Evaluating Explanation Quality in X-IDS Using Feature Alignment Metrics

Current XAI evaluation methods fall short for security applications: The paper identifies that existing explainability metrics focus on technical properties like fidelity and simplicity, but fail to measure whether explanations are actually meaningful and useful to security professionals who need to act on them.

Domain knowledge should be the standard for evaluating explanations: The researchers propose new metrics that measure how well AI explanations align with established security knowledge bases. This approach ensures explanations reflect real-world expertise rather than just being technically sound, making them more trustworthy and actionable for security analysts.

Different AI systems and attack types produce varying explanation quality: The experimental results demonstrate that the proposed metrics reveal meaningful differences in explanation quality across different X-IDS systems and different types of cyberattacks, suggesting that one-size-fits-all evaluation approaches are insufficient.

Better evaluation metrics can improve practical AI deployment in security: By providing security teams with metrics that assess how well explanations reflect known domain knowledge, organizations can make more informed decisions about which AI systems to trust and deploy in real cybersecurity operations.

Explainability evaluation requires collaboration between AI developers and domain experts: The paper's approach of grounding explanation quality in domain-specific knowledge bases highlights that responsible AI in high-stakes fields like cybersecurity requires input from both technical AI researchers and security professionals.