Explainable AI-Enabled Intrusion Detection Systems for Computer Networks

Securing computer networks is critical as cyber threats grow more complex. Machine learning (ML) has advanced intrusion detection systems (IDS) by analyzing network data to detect security breaches. Unlike traditional systems, ML-driven IDS can adapt to new threats but often lack transparency. Explainable AI (XAI) addresses this by providing clear explanations for ML-based IDS decisions, enhancing trust and accountability. This paper surveys some of the recently proposed IDS integrating ML/DL and XAI, highlighting advancements, methodologies, and practical implementations, while evaluating their effectiveness and discussing future research directions. This will be valuable for both seasoned and emerging researchers in the area.