Secure Spectrum Data Fusion with Presence of Massive Malicious Users

Abstract

AbstractMalicious users are critical threats to the collaborative spectrum sensing (CSS) in cognitive radio networks. Especially with increasing number of malicious users, the performance of conventional malicious user detection (MUD) algorithms degrades fiercely. In this paper, we consider the secure CSS issue in the scenario that large number of malicious users exist in CSS system. We propose a new MUD algorithm, which detects the malicious users in a binary hypothesis test fashion. Based on the MUD, we provide a secure CSS approach. It is proved that the proposed secure CSS approach is insensitive with the number of malicious users, and it achieves a favorable performance even when a large number of malicious users are present.