A density based scheme to countermeasure spectrum sensing data falsification attacks in cognitive radio networks

Abstract

Cognitive radio networks are a promising solution to the spectrum scarcity issue. In cognitive radio networks, because of the low reliability of individual spectrum sensing by a single secondary user, cooperative spectrum sensing is critical to accurately detect the existence of a primary user signal. However, cooperative spectrum sensing is vulnerable to the spectrum sensing data falsification (SSDF) attack. Specifically, a malicious user can send a falsified sensing report to mislead other (benign) secondary users to make an incorrect decision on the PU activity. Therefore, detecting the SSDF attack or identifying the malicious sensing reports is extremely important for robust cooperative spectrum sensing. This paper proposes a distributed density based SSDF detection (DBSD) scheme to countermeasure the SSDF attack. DBSD can effectively exclude the malicious sensing reports from SSDF attackers, so that a benign secondary user can effectively detect the PU activity in distributed cooperative spectrum sensing. Furthermore, DBSD can also exclude abnormal sensing reports from ill-functioned secondary users. Simulation results show that DBSD achieves very good performance in cooperative spectrum sensing.