Enhancing Cybersecurity: A Proximal Policy Optimization Approach for Security Policy Optimization

With the continuous evolution of cyber threats, cybersecurity strategies are crucial for addressing these threats to protect digital assets, data integrity, and system availability. Ensuring safe and efficient policy optimization in the face of cyber threats remains a significant challenge in current research. This paper proposes a security policy optimization model based on Proximal Policy Optimization (PPO). The model effectively constrains the step size of policy updates by incorporating a Kullback-Leibler divergence constraint on the magnitude of parameter changes into the objective function and constructing a new objective function to clip the advantage function, thereby simplifying the problem-solving process. Experimental results show that, compared to traditional methods, our model achieves a throughput increase to 95.875 Mbit/s, reduces the packet capture rate to 22.12% decreases network latency to 42.57 ms, and converges overall in a more favorable direction.