Phishing for Phools in the Internet of Things: Modeling One-to-Many Deception using Poisson Signaling Games

TL;DR

This paper models one-to-many deception in the Internet of Things using Poisson signaling games, providing a quantitative framework to understand and mitigate deception in cyberspace and IoT networks.

Contribution

It introduces a novel Poisson signaling game model that extends traditional signaling games to include multiple receivers, evidence, and unknown numbers, with analytical solutions.

Findings

Higher detection abilities enable crowd-defense tactics.

Poisson signaling games support equilibrium analysis of deception rates.

Potential application in defending IoT networks against botnet recruitment.

Abstract

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.