Anomaly Detection

What is Anomaly Detection in the context of user behavior?

Anomaly detection in user behavior involves identifying unusual patterns or activities that deviate significantly from normal user behavior. This helps in identifying potential security threats, fraudulent activities, or system malfunctions.

How does Anomaly Detection work?

Anomaly detection systems typically employ statistical methods, machine learning algorithms, or a combination of both to analyze user data and identify outliers. These outliers are then flagged as potential anomalies for further investigation.

What kind of user data is used for Anomaly Detection?

User data such as IP address, device type, login times, and access patterns, can be used for anomaly detection.

What are the benefits of Anomaly Detection?

Anomaly detection helps in preventing fraud, detecting security breaches, improving system performance, and enhancing customer experience by identifying and addressing issues promptly.

How does Anomaly Detection help in preventing fraud?

By identifying unusual user behavior patterns, anomaly detection systems can detect fraudulent activities like account takeover, unauthorized access, and fraudulent transactions.

How do you balance false positives and false negatives in anomaly detection?

By threshold tuning, threat scoring, and human-in-the-loop verification.