Semi-supervised machine learning for primary user emulation attack detection and prevention through core-based analytics for cognitive radio networks
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...
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| Format: | Article |
| Language: | English |
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Wiley
2019-09-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147719860365 |
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| _version_ | 1850230290022137856 |
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| author | Sundar Srinivasan KB Shivakumar Muazzam Mohammad |
| author_facet | Sundar Srinivasan KB Shivakumar Muazzam Mohammad |
| author_sort | Sundar Srinivasan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-20c5b47947f140db91cdc3ec7c5452ac |
| institution | OA Journals |
| issn | 1550-1477 |
| language | English |
| publishDate | 2019-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-20c5b47947f140db91cdc3ec7c5452ac2025-08-20T02:03:55ZengWileyInternational Journal of Distributed Sensor Networks1550-14772019-09-011510.1177/1550147719860365Semi-supervised machine learning for primary user emulation attack detection and prevention through core-based analytics for cognitive radio networksSundar Srinivasan0KB Shivakumar1Muazzam Mohammad2Department of ECE, Mewar University, Chittorgarh, IndiaDepartment of TCE, Sri Siddhartha Institute of Technology, Tumakuru, IndiaMewar University, Chittorgarh, IndiaCognitive 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.https://doi.org/10.1177/1550147719860365 |
| spellingShingle | Sundar Srinivasan KB Shivakumar Muazzam Mohammad Semi-supervised machine learning for primary user emulation attack detection and prevention through core-based analytics for cognitive radio networks International Journal of Distributed Sensor Networks |
| title | Semi-supervised machine learning for primary user emulation attack detection and prevention through core-based analytics for cognitive radio networks |
| title_full | Semi-supervised machine learning for primary user emulation attack detection and prevention through core-based analytics for cognitive radio networks |
| title_fullStr | Semi-supervised machine learning for primary user emulation attack detection and prevention through core-based analytics for cognitive radio networks |
| title_full_unstemmed | Semi-supervised machine learning for primary user emulation attack detection and prevention through core-based analytics for cognitive radio networks |
| title_short | Semi-supervised machine learning for primary user emulation attack detection and prevention through core-based analytics for cognitive radio networks |
| title_sort | semi supervised machine learning for primary user emulation attack detection and prevention through core based analytics for cognitive radio networks |
| url | https://doi.org/10.1177/1550147719860365 |
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