Anomaly detection

Anomaly detection is a cybersecurity technique that identifies patterns, events, or data points that deviate significantly from established normal behavior within a system, network, or dataset. By continuously monitoring activity—such as network traffic volumes, user login patterns, server processes, and data access sequences—anomaly detection systems build comprehensive baselines of "normal" operation. Any activity falling outside these dynamically learned or predefined norms is flagged as an anomaly, potentially indicating a security incident, intrusion attempt, or system malfunction.

This technique is particularly valuable for uncovering sophisticated threats like zero-day exploits, insider threats, and advanced persistent threats (APTs) that often evade traditional signature-based detection methods. Unlike rule-based approaches that rely on known threat signatures, anomaly detection can identify previously unknown indicators of compromise and emerging attack vectors. This proactive capability empowers security teams to investigate subtle, unusual occurrences early, mitigate risks before they escalate, and strengthen an organization's overall defensive posture as part of a comprehensive threat intelligence framework.