Game-Theoretic Perspectives and Algorithms for Cybersecurity

Abstract

Information plays a key role in many games, and game theory includes reasoning about how agents should perceive signals, and how they should strategically decide what signals to send. This can involve complex tradeoffs about how revealing certain information will affect the beliefs and actions of other players. I will overview some basic approaches for modeling information in game theory, such as signaling games, and applications to games such as Poker. The second part of the talk with focus on our work applying game theoretic models and algorithms in cybersecurity. I will discuss how we apply game theory to optimize strategies for deception in cybersecurity, including honeypots, honey traffic, and other deceptive objects. I will also cover work that considers dynamic deception using sequential models that capture uncertainty. Finally, I will discuss some recent work in adversarial learning and connections between this area and game theory.