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The research aims to improve the efficiency and real-time performance of software-defined honeypots in dynamic attack scenarios.

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

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The research aims to improve the efficiency and real-time performance of software-defined honeypots in dynamic attack scenarios.
Proposed a DPU-accelerated strategy for honeypot deployment using hardware acceleration.
Designed an adaptive adjustment strategy combining Stackelberg game theory with deep reinforcement learning.
Conducted simulation experiments to evaluate the effectiveness of the proposed mechanism.
80% of interaction rounds required no adjustments.
Achieved enhanced defense benefits in 20% of rounds with controlled adjustment costs.
Demonstrated stable defense benefits over long-term interactions compared to traditional methods.

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Simulation experiments show enhanced defense benefits and efficiency in honeypots, indicating effective cost management in cybersecurity.

Zhang et al. (2026) studied this question.

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