Cost-Efficient Hybrid Filter-Based Parameter Selection Scheme for Intrusion Detection System in IoT

Abstract

The rapid growth of Internet of Things (IoT) devices has brought about significant advancements in automation, data collection, and connectivity across various domains. However, this increased interconnectedness also poses substantial security challenges, making IoT networks attractive targets for malicious actors. Intrusion detection systems (IDSs) play a vital role in protecting IoT environments from cyber threats, necessitating the development of sophisticated and effective NIDS solutions. This paper proposes an IDS that addresses the curse of dimensionality by eliminating redundant and highly correlated features, followed by a wrapper-based feature ranking to determine their importance. Additionally, the IDS incorporates cutting-edge image processing techniques to reconstruct data into images, which are further enhanced through a filtering process. Finally, a meta classifier, consisting of three base models, is employed for efficient and accurate intrusion detection. Simulation results using industry-standard datasets demonstrate that the hybrid parameter selection approach significantly reduces computational costs while maintaining reliability. Furthermore, the combination of image transformation and ensemble learning techniques achieves higher detection accuracy, further enhancing the effectiveness of the proposed IDS.