Safeguarding against external intrusions utilizing adaptive bio-inspired multi-population anomaly detection for IoT network

Abstract

The rapid growth of Internet of Things (IoT) devices has dramatically increased demand for robust, adaptive security solutions capable of countering the growing sophistication of cyberattacks. Despite extensive research efforts focused on anomaly-based intrusion detection systems tailored to IoT network traffic, conventional detection frameworks often fail to effectively identify novel or zero-day attack patterns, thereby falling short of the dynamic security requirements of modern IoT ecosystems. To address these critical limitations, this study introduces a novel anomaly-based intrusion detection system called Chaotic Multi-Population Grasshopper Optimization with Differential Evolution (CMGODE). The proposed approach significantly enhances the standard Grasshopper Optimization Algorithm by integrating chaotic mapping mechanisms to improve exploitation and prevent premature convergence, adopting a multi-population strategy to maintain diversity and enhance global search, and incorporating a differential evolution-based refinement phase to improve the quality of global candidate solutions. The effectiveness of the CMGODE-based detection system is thoroughly evaluated on two widely adopted benchmark datasets, namely BoT-IoT and UNSW-NB15. Experimental results demonstrated that our proposed method achieved an excellent balance between high detection accuracy and computational efficiency, consistently outperforming several state-of-the-art approaches in accurately identifying both known and previously unseen attacks within IoT network environments.