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This paper studies reinforcement learning (RL) under malicious falsification on cost signals and introduces a quantitative framework of attack models to understand the vulnerabilities of RL.
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Pawlick, J., Chen, J., Zhu, Q.: istrict: An interdependent strategic trust mechanism for the cloud-enabled internet of controlled things. IEEE Transactions on Information Forensics and Security (2018)
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Pawlick, J., Colbert, E., Zhu, Q.: Modeling and analysis of leaky deception using signaling games with evidence. IEEE Transactions on Information Forensics and Security 14
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Zhu, Q., Basar, T.: Game-theoretic methods for robustness, security, and resilience of cyberphysical control systems: games-in-games principle for optimal cross-layer resilient control systems. IEEE Control Systems Magazine 35
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Everitt, T., Krakovna, V., Orseau, L., Legg, S.: Reinforcement learning with a corrupted reward channel. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence. pp. 4705–4713. AAAI Press (2017)
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Horák, K., Zhu, Q., Bošanskỳ, B.: Manipulating adversary’s belief: A dynamic game approach to deception by design for proactive network security. In: International Conference on Decision and Game Theory for Security. pp. 273–294. Springer (2017)
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Pawlick, J., Zhu, Q.: Strategic trust in cloud-enabled cyber-physical systems with an application to glucose control. IEEE Transactions on Information Forensics and Security 12
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Rass, S., Alshawish, A., Abid, M.A., Schauer, S., Zhu, Q., De Meer, H.: Physical intrusion games–optimizing surveillance by simulation and game theory. IEEE Access 5
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Zhang, R., Zhu, Q.: A game-theoretic approach to design secure and resilient distributed support vector machines. IEEE transactions on neural networks and learning systems 29
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Behzadan, V., Munir, A.: Adversarial reinforcement learning framework for benchmarking collision avoidance mechanisms in autonomous vehicles. IEEE Transactions on Intelligent Transportation Systems (2019)
2019
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Pawlick, J., Colbert, E., Zhu, Q.: A game-theoretic taxonomy and survey of defensive deception for cybersecurity and privacy. ACM Computing Surveys (2019, to appear)
2019
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Pawlick, J., Nguyen, T.T.H., Colbert, E., Zhu, Q.: Optimal timing in dynamic and robust attacker engagement during advanced persistent threats. In: 2019 17th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt). pp. 1–6. IEEE (2019)
2019
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