Automatic Vulnerability Management with Reinforcement Learning: AI-Driven Patch Prioritization and Deployment in Large-Scale Networks
Keywords:
Reinforcement learning, autonomous vulnerability management, cybersecurity, patch deployment strategies, large-scale networkAbstract
On large networks, complex cybersecurity calls for innovative vulnerability management. Patch prioritizing and distribution automated using reinforcement learning (RL) provides businesses with constant security patches. Using RL, autonomous vulnerability management (AVM) schedules repairs, evaluates risk, and responds to network changes. Investigated are Proximal Policy Optimization (PPO) and Deep Q-Networks (DQN) along with their cybersecurity framework integration. The studies indicate how RL-driven AVM creates networks and lowers risks. We investigate and propose solutions for real-time decision-making, computational overhead, and scalability. Zero-trust systems and federated learning will allow RL to improve cybersecurity cooperation.
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