Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/329992 
Year of Publication: 
2022
Citation: 
[Journal:] Games [ISSN:] 2073-4336 [Volume:] 13 [Issue:] 6 [Article No.:] 81 [Year:] 2022 [Pages:] 1-24
Publisher: 
MDPI, Basel
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.
Subjects: 
security games
behavior models
deception
uncertainty
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
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