Partial adversarial behavior deception in security games

• Main Authors: NGUYEN, Thanh H., SINHA, Arunesh, HE, He
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Agent-based and Multi-agent Systems: Algorithmic Game Theory; Agent-based and Multi-agent Systems: Noncooperative Games; Machine Learning: Adversarial Machine Learning; Artificial Intelligence and Robotics; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/5534; https://ink.library.smu.edu.sg/context/sis_research/article/6537/viewcontent/IJCAI2020_AdversarialBehavior_1___2_.pdf
• Institution: Singapore Management University

Learning attacker behavior is an important research topic in security games as security agencies are often uncertain about attackers’ decision making. Previous work has focused on developing various behavioral models of attackers based on historical attack data. However, a clever attacker can manipu...