Anomaly-Based Detection
Anomaly-based detection is a cybersecurity and data analysis technique that identifies unusual patterns or outliers in data that deviate from expected behavior. It involves establishing a baseline of normal activity and flagging deviations as potential threats, errors, or interesting events. This approach is widely used in intrusion detection systems, fraud prevention, and system monitoring to detect unknown or novel attacks.
Developers should learn anomaly-based detection when building systems that require proactive security monitoring, such as network security tools, financial transaction platforms, or IoT device management. It is particularly valuable for detecting zero-day exploits, insider threats, or subtle fraud patterns that rule-based systems might miss, making it essential for applications in cybersecurity, finance, and operational technology.