论文标题
动态网络中的异常检测
Anomaly detection in dynamic networks
论文作者
论文摘要
从一系列时间网络中检测异常有许多应用程序，包括运输网络中的道路事故和社交网络中的可疑事件。尽管有许多用于网络异常检测的方法，但即使在处理时间依赖性方面具有悠久的历史和经过证实的能力，统计方法仍在该空间中使用。在本文中，我们介绍了一种基于特征的网络异常检测方法\ textIt {Oddnet}，该方法使用时间序列方法来建模时间依赖性。我们证明了Oddnet对合成和现实世界数据集的有效性。 R软件包ODDNET实现此算法。
Detecting anomalies from a series of temporal networks has many applications, including road accidents in transport networks and suspicious events in social networks. While there are many methods for network anomaly detection, statistical methods are under utilised in this space even though they have a long history and proven capability in handling temporal dependencies. In this paper, we introduce \textit{oddnet}, a feature-based network anomaly detection method that uses time series methods to model temporal dependencies. We demonstrate the effectiveness of oddnet on synthetic and real-world datasets. The R package oddnet implements this algorithm.
