Anomaly Detection
By identifying outliers in data, anomaly detection helps prevent potential threats or problems. In cybersecurity, for example, anomaly detection algorithms can alert organizations to suspicious activities, allowing them to act swiftly and mitigate risks before they escalate into major issues.
Anomaly detection is the process of identifying patterns or data points that deviate significantly from the expected behavior. In AI, it is used to detect unusual patterns, such as fraudulent transactions or system malfunctions. Machine learning algorithms play a critical role in anomaly detection, learning normal behavior from historical data and flagging deviations. This method is widely applied in cybersecurity, network monitoring, and fraud detection.