{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:51:24Z","timestamp":1760244684328,"version":"build-2065373602"},"reference-count":28,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T00:00:00Z","timestamp":1669939200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000183","name":"ARO","doi-asserted-by":"publisher","award":["W911NF-20-1-0344","W911NF-21-1-0047"],"award-info":[{"award-number":["W911NF-20-1-0344","W911NF-21-1-0047"]}],"id":[{"id":"10.13039\/100000183","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Games"],"abstract":"<jats:p>In security games, the defender often has to predict the attacker\u2019s behavior based on some observed attack data. However, a clever attacker can intentionally change its behavior to mislead the defender\u2019s learning, leading to an ineffective defense strategy. This paper investigates the attacker\u2019s 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\u2019s 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/g13060081","type":"journal-article","created":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T06:29:09Z","timestamp":1670221749000},"page":"81","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Complete Analysis on the Risk of Using Quantal Response: When Attacker Maliciously Changes Behavior under Uncertainty"],"prefix":"10.3390","volume":"13","author":[{"given":"Thanh Hong","family":"Nguyen","sequence":"first","affiliation":[{"name":"Department of Computer and Information Science, University of Oregon, Eugene, OR 97403, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amulya","family":"Yadav","sequence":"additional","affiliation":[{"name":"College of Information Sciences and Technology, Pennsylvania State University, State College, PA 16801, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tambe, M. 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