文章基本信息

标题：Semi-supervised machine learning for primary user emulation attack detection and prevention through core-based analytics for cognitive radio networks
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作者：Sundar Srinivasan ; KB Shivakumar ; Muazzam Mohammad 等
期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2019
卷号：15
期号：9
页码：1
DOI：10.1177/1550147719860365
出版社：Hindawi Publishing Corporation
摘要：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.
关键词：Cognitive radio network; primary user emulation; dynamic spectrum sensing; unsupervised machine learning; supervised machine learning; semi-supervised machine learning; deep learning convolution network; feed forward neural network; reinforced machine learning
其他关键词：Cognitive radio network ; primary user emulation ; dynamic spectrum sensing ; unsupervised machine learning ; supervised machine learning ; semi-supervised machine learning ; deep learning convolution network ; feed forward neural network ; reinforced machine learning