Countering attacker data manipulation in security games

• Main Authors: BUTLER, Andrew R., NGUYEN, Thanh H., SINHA, Arunesh
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/6564; https://ink.library.smu.edu.sg/context/sis_research/article/7567/viewcontent/Addressing_Partial_Adversarial_Deception_GameSec_1_.pdf
• Institution: Singapore Management University

. Defending against attackers with unknown behavior is an important area of research in security games. A well-established approach is to utilize historical attack data to create a behavioral model of the attacker. However, this presents a vulnerability: a clever attacker may change its own behavior during learning, leading to an inaccurate model and ineffective defender strategies. In this paper, we investigate how a wary defender can defend against such deceptive attacker. We provide four main contributions. First, we develop a new technique to estimate attacker true behavior despite data manipulation by the clever adversary. Second, we extend this technique to be viable even when the defender has access to a minimal amount of historical data. Third, we utilize a maximin approach to optimize the defender’s strategy against the worst-case within the estimate uncertainty. Finally, we demonstrate the effectiveness of our counterdeception methods by performing extensive experiments, showing clear gain for the defender and loss for the deceptive attacker.