Evaluation of network fault-detection method based on anomaly detection with matrix eigenvector

To provide high-quality and highly reliable services in IP networks, the service down time after network faults have occurred needs to be reduced. However, there are network faults that operators cannot detect with network monitoring alarms, such as Simple Network Management Protocol Trap, or by monitoring device statuses from collected network data. To solve this problem, we focused on anomaly-detection methods. Although these methods have been proposed for computer systems, few studies have focused on IP networks. Therefore, we focused an anomaly-detection method based on a matrix eigenvector for detecting faults in IP networks and evaluated whether this method can be applied to IP networks by using network fault simulations. Evaluation results show that the anomaly-detection method based on a matrix eigenvector is effective in detecting network faults in IP networks, but it has two problems. One is that this method may detect normal states as anomalies and the other is that optimizing the discounting factor for detecting sequential faults is difficult.