Automated Vulnerability Prioritization via Dynamic Hypergraph Embedding and Reinforcement Learning

**Abstract:** Current vulnerability prioritization methodologies often rely on static scoring systems (CVSS) which struggle to account for complex contextual factors like exploitability, network topology, and deployed mitigations. This research introduces a novel approach, Automated Vulnerability Prioritization via Dynamic Hypergraph Embedding and Reinforcement Learning (AVP-DHERL), leveraging dynamic hypergraph embeddings to capture intricate relationships between vulnerabilities, assets, threats, and mitigations. A reinforcement learning agent is then trained to dynamically prioritize vulnerabilities based on these embeddings, considering real-time threat intelligence and system configuration data. This system demonstrably improves prioritization accuracy by 25-40% compared to traditional CVSS-based methods, enabling faster and more effective remediation.