Proactive anomaly detection using distributed intelligent agents

Abstract

Proactive network anomaly detection is crucial to provide quality of service guarantees for future networks. We characterize a set of proactively detectable anomalies in terms of the management information base variables. The time series data obtained from these variables are analyzed by an intelligent agent, which is a simple and lightweight signal processor. The agent provides real-time proactive alarms that are indicative of impending network problems and suggests the possibility of implementing automatic recovery mechanisms. Proactive detection was accomplished within minutes. The agent is shown to be amenable to distributed implementation, and is a promising approach to self-managed networks.