Hide and Seek: A Markov-Based Defense Strategy Against Off-Sensing Attack in Cognitive Radio Networks

In a cognitive radio-based network (CRN), secondary users (SUs) opportunistically access underutilized spectrum resources and stop utilizing these resources when licensed or primary users (PUs) reappear. However, this way of opportunistic spectrum access is susceptible to novel vulnerabilities. Recently, a new attack, off-sensing (OS), has shed light on a vulnerability in the Federal Communications Commission (FCC) policy of CRN, which affects the channel utilization of the victim SU by creating an illusion of a PU's presence. However, prior work on OS-attack considers a deterministic approach that is unrealistic and is futile to fortify against conventional defense techniques. In this paper, we propose a new random approach, the random-OS attack, which adapts to realistic scenarios and is difficult to detect using conventional techniques. Then, we model the interaction between the victim SU and attackers as a stochastic zero-sum Markov game and propose a novel safeguard approach based on the Markov decision process to defend the proposed attack, namely hide and seek. Finally, we introduce an OS-attack detection strategy, which utilizes the sensing history to detect the presence of attackers without violating any policy or design constraints and without any networking overhead. Mathematical analysis and extensive simulation results exhibit the superior performance of our proposed work and advent a direction in designing safeguard strategies without amending the current FCC policies.