Abstract In this work, we propose a new approach to detect anomalous graphs in a stream of directed and labeled heterogeneous edges. The stream consists of a sequence of edges derived from different graphs. Each of these dynamic graphs represents the evolution of a specific activity in a monitored system whose events are acquired in real-time. Our approach is based on graph clustering and uses a simple graph embedding based on substructures and graph edit distance. Our graph representation is flexible and updates incrementally the graph vectors as soon as a new edge arrives. This allows the detection of anomalies in real-time which is an important requirement for sensitive applications such as cyber-security. Our implementation results prove the effectiveness of our approach in terms of accuracy of detection and time processing.

中文翻译：

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在异构标记图流中进行异常检测的简单图嵌入

摘要 在这项工作中，我们提出了一种新方法来检测有向和标记的异构边流中的异常图。流由来自不同图的一系列边组成。这些动态图形中的每一个都代表了被​​监控系统中特定活动的演变，其事件是实时获取的。我们的方法基于图聚类，并使用基于子结构和图编辑距离的简单图嵌入。我们的图表示是灵活的，一旦新边到达，就会增量更新图向量。这允许实时检测异常，这是敏感应用程序（例如网络安全）的重要要求。我们的实施结果证明了我们的方法在检测和时间处理的准确性方面的有效性。