A Machine Learning Based Framework for Enhancing Intrusion Detection and Prevention Systems

An Intrusion Detection and Prevention System (IDPS) is a sophisticated, integrated solution that monitors network traffic for suspicious activities and unauthorized access, promptly notifying administrators and taking preemptive action against potential threats. IDPS technologies are crucial for maintaining network security and protecting sensitive data. In today's interconnected digital landscape, cybersecurity remains a primary concern. Traditional IDPS approaches, including signature-based and anomaly-based detection methods, effectively safeguard networks against malicious activities. However, the evolving nature of cyber threats necessitates enhancing these systems with cutting-edge technologies like machine learning (ML). These advancements improve intrusion detection by continuously learning from network behavior and adapting to emerging threats. This paper explores the integration of ML techniques into IDPS to enhance overall cybersecurity resilience, reduce false positives, and improve threat detection accuracy. The objective is to enhance IDPS's ability to identify and neutralize sophisticated cyber threats while addressing the limitations of rule-based systems. For achieving this, a new framework called Adaptive Machine Learning-Enhanced IDPS (AML-IDPS) is proposed, incorporating new components such as multi-layer threat detection and explainability into existing structures and validated framework using the CICIDS2017 dataset for effectiveness.