AN IMPROVED GREY WOLF OPTIMIZATION ALGORITHM WITH CHAOTIC MAPPING FOR INTRUSION DETECTION IN NETWORKS

Abstract

Intrusion Detection Systems (IDS) play a vital role in safeguarding computer networks against increasingly sophisticated cyber threats. However, many optimization-based IDS models suffer from an imbalance between exploration and exploitation, leading to premature convergence, poor feature diversity, and high false alarm rates.
This study aims to develop an improved intrusion detection framework by enhancing the Modified Grey Wolf Optimization (MGWO) algorithm with a chaotic mapping to address the exploration-exploitation imbalance and enhance feature selection and detection performance. The proposed Intrusion Detection System integrates the Improved Modified Grey Wolf Optimization (IMGWO) algorithm with a deterministic chaotic position-update mechanism. Information Gain is employed to evaluate feature significance, while MinMax normalization ensures effective data scaling. The optimized feature subsets are used to train the classifier, and experiments were conducted using the UNSW-NB15 benchmark dataset. Performance is evaluated using standard metrics, including accuracy, F1 Score, False Positive Rate (FPR), Classification Error Rate (CER), and G-mean. Experimental results demonstrate that the proposed IMGWO-based IDS significantly outperforms existing approaches. The model achieved an accuracy of 98.07%, an F1-score of 97.51%, an FPR of 1.55%, a CER of 0.97%, and a G-Mean of 97.96%, indicating improved detection capability and reduced false alarms.