A Modified Multi-objective Particle Swarm Optimizer-Based Lévy Flight: An Approach Toward Intrusion Detection in Internet of Things

Abstract

The emerging of the Internet of things (IoT), and more, the advent of the Internet of everything have revolutionized the computer networks industry. The high diversity of IoT devices, its protocols and standards, and its limited computational resources have led to the appearance of novel security challenges. Hence, the traditional security countermeasures of encryption and authentication are insufficient. Promoting the network security is a fundamental concern for practitioners for safeguarding their economical and industrial strategies. Intrusion detection systems (IDSs) are the major solutions for protecting Internet-connected frameworks at the network-level. But, more importantly, is how to convert the traditional IDSs into intelligent IDSs that resemble the intelligent IoT. This paper presents a new approach for converting the traditional IDSs into smart, evolutionary, and multi-objective IDSs for IoT networks. Moreover, this article presents a modified algorithm for IDSs that tackles the problem of feature selection. The modified algorithm stands on the integration of multi-objective particle swarm optimization with Levy flight randomization component (MOPSO-Levy); the modified MOPSO-Levy has been tested on real IoT network data that is drawn from UCI repository. MOPSO-Levy has achieved superior performance results when compared with state-of-the-art evolutionary multi-objective algorithms.