Publication Type
Conference Proceeding Article
Publication Date
10-2021
Abstract
. 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.
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of conference on decision and game theory for security (GameSec)
First Page
1
Last Page
20
City or Country
Online
Citation
BUTLER, Andrew R.; NGUYEN, Thanh H.; and SINHA, Arunesh.
Countering attacker data manipulation in security games. (2021). Proceedings of conference on decision and game theory for security (GameSec). 1-20.
Available at: https://ink.library.smu.edu.sg/sis_research/6564
Creative Commons License
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