Cross-Layer Attack and Defense in Cognitive Radio Networks

Abstract

The existing research on security issues in cognitive radio networks mainly focuses on attack and defense in individual network layers. However, the attackers do not necessarily restrict themselves within the boundaries of network layers. In this paper, we design cross-layer attack strategies that can largely increase the attackers' power or reducing their risk of being detected. As a case study, we investigate the coordinated report-false-sensing data attack (PHY layer) and small-back-off-window attack (MAC layer). Furthermore, we propose a trust-based cross-layer defense framework that relies on abnormal detection in individual layers and cross-layer trust fusion. Simulation results demonstrate that the proposed defense framework can significantly reduce the maximum damage caused by attackers.