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ABSTRACT Wireless sensor networks (WSNs) face significant security challenges because of their resource constraints and exposure to malicious attacks. Traditional intrusion detection systems (IDSs) often suffer from low detection accuracy, high false alarm rates, and long processing times. To address these issues, this paper proposes an enhanced IDS framework based on the arithmetic optimization algorithm (AOA) for feature selection, combined with support vector machine (SVM) for classification. A new cost function is introduced to guide the feature selection process and improve model performance. The approach, named ASIDS, is evaluated on the NSL‐KDD dataset, achieving an accuracy of 96. 65%, a detection rate of 98. 69%, and a false alarm rate of 0. 04% using only 15 features, with a significant reduction in execution time. Comparative results with state‐of‐the‐art methods demonstrate the effectiveness and efficiency of the proposed framework in enhancing intrusion detection in WSNs.
Otair et al. (Wed,) studied this question.