Abstract
Primary User Emulation (PUE) attack is a type of Denial of Service (DoS) attack in Cognitive Wireless Sensor Network (CWSNs), where malicious secondary users (SU) try to emulate primary users (PUs) to maximize their own spectrum usage or obstruct other SUs from accessing the spectrum. In this paper we have used a classification algorithm to monitor and classify the different types of PU and SU signal profiles. Detection of an abnormal signal profile of any SU helps us to identify a PUE attacker in the network. Although the learning phase is computation-intensive, the signal classification phase of our application does not requires extensive computational capabilities and memory and is therefore suitable for use in resource constrained cognitive sensor nodes. We have implemented and tested our application on a CWSN simulator. Since it is not based on the positional information of nodes, so it is also suitable for mobile cognitive sensor networks.
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