Adaptive Firewall Strategy Generation and  Optimization Based on Reinforcement Learning

Yatharth Rajeev Bhagwat

doi:10.36948/ijfmr.2025.v07i06.60691

Adaptive Firewall Strategy Generation and Optimization Based on Reinforcement Learning

Author(s)	Mr. Yatharth Rajeev Bhagwat
Country	India
Abstract	Traditional firewall systems use static rule sets, making them unsuitable for growing cyber threats and network circumstances. In this research, we automate firewall rule development and optimization using Reinforcement Learning (RL) to increase network security and reduce human setup. This paper introduces FireRL, a system that uses RL to help make smart decisions about firewall rules by treating it like a Markov Decision Process (MDP), enabling an RL agent to learn optimal firewall policies from simulated network traffic. This proposed method utilizes the Proximal Policy Optimization (PPO) Actor-Critic algorithm to balance throughput, latency, and threat mitigation via stable, on-policy policy gradient updates. Experiments are performed in benign and dangerous traffic situations. Firewalls outperform static firewalls because the PPO agent quickly reacts to new threats and reduces false positives by directly optimizing the rule setting policy. Resistance to new attack vectors demonstrates the system's flexibility and resilience. This research concludes with a self-optimizing firewall approach that greatly lowers expert-led settings. FireRL's proactive and scalable RL-based defense is ideal for current cybersecurity.
Keywords	adaptive firewall, reinforcement learning, deep Q-Learning, network security, policy optimization, markov decision process (MDP)
Field	Computer > Network / Security
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-11-15
DOI	https://doi.org/10.36948/ijfmr.2025.v07i06.60691

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Adaptive Firewall Strategy Generation and Optimization Based on Reinforcement Learning

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