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首页> 外文会议>IEEE International Conference on Cyber Security and Resilience >Unveiling MIMETIC: Interpreting Deep Learning Traffic Classifiers via XAI Techniques
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Unveiling MIMETIC: Interpreting Deep Learning Traffic Classifiers via XAI Techniques

机译：揭开模拟的面纱：通过XAI技术解读深度学习流量分类器

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The widespread use of powerful mobile devices has deeply affected the mix of traffic traversing both the Internet and enterprise networks (with bring-your-own-device policies). Traffic encryption has become extremely common, and the quick proliferation of mobile apps and their simple distribution and update have created a specifically challenging scenario for traffic classification and its uses, especially network-security related ones. The recent rise of Deep Learning (DL) has responded to this challenge, by providing a solution to the time-consuming and human-limited handcrafted feature design, and better clas-sification performance. The counterpart of the advantages is the lack of interpretability of these black-box approaches, limiting or preventing their adoption in contexts where the reliability of results, or interpretability of polices is necessary. To cope with these limitations, eXplainable Artificial Intelligence (XAI) techniques have seen recent intensive research. Along these lines, our work applies XAI-based techniques (namely, Deep SHAP) to interpret the behavior of a state-of-the-art multimodal DL traffic classifier. As opposed to common results seen in XAI, we aim at a global interpretation, rather than sample-based ones. The results quantify the importance of each modality (payload- or header-based), and of specific subsets of inputs (e.g., TLS SNI and TCP Window Size) in determining the classification outcome, down to per-class (viz. application) level. The analysis is based on a publicly-released recent dataset focused on mobile app traffic.
机译：功能强大的移动设备的广泛使用已经深刻影响了互联网和企业网络的流量组合（使用自带设备策略）。流量加密已经变得非常普遍，移动应用的快速普及及其简单的分发和更新为流量分类及其使用，尤其是与网络安全相关的流量分类及其使用创造了一个特别具有挑战性的场景。最近兴起的深度学习（Deep Learning，DL）通过为耗时且人为有限的手工特征设计提供解决方案，以及更好的分类性能，回应了这一挑战。与之相对应的优点是，这些黑匣子方法缺乏可解释性，在结果的可靠性或政策的可解释性是必要的情况下，限制或阻止了它们的采用。为了应对这些限制，可解释人工智能（XAI）技术最近得到了深入的研究。沿着这些思路，我们的工作应用基于XAI的技术（即深度SHAP）来解释最先进的多模式DL流量分类器的行为。与XAI中常见的结果不同，我们的目标是进行全局解释，而不是基于样本的解释。结果量化了每种模式（基于有效载荷或报头）以及特定输入子集（例如TLS SNI和TCP窗口大小）在确定分类结果时的重要性，直至每类（即应用）水平。该应用基于最近发布的一个移动流量分析数据集公开发布。

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