Pattern based anomalous user detection in cognitive radio networks

Cognitive radio (CR) provides the ability to sense the range of frequencies (spectrum) that are not utilized by the incumbent user (primary user) and to opportunistically use the unoccupied spectrum in a heterogeneous environment. This can use a collaborative spectrum sensing approach to detect the spectrum holes. However, this nature of the collaborative mechanism is vulnerable to security attacks and faulty observations communicated by the opportunistic users (secondary users). Detecting such malicious users in CR networks is challenging as the pattern of malicious behavior is unknown apriori. In this paper we present an unsupervised approach to detect those malicious users, utilizing the pattern of their historic behavior. Our evaluation reveals that the proposed scheme effectively detects the malicious data in the system and provides a robust framework for CR to operate in this environment.