A Reinforcement Learning Framework for Adaptive Privacy–Security Trade-off in Proxy Networks

Abstract

Proxy-based networks continue to serve as an essential part of anonymous communication, traffic routing, and security policy enforcement. However, with predefined security and privacy configurations, these networks find it difficult to respond to changing scenarios, emerging cyber threats, and user requirements. The paper proposes an adaptive solution to these issues by using reinforcement learning for proxy-based security and privacy optimization. In this scenario, proxy action choices are modeled as Markov decision processes. The RL agent selects the optimal strategies for privacy and security, such as encryption levels, traffic privacy, access mechanisms, and anomaly levels based on network state. The reward function can be optimized to maximize privacy and security and to eliminate any delays and computational complexity.

Its operation is demonstrated in an environment with mixed traffic, involving the presence of regular users as well as malicious traffic such as DoS/DDoS attacks, probing, spoofing, and data leakage. Results obtained from the experiment reveal that the solution employing the concept of reinforcement learning has a superior performance to that of the traditional proxy setup. It seems that this approach leads to enhanced accuracy in detecting attacks, improved adaptation times, and higher quality-of-service levels without any sacrifice in terms of privacy protected in a traditional manner.