A Graph Neural Network Framework for Real Time Cyber Threat Intelligence and Risk Analysis

The increasing sophistication of cyber threats necessitates intelligent, adaptive, and real-time threat detection mechanisms. Long-standing cyber security systems face difficulties in managing attacks because the complex patterns of cyber threats form intricate interconnections among networks along with devices and users. This research presents a Graph Neural Network (GNN) framework to perform real-time cyber threat analysis and intelligence using deep learning graph approaches for modeling complex attack patterns and anomaly detection for enhanced prediction security. Cyber threats get represented through graph structures using GNN that link entities (such as users or devices and IP addresses) to their relationship paths called edges. This method helps the proposed GNN model to uncover complex relationships in cyber security data. GNNs surpass traditional machine learning algorithms by detecting underlying spatial and temporal elements from threat environments thus attaining superior threat identification and activated risk prediction and minimal false alarm rates. Through this framework real-time data streams integrate with anomaly detection and adversarial threat modeling while performing dynamic cyber risk prediction and mitigation. GNNS-based threat analysis demonstrates better accuracy at 96.8 % with a 40% decrease in false detection rates together with enhanced speed over traditional security methods. The model demonstrates its ability to identify unknown cyber attacks at the same time it displays its capability to adapt to emerging online security risks.