Countering attacker data manipulation in security games
. 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...
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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2021
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| Subjects: | |
| 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 |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | . 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. |
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