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A Complete Analysis on the Risk of Using Quantal Response: When Attacker Maliciously Changes Behavior under Uncertainty

Author

Listed:
  • Thanh Hong Nguyen

    (Department of Computer and Information Science, University of Oregon, Eugene, OR 97403, USA)

  • Amulya Yadav

    (College of Information Sciences and Technology, Pennsylvania State University, State College, PA 16801, USA)

Abstract
In security games, the defender often has to predict the attacker’s behavior based on some observed attack data. However, a clever attacker can intentionally change its behavior to mislead the defender’s learning, leading to an ineffective defense strategy. This paper investigates the attacker’s imitative behavior deception under uncertainty , in which the attacker mimics a (deceptive) Quantal Response behavior model by consistently playing according to a certain parameter value of that model, given that it is uncertain about the defender’s actual learning outcome. We have three main contributions. First, we introduce a new maximin-based algorithm to compute a robust attacker deception decision under uncertainty, given the defender is unaware of the attacker deception. Our polynomial algorithm is built via characterizing the decomposability of the attacker deception space as well optimal deception behavior of the attacker against the worst case of uncertainty. Second, we propose a new counter-deception algorithm to tackle the attacker’s deception. We theoretically show that there is a universal optimal defense solution, regardless of any private knowledge the defender has about the relation between their learning outcome and the attacker deception choice. Third, we conduct extensive experiments in various security game settings, demonstrating the effectiveness of our proposed counter-deception algorithms to handle the attacker manipulation.

Suggested Citation

  • Thanh Hong Nguyen & Amulya Yadav, 2022. "A Complete Analysis on the Risk of Using Quantal Response: When Attacker Maliciously Changes Behavior under Uncertainty," Games, MDPI, vol. 13(6), pages 1-24, December.
    • Handle: RePEc:gam:jgames:v:13:y:2022:i:6:p:81-:d:991565
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    References listed on IDEAS

    as
    1. McKelvey Richard D. & Palfrey Thomas R., 1995. "Quantal Response Equilibria for Normal Form Games," Games and Economic Behavior, Elsevier, vol. 10(1), pages 6-38, July.
    2. Zhuang, Jun & Bier, Vicki M. & Alagoz, Oguzhan, 2010. "Modeling secrecy and deception in a multiple-period attacker-defender signaling game," European Journal of Operational Research, Elsevier, vol. 203(2), pages 409-418, June.
    3. Bo An & Fernando Ordóñez & Milind Tambe & Eric Shieh & Rong Yang & Craig Baldwin & Joseph DiRenzo & Kathryn Moretti & Ben Maule & Garrett Meyer, 2013. "A Deployed Quantal Response-Based Patrol Planning System for the U.S. Coast Guard," Interfaces, INFORMS, vol. 43(5), pages 400-420, October.
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