Efficient traffic-based IoT device identification using a feature selection approach with Lévy flight-based sine chaotic sub-swarm binary honey badger algorithm

Abstract

Internet of Things (IoT) refers to the various devices connected to the Internet, enabling them to communicate and transmit data with each other. The rapid development of the IoT also brings security and other problems in cyberspace. In this case, device identification is a crucial tool for IoT security issues, which can detect and prevent cyber-attacks. However, device identification has some challenges on IoT traffic datasets, such as large-scale and high-dimensional sparse datasets, features prone to security vulnerabilities, and identification of IP and non-IP devices, which affects the performance of classifiers. Feature selection can be seen as an effective data preprocessing technique in IoT device identification, which may improve the performance of classification and reduce the computational complexity of IoT device identification. In this paper, we propose a traffic-based IoT device identification model using a novel wrapper feature selection approach based on an improved and efficient method, which we call Lévy flight-based sine chaotic sub-swarm binary honey badger algorithm (LS2-BHBA). Specifically, four improved factors are employed in LS2-BHBA to expand the search scope, balance the exploration and exploitation phases, and enhance the search capability. In addition, a binary mechanism is implemented to enhance the suitability of the proposed LS2-BHBA for feature selection in IoT device identification. The experimental results on several real IoT traffic datasets denote that LS2-BHBA can reduce the number of features to 10%, and the classification accuracy can reach 98%, which outperforms some classical and latest comparison algorithms in the feature selection of IoT device identification.