Adaptive Software-Defined Honeypot Strategy Using Stackelberg Game and Deep Reinforcement Learning with DPU Acceleration

Software-defined (SD) honeypots, as dynamic cybersecurity technologies, enhance defense efficiency through flexible resource allocation. However, traditional SD honeypots face latency and jitter issues under network fluctuations, while balancing adjustment costs with defense benefits remains challenging. This paper proposes a DPU-accelerated SD honeypot security service deployment method, leveraging DPU hardware acceleration to optimize network traffic processing and protocol parsing, thereby significantly improving honeypot environment construction efficiency and response real-time performance. For dynamic attack–defense scenarios, we design an adaptive adjustment strategy combining Stackelberg game theory with deep reinforcement learning (AASGRL). By calculating the expected defense benefits and adjustment costs of optimal honeypot deployment strategies, the approach dynamically determines the timing and scope of honeypot adjustments. Simulation experiments demonstrate that the mechanism requires no adjustments in 80% of interaction rounds, while achieving enhanced defense benefits in 20% of rounds with controlled adjustment costs. Compared to traditional methods, the AASGRL mechanism maintains stable defense benefits in long-term interactions, verifying its effectiveness in balancing low costs and high benefits against dynamic attacks. This work provides critical technical support for building adaptive proactive network defense systems.