Designing Better Strategies against Human Adversaries in Network Security Games: Extended Abstract

Citation:

Rong Yang, Fei Fang, Albert Xin Jiang, Karthik Rajagopal, Milind Tambe, and Rajiv Maheswaran. 2012. “Designing Better Strategies against Human Adversaries in Network Security Games: Extended Abstract .” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS)(Short paper) .

Abstract:

In a Network Security Game (NSG), security agencies must allocate limited resources to protect targets embedded in a network, such as important buildings in a city road network. A recent line of work relaxed the perfect-rationality assumption of human adversary and showed significant advantages of incorporating the bounded rationality adversary models in non-networked security domains. Given that real-world NSG are often extremely complex and hence very difficult for humans to solve, it is critical that we address human bounded rationality when designing defender strategies. To that end, the key contributions of this paper include: (i) comprehensive experiments with human subjects using a web-based game that we designed to simulate NSGs; (ii) new behavioral models of human adversary in NSGs, which we train with the data collected from human experiments; (iii) new algorithms for computing the defender optimal strategy against the new models.
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