基于聚合类激活图的网络数据全局解释交互式分析
Interactive Analysis of Global Explanations using Aggregated Class Activation Maps for Network Data
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中文总结 AI 辅助
针对网络流量分类中同一类别内存在不同模式的问题，提出基于聚合类激活图的可视化交互式系统，可实现全局解释的探索优化，辅助专家检测新模式、制定网络管理规则及优化深度学习模型。
中文摘要 AI 辅助
近期机器学习（ML）领域的进展表明，深度学习（DL）在不同应用场景中取得了令人瞩目的成果，包括将计算机网络流量分类到对应应用的任务中。然而，数据中同一预测类别内常存在不同模式，这对提供清晰全面解释的能力构成重大挑战，凸显了检测和分析这些模式的工具的必要性。此外，提取类别的描述性规则是网络流量分析和入侵检测的关键需求，尤其在利用下一代防火墙等先进工具时。我们提供了一个可视化交互式系统，用于解释网络流量的类别预测。从给定类别的多个样本中推导的全局解释有助于理解模型预测。全局解释的可视化使不同模式得以识别，为专家提供对其特征更全面的概览。我们引入了一个原型，该原型支持对全局解释进行可视化探索和优化，使网络专家能够为特定应用检测并优化新模式。这些解释有助于识别误导性特征，并制定用于网络管理的新规则。我们的方法还旨在让机器学习专家获得新见解，包括分离或合并类别的可能性，以及开发更准确可靠的深度学习模型。我们提出的原型已通过机器学习和网络分析领域的专家进行评估。
英文摘要
Recent machine learning (ML) advances have demonstrated that deep learning (DL) achieves impressive results in different application domains, including the classification of computer network traffic to corresponding applications. However, the data frequently contains diverging patterns within a single predicted class. This presents a significant challenge to the ability to provide a clear and comprehensive explanation and emphasizes the necessity for tools capable of detecting and analyzing these patterns. Furthermore, the capacity to extract descriptive rules for classes is a crucial requirement in network traffic analysis and intrusion detection, particularly when leveraging advanced tools like next-generation firewalls. We provide a visual-interactive system that explains predictions of classes for network traffic. Global explanations derived from multiple samples of a given class contribute to understanding model predictions. Visualization of global explanations enables recognition of different patterns that offer experts a more comprehensive overview of its characteristics. We introduce a prototype that facilitates visual exploration and refinement of global explanations, enabling network experts to detect and refine new patterns for specific applications. These explanations support the identification of misleading features and the formulation of new rules for the management of networks. Our approach also aims at enabling ML experts to acquire new insights, including the possibility of separating or merging classes and the development of more accurate and reliable DL models. Our proposed prototype was evaluated by experts in machine learning and network analysis.
发表机构
- Fraunhofer IGD（弗劳恩霍夫计算机图形学研究所）
- TU Darmstadt（达姆施塔特工业大学）
机构由 AI 辅助整理，请以论文原文为准。
