NETWORK ATTACK DETECTION BY GROUP SEARCH OPTIMIZATION USING CONVOLUTIONAL DEEP LEARNING MODEL

Abstract

Intrusion Detection Systems (IDS) play a vital role in safeguarding modern networks against increasingly complex cyberattacks. However, the presence of high-dimensional, redundant, and noisy features in network traffic data often degrades detection accuracy and scalability. To address this challenge, this paper proposes GSFOIDS (Group Search Based Feature Optimization for Intrusion Detection System), a hybrid framework that integrates Group Search Optimization (GSO) with a Convolutional Neural Network (CNN) for efficient and accurate intrusion detection. Initially, the intrusion dataset is cleaned and preprocessed to remove irrelevant attributes and improve data quality. GSO is then employed to identify an optimal subset of discriminative features using cooperative producer, scrounger, and ranger search strategies. A CNN-based fitness function guides the optimization process by assessing classification accuracy. Experimental results demonstrate that GSFOIDS significantly outperforms existing models across multiple evaluation metrics