document: Eigenspace-based anomaly detection in computer systems We report on an automated runtime anomaly detection method at the application layer of multi-node computer systems . Although several network management systems are available in the market , none of them have sufficient capabilities to detect faults in multi-tier Web-based systems with redundancy . We model a Web-based system as a weighted graph , where each node represents a `` service '' and each edge represents a dependency between services . Since the edge weights vary greatly over time , the problem we address is that of anomaly detection from a time sequence of graphs . In our method , we first extract a feature vector from the adjacency matrix that represents the activities of all of the services . The heart of our method is to use the principal eigenvector of the eigenclusters of the graph . Then we derive a probability distribution for an anomaly measure defined for a time-series of directional data derived from the graph sequence . Given a critical probability , the threshold value is adaptively updated using a novel online algorithm . We demonstrate that a fault in a Web application can be automatically detected and the faulty services are identified without using detailed knowledge of the behavior of the system .
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