Spectrum Sensing Data Falsification Attack in Cognitive Radio Networks: An Analytical Model for Evaluation and Mitigation of Performance Degradation
Cognitive Radio (CR) networks enable dynamic spectrum access and can significantly improve spectral efficiency. Cooperative Spectrum Sensing (CSS) exploits the spatial diversity between CR users to increase sensing accuracy. However, in a realistic scenario, the trustworthy of CSS is vulnerable to S...
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Amirkabir University of Technology
2018-06-01
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| Series: | AUT Journal of Electrical Engineering |
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| Online Access: | https://eej.aut.ac.ir/article_1979_cbdb531b13a25ba73caacb6553483adf.pdf |
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| author | A.A Sharifi M. Mofarreh-Bonab |
| author_facet | A.A Sharifi M. Mofarreh-Bonab |
| author_sort | A.A Sharifi |
| collection | DOAJ |
| description | Cognitive Radio (CR) networks enable dynamic spectrum access and can significantly improve spectral efficiency. Cooperative Spectrum Sensing (CSS) exploits the spatial diversity between CR users to increase sensing accuracy. However, in a realistic scenario, the trustworthy of CSS is vulnerable to Spectrum Sensing Data Falsification (SSDF) attack. In an SSDF attack, some malicious CR users deliberately report falsified local sensing results to a data collector or Fusion Center (FC) and, then, affect the global sensing decision. In the present study, we investigate an analytical model for a hard SSDF attack and propose a robust defense strategy against such an attack. We show that FC can apply learning and estimation methods to obtain the attack parameters and use a better defense strategy. We further assume a log-normal shadow fading wireless environment and discuss the attack parameters that can affect the strength of SSDF attack. Simulation results illustrate the effectiveness of the proposed defense method against SSDF attacks, especially when the malicious users are in the majority. |
| format | Article |
| id | doaj-art-0af62c5c976a4e3e9749b695a47be688 |
| institution | Kabale University |
| issn | 2588-2910 2588-2929 |
| language | English |
| publishDate | 2018-06-01 |
| publisher | Amirkabir University of Technology |
| record_format | Article |
| series | AUT Journal of Electrical Engineering |
| spelling | doaj-art-0af62c5c976a4e3e9749b695a47be6882025-08-20T03:26:43ZengAmirkabir University of TechnologyAUT Journal of Electrical Engineering2588-29102588-29292018-06-01501435010.22060/eej.2017.12528.50941979Spectrum Sensing Data Falsification Attack in Cognitive Radio Networks: An Analytical Model for Evaluation and Mitigation of Performance DegradationA.A Sharifi0M. Mofarreh-Bonab1Department of Electrical Engineering, University of Bonab, Bonab, IranDepartment of Electrical Engineering, University of Bonab, Bonab, IranCognitive Radio (CR) networks enable dynamic spectrum access and can significantly improve spectral efficiency. Cooperative Spectrum Sensing (CSS) exploits the spatial diversity between CR users to increase sensing accuracy. However, in a realistic scenario, the trustworthy of CSS is vulnerable to Spectrum Sensing Data Falsification (SSDF) attack. In an SSDF attack, some malicious CR users deliberately report falsified local sensing results to a data collector or Fusion Center (FC) and, then, affect the global sensing decision. In the present study, we investigate an analytical model for a hard SSDF attack and propose a robust defense strategy against such an attack. We show that FC can apply learning and estimation methods to obtain the attack parameters and use a better defense strategy. We further assume a log-normal shadow fading wireless environment and discuss the attack parameters that can affect the strength of SSDF attack. Simulation results illustrate the effectiveness of the proposed defense method against SSDF attacks, especially when the malicious users are in the majority.https://eej.aut.ac.ir/article_1979_cbdb531b13a25ba73caacb6553483adf.pdfcognitive radiocooperative spectrum sensingspectrum sensing data falsification attackmalicious user |
| spellingShingle | A.A Sharifi M. Mofarreh-Bonab Spectrum Sensing Data Falsification Attack in Cognitive Radio Networks: An Analytical Model for Evaluation and Mitigation of Performance Degradation AUT Journal of Electrical Engineering cognitive radio cooperative spectrum sensing spectrum sensing data falsification attack malicious user |
| title | Spectrum Sensing Data Falsification Attack in Cognitive Radio Networks: An Analytical Model for Evaluation and Mitigation of Performance Degradation |
| title_full | Spectrum Sensing Data Falsification Attack in Cognitive Radio Networks: An Analytical Model for Evaluation and Mitigation of Performance Degradation |
| title_fullStr | Spectrum Sensing Data Falsification Attack in Cognitive Radio Networks: An Analytical Model for Evaluation and Mitigation of Performance Degradation |
| title_full_unstemmed | Spectrum Sensing Data Falsification Attack in Cognitive Radio Networks: An Analytical Model for Evaluation and Mitigation of Performance Degradation |
| title_short | Spectrum Sensing Data Falsification Attack in Cognitive Radio Networks: An Analytical Model for Evaluation and Mitigation of Performance Degradation |
| title_sort | spectrum sensing data falsification attack in cognitive radio networks an analytical model for evaluation and mitigation of performance degradation |
| topic | cognitive radio cooperative spectrum sensing spectrum sensing data falsification attack malicious user |
| url | https://eej.aut.ac.ir/article_1979_cbdb531b13a25ba73caacb6553483adf.pdf |
| work_keys_str_mv | AT aasharifi spectrumsensingdatafalsificationattackincognitiveradionetworksananalyticalmodelforevaluationandmitigationofperformancedegradation AT mmofarrehbonab spectrumsensingdatafalsificationattackincognitiveradionetworksananalyticalmodelforevaluationandmitigationofperformancedegradation |