论文标题
解释：迈向无监督网络流量分析的可解释AI
EXPLAIN-IT: Towards Explainable AI for Unsupervised Network Traffic Analysis
论文作者
论文摘要
无监督的学习方法，尤其是聚类技术的应用代表了分析网络测量的强大探索手段。发现潜在的数据特征，将类似的测量分组在一起以及确定最终感兴趣的模式是可以通过聚类来解决的一些应用。不受监督的聚集并不总是提供对所产生的输出的精确和清晰的见解，尤其是当输入数据结构和分布很复杂且难以掌握时。在本文中，我们介绍了一种涉及未标记数据，创建有意义的簇的方法，并提出了对最终用户的聚类结果的解释。解释 - 它依靠一种新颖的可解释的人工智能（AI）方法，该方法允许理解导致基于学习的基于学习的模型的特定决定的原因，并将其应用于无监督的学习领域。我们将解释应用于加密的流量方案下的YouTube视频质量分类问题，显示出令人鼓舞的结果。
The application of unsupervised learning approaches, and in particular of clustering techniques, represents a powerful exploration means for the analysis of network measurements. Discovering underlying data characteristics, grouping similar measurements together, and identifying eventual patterns of interest are some of the applications which can be tackled through clustering. Being unsupervised, clustering does not always provide precise and clear insight into the produced output, especially when the input data structure and distribution are complex and difficult to grasp. In this paper we introduce EXPLAIN-IT, a methodology which deals with unlabeled data, creates meaningful clusters, and suggests an explanation to the clustering results for the end-user. EXPLAIN-IT relies on a novel explainable Artificial Intelligence (AI) approach, which allows to understand the reasons leading to a particular decision of a supervised learning-based model, additionally extending its application to the unsupervised learning domain. We apply EXPLAIN-IT to the problem of YouTube video quality classification under encrypted traffic scenarios, showing promising results.
