Intrusion detection and response model to enhance security in cognitive radio networks

Abstract

With the rapid proliferation of new technologies and services in the wireless domain, spectrum scarcity has become a major concern. Cognitive radios (CRs) arise as a promising solution to the scarcity of spectrum. A basic operation of the CRs is spectrum sensing. Whenever a primary signal is detected, CRs have to vacate the specific spectrum band. Malicious users can mimic incumbent transmitters so as to enforce CRs to vacate the specific band. Cognitive radio networks (CRNs) are expected to bring an evolution to the spectrum scarcity problem through intelligent use of the fallow spectrum bands. However, as CRNs are wireless in nature, they face all common security threats found in the traditional wireless networks. Common security combating measures for wireless environments consist of authorization, authentication, and access control. But CRNs face new security threats and challenges that have arisen due to their unique cognitive (self-configuration, self-healing, self-optimization, and self-protection) characteristics. Because of these new security threats, the use of traditional security combating measures would be inadequate to address the challenges. Consequently, this research work proposes an Intrusion Detection and Response Model (IDRM) to enhance security in cognitive radio networks. Intrusion detection monitors all the activities in order to detect the intrusion. It searches for security violation incidents, recognizes unauthorized accesses, and identifies information leakages. Unfortunately, system administrators neither can keep up with the pace that an intrusion detection system is delivering responses or alerts, nor can they react within adequate time limits. Therefore, an automatic response system has to take over this task by reacting without human intervention within the cognitive radio network.