Novel IDS Framework: Integrating GWO Feature Selection with KAN Classification

The past few decades have seen significant lifestyle improvements through cutting-edge innovations like AI and fog computing enabled by the World Wide Web, which are crucial for practical applications involving both symmetrical and asymmetrical information distributions. However, these advancements also come with the risk off requent and dangerous cyber-attacks. To mitigate these unwanted malwares, researchers must develop and implement novel intrusion detection systems (IDSs). Challenges such as inadequate accuracy scores due to numerous unnecessary and ineffective features, poor identification of attacks through specific deep learning classification methods, the costly and inefficient use of labeled training databases, and the excessive time required for system development and evaluation hinder there liability and efficiency of IDSs.This paper proposes a hybrid approach that combines the Grey Wolf Optimizer (GWO) for feature selection and the Kolmogorov-Arnold Network(KAN) algorithm for classification to address these challenges.The GWO is utilized to extract important features through meta-heuristic methods, demonstrating its effectiveness in feature selection. The proposed model achieves a high accuracy score, precision, recall, and F1-score of 92.09%, indicating its capability to accurately identify both normal and intrusive activities. The Matthews Correlation Coefficient (MCC) of 0.9019 further validates the model's robustness.