The classification of internet traffic has become increasingly important due to the rapid growth of today's networks and applications. The number of connections and the addition of new applications in our networks causes a vast amount of log data and complicates the search for common patterns by experts. Finding such patterns among specific classes of applications is necessary to fulfill various requirements in network analytics. Deep learning methods provide both feature extraction and classification from data in a single system. However, these networks are very complex and are used as black-box models, which weakens the experts' trust in the classifications. Moreover, by using them as a black-box, new knowledge cannot be obtained from the model predictions despite their excellent performance. Therefore, the explainability of the classifications is crucial. Besides increasing trust, the explanation can be used for model evaluation gaining new insights from the data and improving the model. In this paper, we present a visual interactive tool that combines the classification of network data with an explanation technique to form an interface between experts, algorithms, and data.
翻译:由于当今网络和应用程序的迅速增长,互联网通信的分类变得日益重要。连接数量和我们网络中新应用程序的增加导致大量日志数据,使专家寻找共同模式的工作复杂化。在具体应用类别中找到这种模式对于满足网络分析的各种要求是必要的。深层次学习方法从单一系统的数据中提取和分类特征。然而,这些网络非常复杂,被用作黑盒模型,削弱了专家对分类的信任。此外,利用它们作为黑盒,无法从模型预测中获得新的知识,尽管它们表现出色。因此,分类的可解释性至关重要。除了增加信任外,还可以在模型评估中利用解释,从数据中获得新的洞见和改进模型。在本文中,我们提出了一个视觉互动工具,将网络数据的分类与解释技术结合起来,形成专家、算法和数据之间的界面。