Statistical anomaly detection is an intrusion detection technique that models the normal behavior of a network's traffic and identifies deviations from this norm. It uses statistical metrics such as median, mean, mode, and standard deviation to establish a baseline of regular activities. When network traffic deviates from these established performance parameters, the system flags these events as potential intrusions. This method is effective in observing the network for abnormal usage patterns that could indicate a security breach.
References: The explanation is based on the principles of statistical anomaly detection as described in various Network Defender (CND) documents and study guides. Specifically, it aligns with the descriptions found in resources like the Saylor Academy's module on Intrusion Detection Systems1, which details how a statistics-based IDS builds a distribution model for normal behavior and flags low probability events as potential intrusions.