Angle based malicious user detection for wideband cognitive radio network

The cognitive radio (CR) users usually lack global information about the usage of the current spectrum resource, which makes the CR network (CRN) vulnerable to all sorts of attacks by malicious user (MU). Therefore, substantial studies have been focused on the attack-proof collaborative spectrum sensing schemes in the narrow-band environment. However, the high dimensional data obtained in the wideband spectrum sensing leads to the problem of "the curse of dimensionality". To solve this problem, we study the nature of spectrum sensing data falsification (SSDF) attacks and propose an angle based malicious user detection (ABMUD) to identify the MUs. In this scheme, we not only employ the distance between CR users in full detection space, but also the directions of distance vectors. The simulation results show that the proposed ABMUD algorithm can detect SSDF independent attacks very well.