{"type":"ScholarlyArticle","title":"Defending Against Man-In-The-Middle Attack in Repeated Games","authors":[{"name":"Shuxin Li","slug":"shuxin-li","given":"Shuxin","family":"Li","affiliation":"School of Computer Science and Technology , Tianjin University , China"},{"name":"Xiaohong Li","slug":"xiaohong-li","given":"Xiaohong","family":"Li","affiliation":"School of Computer Science and Technology , Tianjin University , China"},{"name":"Jianye Hao","slug":"jianye-hao","given":"Jianye","family":"Hao","affiliation":"School of Computer Software , Tianjin University , China"},{"name":"Bo An","slug":"bo-an","given":"Bo","family":"An","affiliation":"School of Computer Science and Engineering , Nanyang Technological University , Singapore"},{"name":"Zhiyong Feng","slug":"zhiyong-feng","given":"Zhiyong","family":"Feng","affiliation":"School of Computer Software , Tianjin University , China"},{"name":"Kangjie Chen","slug":"kangjie-chen","given":"Kangjie","family":"Chen","affiliation":"Tianjin International Engineering Institute , Tianjin University , China"},{"name":"Chengwei Zhang","slug":"chengwei-zhang","given":"Chengwei","family":"Zhang","affiliation":"School of Computer Science and Technology , Tianjin University , China"}],"abstract":"The Man-in-the-Middle (MITM) attack has become widespread in networks nowadays. The MITM attack would cause serious information leakage and result in tremendous loss to users. Previous work applies game theory to analyze the MITM attack-defense problem and computes the optimal defense strategy to minimize the total loss. It assumes that all defenders are cooperative and the attacker know defenders' strategies beforehand. However, each individual defender is rational and may not have the incentive to cooperate. Furthermore, the attacker can hardly know defenders' strategies ahead of schedule in practice. To this end, we assume that all defenders are self-interested and model the MITM attack-defense scenario as a simultaneous-move game. Nash equilibrium is adopted as the solution concept which is proved to be always unique. Given the impracticability of computing Nash equilibrium directly, we propose practical adaptive algorithms for the defenders and the attacker to learn towards the unique Nash equilibrium through repeated interactions. Simulation results show that the algorithms are able to converge to Nash equilibrium strategy efficiently.","doi":"10.24963/ijcai.2017/523","url":"https://ijcai.org/proceedings/2017/523","pdf":"https://ijcai.org/proceedings/2017/0523.pdf","markdown":"https://ijcai.org/proceedings/2017/523.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/523","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":523,"pages":{"first":3742,"last":3748},"keywords":["Knowledge Representation, Reasoning, and Logic: Game Theory","Multidisciplinary Topics and Applications: AI&Security and Privacy","Machine Learning: Reinforcement Learning"],"videos":[],"references":[{"position":1,"raw":"[Albina et al., 2013] Miss. 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