Description: Anomaly-based detection is an approach used in intrusion detection systems (IDS) that focuses on identifying behaviors that deviate from what is considered normal in a system or network. This method is based on the premise that malicious or unauthorized activities often manifest as anomalies in data traffic, usage patterns, or user behaviors. Unlike systems that use predefined signatures to detect intrusions, anomaly-based detection does not require prior knowledge of threats, making it particularly useful for identifying unknown attacks or new variants of malware. This approach involves the use of machine learning techniques and statistical analysis to establish a normal behavior profile and subsequently monitor activities for significant deviations. The ability to adapt and learn from changes in the operational environment is one of the most notable features of this method, allowing for more effective and real-time detection of potential intrusions. In a world where cyber threats are becoming increasingly sophisticated, anomaly-based detection has become an essential tool for cybersecurity, providing an additional layer of defense against attacks that may go unnoticed by traditional methods.
