Network anomaly detection based on Eigen equation compression

This paper addresses the issue of unsupervised network anomaly detection. In recent years, networks have played more and more critical roles. Since their outages cause serious economic losses, it is quite significant to monitor their changes over time and to detect anomalies as early as possible. In this paper, we specifically focus on the management of the whole network. In it, it is important to detect anomalies which make great impact on the whole network, and the other local anomalies should be ignored. Further, when we detect the former anomalies, it is required to localize nodes responsible for them. It is challenging to simultaneously perform the above two tasks taking into account the nonstationarity and strong correlations between nodes. We propose a network anomaly detection method which resolves the above two tasks in a unified way. The key ideas of the method are: (1) construction of quantities representing feature of a whole network and each node from the same input based...

• Type: Conference
• Year: 2009
• Where: KDD
• Authors: Shunsuke Hirose, Kenji Yamanishi, Takayuki Nakata, Ryohei Fujimaki
• Keywords: Copyright 2009 Acm, Data Mining, KDD 2009, Local Anomalies, Network Anomaly Detection