Latent Variable Mining with Its Applications to Anomalous Behavior Detection

In this paper, we propose a new approach to anomaly detection by looking at the latent variable space to make the first step toward latent anomaly detection. Most conventional approaches to anomaly detection are concerned with tracking data which are largely deviated from the ordinary pattern. In this paper, we are instead concerned with the issue of how to track changes happening in the latent variable space consisting of the meta information existing behind observed data. For example, in the case of masquerade detection, the conventional task was to detect anomalous command lines related to masqueraders' malicious behaviors. Meanwhile we rather attempt to track changes of behavior patterns such as writing mails, making software, etc. which are information abstract level than command lines. The key ideas of the methods are: 1) constructing the model variation vector, which is introduced relative to the latent variable space, and 2) the latent anomaly detection is reduced to the ...

• Type: Conference
• Year: 2008
• Where: SDM
• Authors: Shunsuke Hirose, Kenji Yamanishi
• Keywords: Anomaly Detection, Data Mining, Latent Anomaly Detection, Latent Variable Space, SDM 2008