Advanced Reinforcement Learning Techniques for Real-Time Adaptive Firewall Management in Dynamic Network Environments
Keywords:
Reinforcement Learning, Adaptive Firewall, Deep Q-Networks, Actor-CriticAbstract
The advent of advanced network environments, coupled with increasing cyber threats, has necessitated the development of more intelligent and adaptive firewall management systems. Traditional rule-based firewall systems are insufficient in dynamically changing network conditions and sophisticated attack strategies. This paper investigates the application of advanced reinforcement learning (RL) techniques in real-time adaptive firewall management to optimize security decision-making. By utilizing state-of-the-art RL algorithms, such as Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Actor-Critic methods, this paper discusses how these algorithms can enable firewalls to adapt to evolving network conditions and mitigate advanced persistent threats. The research explores various aspects, including the integration of RL with existing firewall systems, the challenges of training RL models in complex and dynamic environments, and the potential for autonomous decision-making in real-time threat detection and mitigation. Case studies and performance evaluations illustrate the effectiveness of RL-based approaches in improving firewall efficiency, reducing response times, and enhancing the overall security posture of network infrastructures.
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