AI Security
Advanced
ML Anomaly Detection
Machine learning anomaly detection builds statistical models of normal behavior across networks, endpoints, and users, then flags deviations as potential threats. This approach excels at catching insider threats, lateral movement, and novel malware that signature-based tools miss. Tuning these models to reduce alert fatigue while maintaining sensitivity is a key practitioner skill.
Key Capabilities
- Statistical modeling of user and entity behavior
- Unsupervised learning for outlier detection
- Network traffic anomaly analysis
- Log-based ML model training and evaluation
- Threshold tuning to balance sensitivity and alert fatigue
Tags
Machine Learning Anomaly Detection UEBA Insider Threat Behavioral