Catching Attacker(s) for Collaborative Spectrum Sensing in Cognitive Radio Systems: An Abnormality Detection Approach

Collaborative spectrum sensing, which collects local observations or decisions from multiple secondary users to make a decision by a fusion center, is an effective approach to alleviate the unreliability of single-user spectrum sensing. However, it is subject to the attack of malicious secondary user(s), which may send false reports. Therefore, it is necessary to detect potential attacker(s) and make attack-proof decisions for spectrum sensing. Many existing attacker detection schemes are based on the knowledge of the attacker's strategy and thus apply the Bayesian detection of attackers. However, in practical cognitive radio systems, the data fusion center typically does not know the attacker's strategy.