Anomaly detection for diagnosis

Abstract

The author presents a method for detecting anomalous events in communication networks and other similarly characterized environments in which performance anomalies are indicative of failure. The methodology, based on automatically learning the difference between normal and abnormal behavior, has been implemented as part of an automated diagnosis system from which performance results are drawn and presented. The dynamic nature of the model enables a diagnostic system to deal with continuously changing environments without explicit control, reaching to the way the world is now, as opposed to the way the world was planned to be. Results of successful deployment in a noisy, real-time monitoring environment are shown.