Description: Anomaly detection refers to the identification of unusual patterns that do not conform to expected behavior within a dataset. This process is fundamental in various areas, such as cybersecurity, system monitoring, and data analytics. Anomaly detection enables organizations to identify potential issues before they escalate into serious incidents, facilitating informed decision-making. It employs statistical techniques, machine learning, and artificial intelligence to analyze large volumes of data and detect significant deviations. Key features of anomaly detection include the ability to adapt to different data types, the identification of complex patterns, and the reduction of false positives. In various environments, anomaly detection can help identify unusual behaviors that may indicate security or performance issues. In data visualization, these anomalies can be graphically represented to facilitate analysis and understanding. In the context of security assessments, anomaly detection is crucial for identifying unauthorized access or suspicious activities. In cloud computing, this technique is used to monitor traffic and resource usage, while various security software solutions implement anomaly detection algorithms to protect against malware and cyber threats.
