Anomaly-Based Security Monitoring

Anomaly-based security monitoring is a cybersecurity approach that detects threats by identifying deviations from normal behavior patterns in systems, networks, or user activities. It uses machine learning, statistical analysis, or behavioral baselines to establish what constitutes 'normal' and flags anomalies that may indicate security incidents like intrusions, malware, or insider threats. This method complements signature-based detection by catching novel or unknown attacks that lack predefined patterns.

Also known as: Behavioral Anomaly Detection, Anomaly Detection in Security, ABSM, Anomaly-Based Intrusion Detection, Anomaly Monitoring

Why learn Anomaly-Based Security Monitoring?

Developers should learn and implement anomaly-based monitoring to enhance security in dynamic environments like cloud infrastructure, IoT systems, or applications with high user interaction, where traditional rule-based methods may fail. It is crucial for detecting zero-day exploits, insider threats, and sophisticated attacks that evade conventional defenses, helping maintain compliance and reduce false positives in security operations.

Learning Resources

• docs: NIST Guide to Intrusion Detection and Prevention Systems
• course: Coursera: Introduction to Cybersecurity Tools & Cyber Attacks
• tutorial: Splunk: Anomaly Detection Tutorial
• video: YouTube: Anomaly Detection in Cybersecurity Explained
• book: Book: 'Anomaly Detection: Principles and Algorithms' by Varun Chandola et al.