Graph-Based Machine Learning Techniques for Detecting Coordinated Cyber Attacks across Distributed Systems

Abstract

Distributed computer multi-stage cyberattack detection changes using graph-based machine learning. The temporal and systemic links between cloud, on-premises, and edge infrastructure are ignored by signature-based anomaly detection. Coordinated assaults are detected via GNN-based relationship-driven anomaly detection on telemetry, network traffic, authentication, and behavioral activity patterns People, equipment, processes, and services are represented as connected nodes with edges showing communication pathways, access permissions, and operational interdependence using advanced graph modeling. Lateral movement, multi-vector invasion, and sophisticated persistent attacks are adversary cooperation. Scalable GNN solutions for big corporate networks with data heterogeneity, dynamic graph creation, and computational overhead are discussed. Tests demonstrate improved detection accuracy and fewer false positives than standard frameworks. Research improves real-time threat attribution deployment, security, and performance. Remote infrastructure and cybersecurity are improved by graph-based learning systems.