Improved Crow Search-Based Feature Selection and Ensemble Learning for IoT Intrusion Detection

Network intrusion detection in the Internet of Things (IoT) framework has posed considerable challenges in recent decades. A wide variety of machine-learning approaches are introduced in network intrusion detection. The existing methodologies commonly lack consistency in achieving optimal performance across various multi-class categorization tasks. The present study elucidates implementing a unique intrusion system with the primary objective of enriching the efficacy of network intrusion detection. In the initial phase, it is imperative to employ data-denoising methodologies to effectively tackle the issue of data imbalance. In the next step, the enhanced Crow search algorithm is used to determine the most significant features that aid in better classifying intrusion attacks. In the final phase, the ensemble classifier takes the selected features as input to categorize the standard and invader labels. The present work introduces an ensemble mechanism that comprises four distinct classifiers. The assessment of the proposed approach is validated on two denoised datasets, specifically NSL-KDD and UNSW-NB15. The experimental outcomes demonstrate that the formulated approach achieves exceptional accuracy of 99.4% and 99.2% for the NSL-KDD and UNSW-NB15 datasets, respectively.