After infecting the malware attacks on these devices, they become bots that are controlled by attackers, and these will be targeted to the organizations not only for stealing important information but also for breaking down the network. Although some security mechanisms have developed to protect against cyber-attacks, most such systems are rule-based systems, like public IDS systems. And also, the formal rule-based detection could be circumvented by the malware attackers’ knowledge. Therefore, the machine learning-based detection scheme is the replacement for the lack of previous detection techniques. The proposed detection architecture is based on machine learning methodology, like the CART algorithm and public IDS dataset, named N-BaIoT. The experimental results indicate that the detection accuracy of the selected classifier, CART is significantly better than that of the Naïve Bayes classifier, and the overall detection rate using CART is reached up to 99%.