Adaptive Honeypot Engagement through Reinforcement Learning of Semi-Markov Decision Processes

@inproceedings{Huang2019AdaptiveHE,
  title={Adaptive Honeypot Engagement through Reinforcement Learning of Semi-Markov Decision Processes},
  author={Linan Huang and Q. Zhu},
  booktitle={GameSec},
  year={2019}
}
  • Linan Huang, Q. Zhu
  • Published in GameSec 2019
  • Computer Science
  • A honeynet is a promising active cyber defense mechanism. It reveals the fundamental Indicators of Compromise (IoCs) by luring attackers to conduct adversarial behaviors in a controlled and monitored environment. The active interaction at the honeynet brings a high reward but also introduces high implementation costs and risks of adversarial honeynet exploitation. In this work, we apply infinite-horizon Semi-Markov Decision Process (SMDP) to characterize a stochastic transition and sojourn time… CONTINUE READING
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