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Adaptive Hybrid Metaheuristic Optimization for Enhanced Feature Selection in High-Dimensional IoT Intrusion Detection Systems

Authors: Manoj Kumar Chaturvedi

Published: 2025/5/2

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Abstract

The Internet of Things (IoT) is rapidly expanding, creating numerous opportunities but also exposing critical vulnerabilities. Intrusion Detection Systems (IDSs) are crucial for securing IoT networks, yet their performance is often hampered by the high dimensionality of data generated by IoT devices. This paper proposes an adaptive hybrid metaheuristic optimization algorithm for enhanced feature selection in high-dimensional IoT intrusion detection systems. The algorithm combines the strengths of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) with an adaptive parameter control mechanism to efficiently explore the feature space and identify the most relevant features for accurate intrusion detection. The adaptive parameter control dynamically adjusts the parameters of GA and PSO based on the search progress, preventing premature convergence and improving the overall search efficiency. The proposed approach is evaluated using benchmark IoT intrusion detection datasets and compared with state-of-the-art feature selection methods. The experimental results demonstrate that the proposed algorithm achieves superior performance in terms of detection accuracy, false positive rate, and computational efficiency, making it a promising solution for securing IoT networks against evolving cyber threats.

Keywords

IoT Security, Intrusion Detection Systems, Feature Selection, Metaheuristic Optimization, Hybrid Algorithm, Adaptive Parameter Control, Network Anomaly Detection, High-Dimensional Data, Machine Learning, Cybersecurity

References

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How to Cite

Manoj Kumar Chaturvedi , (2025/5/2). Adaptive Hybrid Metaheuristic Optimization for Enhanced Feature Selection in High-Dimensional IoT Intrusion Detection Systems. JANOLI International Journal of Humanities and Linguistics , Volume UIh3MC5UrwhGKptS6jkQ, Issue 3.

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