Research on Kill Chain Resource Allocation Optimization Based on Reinforcement Learning and Game Theory

This paper investigates a resource allocation optimization method based on reinforcement learning and game theory for resource management in kill chain tasks. By constructing a game-theoretic model to analyze the interaction between attackers and defenders and integrating deep reinforcement learning (DRL) algorithms for adaptive strategy optimization, this approach aims to enhance the efficiency of resource allocation for the defender. The proposed method leverages a combination of game theory’s strategic interaction modeling and reinforcement learning’s decision-making capability in uncertain environments. Simulation results demonstrate that the method effectively optimizes resource allocation in dynamic, adversarial scenarios, improving both defense success rate and resource utilization efficiency. Additionally, the performance of the method is compared with traditional heuristic algorithms, showing a clear improvement in terms of adaptability and resource management.