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This paper introduces XSec, a self-explainable deep learning architecture specifically designed for security applications, addressing the critical need for interpretability in threat detection. By employing a novel mask-based approach during training to extract informative sub-features and learning class-representative prototypes, XSec generates deterministic explanations at test time without relying on post-hoc methods. Evaluated across five security scenarios, XSec achieves an impressive average classification accuracy of 97.33% while significantly reducing explanation latency compared to traditional techniques.
XSec achieves 97.33% accuracy in security applications while providing real-time, interpretable explanations without post-hoc analysis.
Deep learning models have become integral to security applications due to their ability to model complex relationships in data and detect sophisticated threats. However, their complexity makes it difficult to understand how predictions are generated, posing significant challenges for interpretability, particularly in security applications where transparency is critical. Existing explanation methods, such as visual explanation techniques and post-hoc approaches, suffer from several limitations: reduced faithfulness due to local approximation errors, instability caused by reliance on randomness, and computational inefficiency that hinders real-time usage. To address these issues, we introduce XSec, a self-explainable deep architecture developed for security applications. During training, XSec uses a novel mask-based approach to extract informative sub-features from the data and learns prototypes, representative patterns that characterize each class. XSec then leverages the prototypes in a dedicated similarity layer at test time to compute similarity scores and generates interpretable explanations without the need for post-hoc analysis. We evaluate XSec across five diverse security scenarios, demonstrating its ability to achieve an average classification accuracy of 97.33% with minimal performance compromise. XSec produces deterministic explanations for a fixed trained model and input and substantially reduces explanation latency compared with approximation-based and perturbation-based post-hoc methods. Through this effort, we extend the applicability of self-explainable AI to security applications, bridging the gap between deep learning performance and the need for explainability in critical scenarios.
