AI 中文总结
本文提出面向安全应用的可自解释深度架构XSec，通过训练时提取子特征、学习原型及测试时相似度层生成解释，在五类安全场景中实现97.33%平均准确率，且解释延迟低、结果确定，填补了深度学习性能与安全场景可解释性的缺口。
AI 中文摘要
深度学习模型因具备对数据中复杂关系进行建模及检测复杂威胁的能力，已成为安全应用中不可或缺的组成部分。然而，其复杂性使得预测生成过程难以被理解，这对可解释性构成了重大挑战，尤其在透明度至关重要的安全应用场景中。现有解释方法，如视觉解释技术和事后解释方法，存在诸多局限：因局部近似误差导致的忠实度降低、因依赖随机性引发的不稳定性，以及阻碍实时使用的计算效率低下问题。为解决这些问题，我们提出XSec，一种专为安全应用开发的可自解释深度架构。在训练过程中，XSec采用新颖的基于掩码的方法从数据中提取有信息的子特征，并学习原型——表征每个类别的代表性模式。在测试阶段，XSec会在专用的相似度层中利用这些原型计算相似度得分，无需事后分析即可生成可解释的解释。我们在五个不同的安全场景中对XSec进行评估，结果表明，XSec在仅产生极小性能折损的情况下，平均分类准确率达到97.33%。对于固定的训练模型和输入，XSec可生成确定性的解释，且与基于近似和基于扰动的事后方法相比，能大幅降低解释延迟。通过这项工作，我们将可自解释AI的适用性扩展到了安全应用领域，弥合了深度学习性能与关键场景中可解释性需求之间的差距。
英文摘要
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.
