TY - JOUR
T1 - Low Resource-Reallocation Defense Strategies for Repeated Security Games With No Prior Knowledge and Limited Observability
AU - Zhu, Jin
AU - Zhang, Jinglong
AU - Ling, Qiang
AU - Dullerud, Geir E.
N1 - This work was supported in part by the National Key Research and Development Program of China under Grant 2018AAA0100802, and in part by the Anhui Provincial Natural Science Foundation under Grant 2008085MF198.
PY - 2023/12/1
Y1 - 2023/12/1
N2 - This article takes into account general repeated security games with no prior knowledge, i.e., the game payoffs and the attacker's behavior model are unknown and limited observability. Besides the traditional 'regret' criterion, 'reallocation times' is introduced as an additional criterion that provides a more comprehensive evaluation of the defense strategies. For such games, a novel random-walk perturbations with uniform exploration (RWP-UE) algorithm is proposed and we deduce the corresponding upper bound of the expected regret and expected reallocation times. Theoretical analysis shows that the RWP-UE algorithm achieves not only low regret with the same magnitude as existing achievements but also fewer reallocation times. Experiments are carried out against four types of attackers, and the results illustrate that the RWP-UE algorithm achieves superior performance.
AB - This article takes into account general repeated security games with no prior knowledge, i.e., the game payoffs and the attacker's behavior model are unknown and limited observability. Besides the traditional 'regret' criterion, 'reallocation times' is introduced as an additional criterion that provides a more comprehensive evaluation of the defense strategies. For such games, a novel random-walk perturbations with uniform exploration (RWP-UE) algorithm is proposed and we deduce the corresponding upper bound of the expected regret and expected reallocation times. Theoretical analysis shows that the RWP-UE algorithm achieves not only low regret with the same magnitude as existing achievements but also fewer reallocation times. Experiments are carried out against four types of attackers, and the results illustrate that the RWP-UE algorithm achieves superior performance.
KW - Low reallocation times
KW - no prior knowledge and limited observability
KW - random-walk perturbations with uniform exploration (RWP-UE)
KW - repeated security games
KW - theoretical upper bound
UR - http://www.scopus.com/inward/record.url?scp=85148416833&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85148416833&partnerID=8YFLogxK
U2 - 10.1109/TCDS.2023.3241364
DO - 10.1109/TCDS.2023.3241364
M3 - Article
AN - SCOPUS:85148416833
SN - 2379-8920
VL - 15
SP - 2156
EP - 2168
JO - IEEE Transactions on Cognitive and Developmental Systems
JF - IEEE Transactions on Cognitive and Developmental Systems
IS - 4
ER -