A game-theoretic model for defending against malicious users in RecDroid

Abstract

RecDroid is a smartphone permission response recommendation system which utilizes the responses from expert users in the network to help inexperienced users. However, in such system, malicious users can mislead the recommendation system by providing untruthful responses. Although detection system can be deployed to detect the malicious users, and exclude them from recommendation system, there are still undetected malicious users that may cause damage to RecDroid. Therefore, relying on environment knowledge to detect the malicious users is not sufficient. In this work, we present a game-theoretic model to analyze the interaction (request/response) between RecDroid users and RecDroid system using a static Bayesian game formulation. In the game RecDroid system chooses the best response strategy to minimize its loss from malicious users. We analyze the game model and explain the Nash equilibrium in a static scenario under different conditions. Through the static game model we discuss the strategy that RecDroid can adopt to disincentivize attackers in the system, so that attackers are discouraged to perform malicious users attack. Finally, we discuss several game parameters and their impact on players' outcome.