Paper Info
Title
Mitigation Strategy Against Spectrum-Sensing Data Falsification Attack In Cognitive Radio Sensor Networks
Abstract
To detect the primary user's activity accurately in cognitive radio sensor networks, cooperative spectrum sensing is recommended to improve the sensing performance and the reliability of spectrum-sensing process. However, spectrum-sensing data falsification attack being launched by malicious users may lead to fatal mistake of global decision about spectrum availability at the fusion center. It is a tough task to mitigate the negative effect of spectrum-sensing data falsification attack and even eliminate these attackers from the network. In this article, we first discuss the randomly false attack model and analyze the effects of two classes of attacks, individual and collaborative, on the global sensing performance at the fusion center. Afterwards, a linear weighted combination scheme is designed to eliminate the effects of the attacks on the final sensing decision. By evaluating the received sensing result, each user can be assigned a weight related to impact factors, which includes result consistency degree and data deviation degree. Furthermore, an adaptive reputation evaluation mechanism is introduced to discriminate malicious and honest sensor node. The evaluation is conducted through simulations, and the results reveal the benefits of the proposed in aspect of mitigation of spectrum-sensing data falsification attack.
Year
DOI
Venue
2019
10.1177/1550147719870645
INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS
Keywords
Field
DocType
Spectrum sensing, reputation degree, data falsification attack, cognitive radio sensor networks
Computer science,Sensing data,Computer network,Cognitive radio sensor networks
Journal
Volume
Issue
ISSN
15
9
1550-1477
Citations 
PageRank 
References 
3
0.40
0
Authors
4
Name
Order
Citations
PageRank
Runze Wan130.40
Lixin Ding218825.22
Naixue Xiong32413194.61
Xing Zhou430.40