Open-Source Security Operations Center (SOC)

8Security Analytics and Machine Learning in SOC

Security operations centers (SOCs) are increasingly leveraging advanced analytics and machine learning (ML) techniques to better detect, investigate, and respond to modern security threats. Where traditional rule-based SIEMs focused primarily on log and alert consolidation, next-generation SOCs incorporate statistical analysis, user behavior modeling, and automated threat hunting to gain deeper behavioral insights (Palo Alto Networks, 2024).

Rather than relying solely on known indicators of compromise (IOCs), analytics empower threat hunters to search for previously undiscovered patterns, spot anomalous user activities indicative of insider risks, and continuously refine detection models based on confirmed real-world incidents. This shift toward analytics-driven security operations requires new tools, skillsets, and processes optimized for exploration over simple monitoring.

Some key areas modern SOCs are applying advanced analytics and ML include security information and event management (SIEM), user and entity behavior analytics (UEBA), network traffic analysis (NTA), and endpoint detection and response (EDR). By fusing diverse data sources through correlated statistical modeling, security practitioners gain a more holistic view of organizational “normal” for establishing baselines and detecting subtle deviations. For example, a SIEM can learn that finance employees rarely access HR systems except for occasional payroll submissions. ...