Network security would be incomplete without firewalls that control traffic flow through rule-based policies. The manual way to configure and manage firewall rules, however, is prone to various pitfalls; rules tend to become overly complex, human error occurs, and cyber threats continue to evolve. This work investigates the reinforcement learning (RL) - driven method for firewall policy generation, utilizing RL as an automated means for policy generation to increase adaptability and reduce administrative overhead. The proposed system utilizes RL agents that learn an optimal policy from real-time network traffic and dynamically update firewall rules to maximize security while minimizing false positives and latency. The key contributions of this work include a novel system architecture that integrates reinforcement learning (RL) with existing firewall frameworks, as well as methodologies for data collection, feature engineering, and reward function design. Additionally, the system is evaluated using simulated network environments and benchmark datasets. It is demonstrated that the RL-based system achieves better accuracy in threat detection compared to traditional static or heuristic approaches, as well as improved policy effectiveness and network performance. Computational cost, explainability, exploration risks, and model generalization are discussed, and future research directions in transfer learning, multi-agent coordination, and integration with broader security frameworks are addressed. This work moves the field closer to realizing real-time, intelligent, and adaptive firewalls that can handle today's cybersecurity challenges. It motivates further exploration of more secure, interpretable, and production-ready RL-driven security solutions.

Firewall automation, Reinforcement learning, Network security, Adaptive policies, Cyber threat detection

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