ENHANCING SOC OPERATIONS WITH COMPLEX EVENT PROCESSING AND STREAM-BASED ANOMALY DETECTION ALGORITHMS

Abstract : Enhancing Security Operations Center (SOC) operations through the integration of Complex Event Processing (CEP) and stream-based anomaly detection algorithms represents a transformative approach to modern cybersecurity challenges. As cyber threats grow increasingly sophisticated and voluminous, traditional security mechanisms struggle to provide real-time, context-aware detection and response capabilities. CEP offers a powerful framework that enables the continuous ingestion, correlation, and analysis of vast streams of security-relevant data from heterogeneous sources such as network logs, endpoint telemetry, and threat intelligence feeds. By applying complex pattern matching and temporal correlation rules, CEP systems can identify multi-stage attack scenarios and subtle indicators of compromise that often evade conventional signature-based methods. Complementing CEP, stream-based anomaly detection algorithms leverage machine learning and statistical techniques to model normal behavior patterns within the data streams and detect deviations indicative of novel or unknown threats. These algorithms operate in real time, adapting dynamically to evolving network conditions and user behaviors, thus minimizing false positives and enabling early warning of emerging attacks. The fusion of CEP with anomaly detection enhances situational awareness within SOCs by providing enriched alerts that combine contextual insights and probabilistic threat assessments, facilitating faster and more accurate incident prioritization and investigation. Moreover, the deployment of such integrated solutions supports automation of routine analysis tasks and enables proactive threat hunting by uncovering hidden correlations and emerging attack vectors. This approach also aligns with the growing need for scalable and flexible security analytics platforms that can handle high-velocity data streams without compromising performance. Experimental evaluations demonstrate that CEP-powered SOC systems augmented with stream-based anomaly detection algorithms significantly improve detection precision, reduce mean time to detect (MTTD) and respond (MTTR), and enhance the overall resilience of organizational security posture. Additionally, these technologies empower SOC analysts by reducing alert fatigue through intelligent alert aggregation and risk scoring, allowing human experts to focus on critical threats requiring deeper forensic analysis. The integration of CEP and streaming anomaly detection thus represents a paradigm shift in cybersecurity operations, moving beyond reactive defenses towards predictive, adaptive, and intelligence-driven security frameworks capable of addressing the complex threat landscape of today and the future. This paper reviews the state-of-the-art methodologies, system architectures, and practical implementation challenges associated with deploying CEP and stream-based anomaly detection in SOC environments, and outlines future research directions to further optimize their effectiveness and scalability.

Keyword Complex Event Processing, Stream-Based Anomaly Detection, Security Operations Center, Real-Time Threat Detection, Cybersecurity Analytics, Incident Response Automation