Spatiotemporal Neural Network based Graph Architecture for Autonomous Anomaly Detection in Critical Infrastructure Security

Abstract

This abstract outline a sophisticated framework designed to fortify autonomous anomaly detection in the realm of critical infrastructure security. Leveraging a Spatiotemporal Neural Network-based Graph Architecture, the proposed model integrates spatial and temporal dimensions to enhance its analytical capabilities. The fusion of spatial and temporal information facilitates a comprehensive understanding of dynamic patterns within the infrastructure, allowing for more nuanced anomaly detection. The architecture's foundation lies in a neural network that is adept at capturing and processing intricate spatiotemporal relationships. This neural network is integrated into a graph-based framework, offering a flexible and scalable representation of the critical infrastructure. The graph structure enables the model to capture and analyze complex relationships and dependencies among various components, enhancing its ability to discern anomalies within the intricate web of infrastructure elements. Autonomy is a key feature of the proposed system, as it operates without constant human intervention.

Through continuous learning and adaptation, the model refines its understanding of normal operating conditions and evolves to identify deviations that may indicate potential security threats. This autonomous capability is paramount in addressing the dynamic and evolving nature of security challenges faced by critical infrastructure. The research presented herein contributes to the advancement of autonomous security systems, providing a robust solution for safeguarding critical infrastructure against emerging and sophisticated threats. By combining state-of-the-art neural network technologies with graph-based representations, the proposed architecture presents a promising avenue for improving the reliability and efficiency of anomaly detection in critical infrastructure security.