Throughput performance under primary user emulation attack in cognitive radio networks

2017 ◽  
Vol 30 (18) ◽  
pp. e3371 ◽  
Author(s):  
Kuldeep Yadav ◽  
Binod Prasad ◽  
Abhijit Bhowmick ◽  
Sanjay Dhar Roy ◽  
Sumit Kundu
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Primary User ◽  
Radio Networks ◽  
Throughput Performance ◽  
Primary User Emulation Attack ◽  
Primary User Emulation

scholarly journals Strategic Surveillance Against Primary User Emulation Attacks in Cognitive Radio Networks

2018 ◽  
Vol 4 (3) ◽  
pp. 582-596 ◽  
Author(s):  
Duc-Tuyen Ta ◽  
Nhan Nguyen-Thanh ◽  
Patrick Maille ◽  
Van-Tam Nguyen
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Primary User ◽  
Radio Networks ◽  
Primary User Emulation

scholarly journals Semi-supervised machine learning for primary user emulation attack detection and prevention through core-based analytics for cognitive radio networks

2019 ◽  
Vol 15 (9) ◽  
pp. 155014771986036 ◽  
Author(s):  
Sundar Srinivasan ◽  
KB Shivakumar ◽  
Muazzam Mohammad
Keyword(s):  
Machine Learning ◽  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Primary User ◽  
Radio Networks ◽  
Attack Detection ◽  
Supervised Machine Learning ◽  
Machine Language ◽  
Detection Accuracy ◽  
Primary User Emulation

Cognitive radio networks are software controlled radios with the ability to allocate and reallocate spectrum depending upon the demand. Although they promise an extremely optimal use of the spectrum, they also bring in the challenges of misuse and attacks. Selfish attacks among other attacks are the most challenging, in which a secondary user or an unauthorized user with unlicensed spectrum pretends to be a primary user by altering the signal characteristics. Proposed methods leverage advancement to efficiently detect and prevent primary user emulation future attack in cognitive radio using machine language techniques. In this paper novel method is proposed to leverage unique methodology which can efficiently handle during various dynamic changes includes varying bandwidth, signature changes etc… performing learning and classification at edge nodes followed by core nodes using deep learning convolution network. The proposed method is compared with that of two other state-of-art machine learning-based attack detection protocols and has found to significantly reduce the false alarm to secondary network, at the same time improve the overall detection accuracy at the primary network.

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