Anomaly-Based Detection is a cybersecurity technique that identifies unusual patterns or behaviors in data. By establishing a baseline of normal activity, it can flag deviations that may indicate potential threats, such as malware or intrusions. This method is effective because it can detect new and unknown attacks that traditional signature-based systems might miss. This approach relies on machine learning algorithms and statistical analysis to monitor network traffic, user behavior, and system performance. When an anomaly is detected, alerts are generated for further investigation, helping organizations respond quickly to potential security incidents and protect their information systems.