Dynamic Cybersecurity Risk Assessment: Temporal Graph Neural Networks and Reinforcement Learning for Proactive Threat Management

Abstract

The cyber threat necessitates rapid paradigm shifts in cyber security strategies. The traditional approach to threat/risk management is becoming increasingly ineffective in dealing with the dynamic nature of modern threats. Therefore, this paper provides a dynamic cyber security risk assessment framework that combines temporal graph neural networks and reinforcement learning (RL). This would allow threat management TGNNs to make proactive decisions to help evolve relationships within networking environments. On the other hand, RL optimizes its defensive technique by learning from previous threats and predicting potential vulnerabilities. The evaluation would require demonstrable efficacy in reducing response time while improving threat detection accuracy and risk mitigation in real-world scenarios.