Explaining Network Intrusion Detection System Using Explainable AI Framework

AI transparency is critical for cybersecurity deployment: The paper demonstrates that while deep learning models improve intrusion detection accuracy, their complexity makes them unsuitable for real-world use without explanation. Organizations need to understand why a system flagged a potential attack before acting on that alert.

Explainable AI methods can identify which network features matter most: By applying techniques like SHAP and LIME to intrusion detection, the research shows that AI can pinpoint specific measurable factors that influence cyberattack predictions, helping security teams understand what patterns the system is actually detecting.

Multiple explanation approaches provide different insights: The paper tests five different explainability methods (SHAP, LIME, Contrastive Explanations Method, ProtoDash, and Boolean Decision Rules), suggesting that no single approach captures everything—organizations may benefit from using complementary techniques to build confidence in AI security decisions.

Explainability must be built into the entire ML pipeline, not added afterward: Rather than treating interpretability as an afterthought, the research emphasizes integrating transparency "at every stage" of development, which helps catch potential issues earlier and builds more trustworthy systems from the ground up.