A Deep Reinforcement Learning Approach to Time Delay Differential Game Deception Resource Deployment

Summary

This paper proposes a method for deploying cyber deception resources that accounts for time delays, using deep reinforcement learning with time-delay differential game theory. The authors model node security evolution in complex networks and apply proximal policy optimization (PPO) to find an optimal deception deployment strategy. Validation on a scale-free complex network showed the method tracked node evolution and attacker and defender strategies, with better stability and effectiveness than other methods.