Anomaly detection in time-evolving networks has many applications, for instance, traffic analysis in transportation networks and intrusion detection in computer networks. One group of popular methods for anomaly detection from evolving networks are robust online subspace trackers. However, these methods suffer from problem of insensitivity to drastic changes in the evolving subspace. In order to solve this problem, we propose a new robust online subspace and anomaly tracker, which is more adaptive and robust against sudden drastic changes in the subspace. More accurate estimation of low rank and sparse components by this tracker leads to more accurate anomaly detection. We evaluate the accuracy of our method with real-world dynamic network data sets with varying sparsity levels. The result is promising and our method outperforms the state-of-the-art.

随时间变化的网络中的异常检测具有许多应用，例如，运输网络中的流量分析和计算机网络中的入侵检测。强大的在线子空间跟踪器是用于从不断发展的网络进行异常检测的一组流行方法。但是，这些方法存在对演化的子空间的急剧变化不敏感的问题。为了解决这个问题，我们提出了一种新的健壮的在线子空间和异常跟踪器，它对子空间的突然急剧变化具有更大的适应性和鲁棒性。通过此跟踪器更准确地估计低秩和稀疏分量，可以更准确地检测异常。我们使用稀疏度不同的动态网络数据集评估我们方法的准确性。