Multistage Signaling Game-Based Optimal Detection Strategies for Suppressing Malware Diffusion in Fog-Cloud-Based IoT Networks

Abstract

We consider the Internet of Things (IoT) with malware diffusion and seek optimal malware detection strategies for preserving the privacy of smart objects in IoT networks and suppressing malware diffusion. To this end, we propose a malware detection infrastructure realized by an intrusion detection system (IDS) with cloud and fog computing to overcome the IDS deployment problem in smart objects due to their limited resources and heterogeneous subnetworks. We then employ a signaling game to disclose interactions between smart objects and the corresponding fog node because of malware uncertainty in smart objects. To minimize privacy leakage of smart objects, we also develop optimal strategies that maximize malware detection probability by theoretically computing the perfect Bayesian equilibrium of the game. Moreover, we analyze the factors influencing the optimal probability of a malicious smart object diffusing malware, and factors influencing the performance of a fog node in determining an infected smart object. Finally, we present a framework to demonstrate a potential and practical application of suppressing malware diffusion in IoT networks.