DT-ARO: Decision Tree-Based Artificial Rabbits Optimization to Mitigate IoT Botnet Exploitation

Abstract

The rapid growth of Artificial Intelligence (AI) algorithms has created the opportunity to solve complex problems such as Internet of Things (IoT) botnet attacks. The severity of IoT botnet attacks is a critical challenge for improving the smart IoT environment. Therefore, there is an urgent need to design and implement an efficient detection model to deal with various IoT bot attacks and simultaneously handle issues related to the massive feature space. This paper introduces a wrapper feature selection technique by adapting the Artificial Rabbit Optimization (ARO) algorithm and the Decision Tree (DT) algorithm to detect various types of IoT botnet attacks. During the design of the suggested DT-ARO model, the N-BaIoT datasets were used as a testbed environment. The feature space optimization step was carried out using the ARO algorithm to select only the high-priority features for detecting the IoT botnet attacks. The binary vector technique was used to distinguish the optimal features. The detection engine was performed using the DT algorithm. The conducted experiments have demonstrated the ability of the suggested DT-ARO model to detect various types of IoT botnet attacks, where the accuracy rate was 99.89%. Meanwhile, effectively reducing the feature’s space. In addition, the accomplished results were compared with the latest typical approaches. The DT-ARO model was found to be competitive with these methods and even outperformed them in reducing the feature space.