A Comprehensive Review of Feature Selection Techniques for Advanced Intrusion Detection

The increasing interconnectivity of computing systems has heightened the demand for strong cybersecurity measures. Ensuring security is crucial to safeguarding devices and data from unauthorized access, particularly as network intrusions remain a major threat in cyberattacks. Intrusion detection systems (IDS) leverage feature engineering and machine learning to enhance their performance and reliability, enabling them to identify threats and anomalies effectively, thereby protecting network integrity. This study focuses on advancing IDS capabilities through practical feature engineering techniques, aiming to boost accuracy and efficiency in detecting and classifying network attacks. The findings provide valuable insights and conclusions that can guide researchers in designing innovative frameworks for a more robust IDS, with performance improvements driven by effective feature engineering strategies.