This paper introduces a novel metric, the Explanability Fragility Score, to quantify instability in AI explanations within cybersecurity intrusion detection systems. The research demonstrates that multicollinearity, a statistical issue with correlated features, can significantly inflate explanation variance and make feature importances non-identifiable. To address this, the paper proposes two mitigation methods, CAA-Filtering and SHARP, aimed at stabilizing AI explanations and improving trustworthiness in security-critical applications. AI
IMPACT Introduces methods to improve the trustworthiness and reproducibility of AI explanations in security-critical systems.
RANK_REASON The cluster contains an academic paper detailing a new metric and mitigation methods for AI explainability.
- AI
- CAA-Filtering
- Dr Anna Lito Michala PhD
- explainability
- intrusion detection systems
- multicollinearity
- SHAP
- SHARP
- UNSW-NB15
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