Network Anomaly Detection

This study explores the efficiency of genetic algorithms for feature selection in network anomaly detection systems, combined with machine learning techniques to enhance detection accuracy. The focus is on identifying optimal feature subsets contributing to high classification accuracy in a network environment dataset. The findings demonstrate significant improvements in detection rates, emphasizing the potential of integrating genetic algorithms with machine learning for network security applications.