Adaptive Data Fusion Approach for Secure Spectrum Sensing in Wireless Cognitive Radio Sensor Networks

Adaptive Data Fusion Approach for Secure Spectrum Sensing in Wireless Cognitive Radio Sensor Networks

Cognitive radio (CR) is a promising technology to solve the serious problem of spectrum scarcity. Cooperative spectrum sensing (CSS) is a key function of CR networks to overcome the effect of multipath fading, shadowing and hidden station problem. As many previous studies have shown, the reliability of the CSS is vulnerable to spectrum sensing data falsification (SSDF) attack. In this type of attack, some malicious CR sensors intentionally report falsified local spectrum sensing results to a data collector or fusion center (FC) and significantly disrupt the global sensing decision. Here, we present an adaptive fusion rule for secure CSS procedure. The main contribution is based on the estimation of attack population and applying the best fusion rule to enhance the CSS performance. We assume that the FC senses its surrounding area to perform spectrum sensing and then assigns a dynamic cooperative weight for each CR sensor to mitigate the destructive effect of the SSDF attack. We further assume that the FC can easily change its fusion rule based on the estimated value of attack population. Computer simulation results are provided to illustrate the performance improvement of the proposed adaptive fusion scheme against malicious SSDF attacks.

Cognitive radio (CR) is a promising technology to solve the serious problem of spectrum scarcity. Cooperative spectrum sensing (CSS) is a key function of CR networks to overcome the effect of multipath fading, shadowing and hidden station problem. As many previous studies have shown, the reliability of the CSS is vulnerable to spectrum sensing data falsification (SSDF) attack. In this type of attack, some malicious CR sensors intentionally report falsified local spectrum sensing results to a data collector or fusion center (FC) and significantly disrupt the global sensing decision. Here, we present an adaptive fusion rule for secure CSS procedure. The main contribution is based on the estimation of attack population and applying the best fusion rule to enhance the CSS performance. We assume that the FC senses its surrounding area to perform spectrum sensing and then assigns a dynamic cooperative weight for each CR sensor to mitigate the destructive effect of the SSDF attack. We further assume that the FC can easily change its fusion rule based on the estimated value of attack population. Computer simulation results are provided to illustrate the performance improvement of the proposed adaptive fusion scheme against malicious SSDF attacks.