A Self-Explainable Deep Architecture for Security Applications

Built-in interpretability improves security decision-making: XSec achieves 97.33% average classification accuracy while providing explanations directly from the model itself, rather than relying on post-hoc analysis. This means security professionals can understand why a threat was detected at the moment of detection, which is critical when decisions have real consequences.

Existing explanation methods have significant reliability problems: The paper identifies that current approaches like visual explanations and post-hoc techniques suffer from "reduced faithfulness due to local approximation errors" and "instability caused by reliance on randomness." This suggests that explanations from standard AI systems may not accurately reflect what the model actually learned, a serious concern in security contexts.

Speed matters for real-world security applications: XSec "substantially reduces explanation latency" compared to other methods, addressing a practical barrier to deploying explainable AI in security. If generating explanations takes too long, security teams cannot use them in real-time threat detection scenarios.

Prototype-based reasoning is more trustworthy than black-box decisions: By learning representative patterns (prototypes) for each threat class during training, XSec produces "deterministic explanations"—the same explanation every time for the same input. This consistency is essential for security applications where reproducibility and auditability are regulatory and operational requirements.

Interpretability doesn't require sacrificing accuracy: The minimal performance compromise demonstrates that making AI systems explainable doesn't mean accepting weaker security detection, addressing a common concern that transparency comes at the cost of effectiveness.