Enhanced Intrusion Detection System Using Machine Learning

Abstract: The evolving cybersecurity landscape demands intrusion detection systems capable of identifying diverse attack patterns across network and application layers. This study addresses limitations in current benchmark datasets by enhancing the CICIDS-2017 dataset through systematic incorporation of multiple attack variants, including web-based threats like Cross-Site Scripting alongside its existing network attack profiles. Our methodology combines realistic attack simulation with rigorous feature engineering to maintain dataset integrity while expanding its threat coverage.We train and evaluate multiple algorithms, selecting the most effective approach based on comprehensive evaluation metrics. The resulting model demonstrates strong capabilities in identifying both traditional network intrusions and contemporary attack patterns. Particular attention is given to maintaining low false positive rates while ensuring broad threat coverage.