TY - JOUR
T1 - A Complete Analysis on the Risk of Using Quantal Response
T2 - When Attacker Maliciously Changes Behavior under Uncertainty
AU - Nguyen, Thanh Hong
AU - Yadav, Amulya
N1 - Publisher Copyright:
© 2022 by the authors.
PY - 2022/12
Y1 - 2022/12
N2 - 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.
AB - 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.
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U2 - 10.3390/g13060081
DO - 10.3390/g13060081
M3 - Article
AN - SCOPUS:85144651517
SN - 2073-4336
VL - 13
JO - Games
JF - Games
IS - 6
M1 - 81
ER -