Defending Against Cooperative Attacks in Cooperative Spectrum Sensing

Accurate spectrum sensing is important in cognitive radio networks. False sensing results in either waste of spectrum or harmful interference to primary users. To improve accuracy, cooperative spectrum sensing, in which a set of secondary users cooperatively sense the presence of the primary user, has emerged. This technique, however, opens a window for malicious users and attackers, who may remotely or physically capture the sensors and manipulate the sensing reports. In this paper, we consider the attack model whereby the attacker injects self-consistent false data simultaneously, and propose a modified COI (combinatorial optimization identification) algorithm to defend against such attacks. We also provide a theorem that detection uncertainty may exist in cooperative spectrum sensing. We intensively evaluate our algorithm with simulations, and the results show that our algorithm is a good technique to complement an existing algorithm, called IRIS.