Presenter(s): Alyssa Huque—Mathematics & Computer Science, Political Science
Faculty Mentor(s): Thanh Nguyen
Session: Prerecorded Poster Presentation
A common dilemma many security agencies face is how to effectively allocate limited resources to protect assets . This predicament is known as the Stackelberg Security Games . In order to optimize defense tactics, security agencies need to be able to anticipate adversarial behavior . Currently, there are computer modeling techniques that simulate the Stackelberg Security Games . However, these implementations are not completely optimized for human adversaries . The quantal response model (QR) operates under the assumption that humans act with perfect rationality, a flawed assumption that was improved upon in the SUQR model . The SUQR integrated a subjective utility function (SU) that could take learned parameters, but only from limited data (Nguyen, et . al 2013) . Deep neural networks have the potential to improve further than the SUQR by providing a better prediction of the attacker’s behavior . Deep neural networks can allow for a more robust set of input features that would be able to account for more nuances of human behavior . A model that could accurately predict adversarial actions has the potential to improve resource allocation and enhance the security of valuable assets .