Mitigating SSDF attack using distance-based outlier approach in cognitive radio networks. (25th September 2019)

• Record Type: Journal Article
• Authors: Singh, Wangjam Niranjan; Marchang, Ningrinla; Taggu, Amar
• Is Part Of: International journal of ad hoc and ubiquitous computing. Volume 32:Number 2(2019)
• Page Start: 119
• Page End: 132
• Publication Date: 2019-09-25
• Subjects: SSDF attack -- distance-based outlier detection -- cognitive radio network -- data mining; Ubiquitous computing -- Periodicals; Embedded computer systems -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Wireless communication systems -- Periodicals; Computer architecture -- Periodicals; 004.2
• Languages: English
• ISSNs: 1743-8225

Abstract

Collaborative spectrum sensing is employed in cognitive radio networks for improving the spectrum sensing accuracy. The collaborating cognitive radios send their individual sensing results to the fusion center (FC) which aggregates the results to come to a final sensing decision. Malicious radios may adversely influence the final sensing decision by transmitting false spectrum sensing results to the FC. This attack is commonly known as the spectrum sensing data falsification (SSDF) attack. Hence, in the light of such a threat, it is pertinent for the FC to identify any such malicious radios, if any and isolate them from the decision process. In this paper, a distance-based outlier detection approach is proposed which mines the sensing reports at the FC for detection and isolation of such malicious users. Numerical simulations results support the validity of the proposed approach.