Abstract
Intrusion detection safeguards computer systems against unauthorized access and malicious activities. Feature selection plays a pivotal role in enhancing the efficiency and effectiveness of intrusion detection algorithms by identifying the most relevant features from vast datasets. In this study, we propose a novel approach to optimize feature selection in intrusion detection algorithms using data mining techniques. We explore various data mining algorithms, including decision trees, genetic algorithms, and particle swarm optimization, to identify the optimal feature subset that maximizes detection accuracy while minimizing computational overhead. Experimental results demonstrate our approach’s efficacy in improving intrusion detection systems’ performance across different datasets, achieving higher detection rates with reduced computational complexity. Our work advances state-of-the-art intrusion detection by leveraging data mining for efficient feature selection.
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