On MMD-Based Secure Fusion Strategy for Robust Cooperative Spectrum Sensing

Abstract

Collaborative spectrum sensing (CSS) in cognitive radio-based networks (CRNs) is vulnerable to spectrum sensing data falsification (SSDF) attack. Existing defense mechanisms are commonly subject to major limitations with unrealistic assumptions which can be easily violated in future wireless networks. Such assumptions include the number of honest users is in majority, the attackers’ flip rates are identical and fixed, the adoption of hard decision approach in the fusion strategy and only TV sets are considered as the primary users to be protected. Essentially, all existing representative schemes utilize certain low-dimensional human-observed metric to distinguish malicious users and honest users based on domain knowledge. Therefore, these defense mechanisms cannot perform properly under certain condition, such as sensing reports with different distributions but have equal mean and variance. In this paper, we propose a secure fusion strategy which adopts “soft decision” method and can distinguish malicious users and honest users under any distribution of sensing reports using maximum mean discrepancy (MMD). Our proposed CSS scheme is suitable for general CRN application scenarios. The simulation results reflect the effects of different kernel functions and window size on the system performance, and show our proposed defense mechanism outperforms the existing works.