Visually Analyze SHAP Plots to Diagnose Misclassifications in ML-based Intrusion Detection

SHAP visualization can help security analysts identify when AI-based intrusion detection systems make mistakes: The paper demonstrates that visual analysis of SHAP (SHapley Additive exPlanations) plots enables analysts to spot false positives and false negatives—two critical failure modes in cybersecurity systems that can lead to either missed threats or wasted resources investigating non-threats.

Explainability is essential for trustworthy security decisions: By making the reasoning behind AI predictions transparent through visual explanations, the approach helps security analysts understand why the system flagged or missed a threat, rather than blindly trusting a "black box" model's output, which is crucial for responsible deployment in high-stakes security contexts.

Visual tools can bridge the gap between AI capabilities and human decision-making: The paper shows that overlapping SHAP plots provide "clear guidance for analysts on how to use the visual analysis approach for reliable course-of-actions," suggesting that explainability tools work best when designed to support human judgment rather than replace it.

This approach addresses a real operational challenge in cybersecurity: False positives and false negatives in intrusion detection systems complicate analysts' work and can undermine confidence in AI-assisted security tools, making the ability to diagnose these errors practically important for organizations deploying machine learning in security operations.