Probabilistic spectrum sensing data falsification attack in cognitive radio networks

Abstract

Cognitive radio has emerged as a solution to the problem of spectrum scarcity in recent years. Spectrum sensing is a key task in cognitive radio systems that can be performed collaboratively. Although collaboration enhances the performance of spectrum sensing, it can put the cognitive network in a vulnerable position. The cognitive users may send falsified reports, hence degrade the performance of spectrum sensing. In this paper, we propose a flexible structure which enables the attacker to reconfigure the attack parameters based on the defense strategy employed at the fusion center (FC) adaptively. In particular, we consider a cognitive network in which the cognitive users send their observations in a quantized format. It is assumed that a soft-decision-based defense strategy is employed at FC to detect the attackers. The attacker maps its quantized observations onto the other quantization levels probabilistically and reports them to FC. The attacker’s objective is to perform probabilistic mapping such that the performance of FC degrades as much as possible. At the same time, the attacker considers the attack costs. We demonstrate that the proposed attack method leads to a convex linear programming problem. A low complexity algorithm is proposed to solve the problem. Simulation results are presented to show the effectiveness of the proposed scheme.