Phishing for Phools in the Internet of Things: Modeling One-to-Many Deception using Poisson Signaling Games
This provides a quantitative model for deception that could aid in policy-making, entrepreneurship, and technological design in cyberspace and IoT, though it is incremental as it builds on existing game theory frameworks.
The paper tackles the problem of modeling one-to-many deception in cyberspace and IoT by developing a Poisson signaling game, which extends traditional signaling games to include exogenous evidence, unknown receiver numbers, and multiple receiver types, and finds closed-form equilibrium solutions that characterize deception rates and show how higher-ability receivers can use crowd-defense tactics to protect lower-ability ones.
Strategic interactions ranging from politics and pharmaceuticals to e-commerce and social networks support equilibria in which agents with private information manipulate others which are vulnerable to deception. Especially in cyberspace and the Internet of things, deception is difficult to detect and trust is complicated to establish. For this reason, effective policy-making, profitable entrepreneurship, and optimal technological design demand quantitative models of deception. In this paper, we use game theory to model specifically one-to-many deception. We combine a signaling game with a model called a Poisson game. The resulting Poisson signaling game extends traditional signaling games to include 1) exogenous evidence of deception, 2) an unknown number of receivers, and 3) receivers of multiple types. We find closed-form equilibrium solutions for a subset of Poisson signaling games, and characterize the rates of deception that they support. We show that receivers with higher abilities to detect deception can use crowd-defense tactics to mitigate deception for receivers with lower abilities to detect deception. Finally, we discuss how Poisson signaling games could be used to defend against the process by which the Mirai botnet recruits IoT devices in preparation for a distributed denial-of-service attack.