{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T12:16:22Z","timestamp":1762431382864,"version":"build-2065373602"},"reference-count":15,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2017,11,8]],"date-time":"2017-11-08T00:00:00Z","timestamp":1510099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Chinese National Science Foundation","award":["61501078 and 61231006"],"award-info":[{"award-number":["61501078 and 61231006"]}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["3132016208"],"award-info":[{"award-number":["3132016208"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Collaborative spectral sensing can fuse the perceived results of multiple cognitive users, and thus will improve the accuracy of perceived results. However, the multi-source features of the perceived results result in security problems in the system. When there is a high probability of a malicious user attack, the traditional algorithm can correctly identify the malicious users. However, when the probability of attack by malicious users is reduced, it is almost impossible to use the traditional algorithm to correctly distinguish between honest users and malicious users, which greatly reduces the perceived performance. To address the problem above, based on the \u03b2 function and the feedback iteration mathematical method, this paper proposes a malicious user identification algorithm under multi-channel cooperative conditions (\u03b2-MIAMC), which involves comprehensively assessing the cognitive user\u2019s performance on multiple sub-channels to identify the malicious user. Simulation results show under the same attack probability, compared with the traditional algorithm, the \u03b2-MIAMC algorithm can more accurately identify the malicious users, reducing the false alarm probability of malicious users by more than 20%. When the attack probability is greater than 7%, the proposed algorithm can identify the malicious users with 100% certainty.<\/jats:p>","DOI":"10.3390\/fi9040079","type":"journal-article","created":{"date-parts":[[2017,11,8]],"date-time":"2017-11-08T12:38:48Z","timestamp":1510144728000},"page":"79","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Malicious Cognitive User Identification Algorithm in Centralized Spectrum Sensing System"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7431-2790","authenticated-orcid":false,"given":"Jingbo","family":"Zhang","sequence":"first","affiliation":[{"name":"Information Science and Technology College, Dalian Maritime University, Dalian 116026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lili","family":"Cai","sequence":"additional","affiliation":[{"name":"Information Science and Technology College, Dalian Maritime University, Dalian 116026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shufang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Information Science and Technology College, Dalian Maritime University, Dalian 116026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,11,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liu, P., Qi, W., Yuan, E., Wei, L., and Zhao, Y. (2017). Full-Duplex Cooperative Sensing for Spectrum-Heterogeneous Cognitive Radio Networks. Sensors, 17.","DOI":"10.3390\/s17081773"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1180","DOI":"10.1109\/SURV.2014.021414.00066","article-title":"Cooperative Communications for Cognitive Radio Networks\u2014From Theory to Applications","volume":"16","author":"Chen","year":"2014","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Shinde, S.C., and Jadhav, A.N. (2016, January 20\u201321). Centralized Cooperative Spectrum Sensing with Energy Detecion in Cognitive Radio and Optimization. Proceedings of the 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, India.","DOI":"10.1109\/RTEICT.2016.7807980"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ye, F., Zhang, X., and Li, Y. (2016). Comprehensive Reputation-Based Security Mechanism against Dynamic SSDF Attack in Cognitive Radio Networks. Symmetry, 8.","DOI":"10.3390\/sym8120147"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wang, W., Li, H., Sun, Y., and Han, Z. (December, January 30). CatchIt: Detect Malicious Nodes in Collaborative Spectrum Sensing. Proceedings of the 2009 IEEE Global Telecommunications Conference (GLOBECOM 2009), Honolulu, HI, USA.","DOI":"10.1109\/GLOCOM.2009.5425380"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chen, R., Park, J.M., and Bian, K. (2008, January 13\u201318). Robust Distributed Spectrum Sensing in Cognitive Radio Networks. Proceedings of the IEEE 27th Conference on Computer Communications (INFOCOM 2008), Phoenix, AZ, USA.","DOI":"10.1109\/INFOCOM.2008.251"},{"key":"ref_7","unstructured":"Zhuo, J. (2016). Micro Blogging Malicious User Identification. [Master\u2019s Thesis, Beijing Jiaotong University]."},{"key":"ref_8","first-page":"80","article-title":"Research on Wind-Band Spectrum Sensing Based on Compression Sensing","volume":"5","author":"Li","year":"2014","journal-title":"J. Harbin Inst. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1049\/iet-com.2012.0082","article-title":"Robust collaborative spectrum sensing in the presence of deleterious users","volume":"7","author":"Arshad","year":"2013","journal-title":"IET Commun."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bae, S., So, J., and Kim, H. (2017). On Optimal Cooperative Sensing with Energy Detection in Cognitive Radio. Sensors, 17.","DOI":"10.3390\/s17092111"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1109\/SURV.2011.122211.00162","article-title":"A Survey on Security Threats and Detection Techniques in Cognitive Radio Networks","volume":"15","author":"Fragkiadakis","year":"2013","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sumathi, A.C., and Vidhyapriya, R. (2012, January 27\u201329). Security in Cognitive Radio Networks\u2014A Survey. Proceedings of the 12th International Conference on Intelligent Systems Design and Applications (ISDA), Kochi, India.","DOI":"10.1109\/ISDA.2012.6416522"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1109\/24.85443","article-title":"Characterization of beta, binomial, and Poisson distributions","volume":"40","author":"Ahmed","year":"1991","journal-title":"IEEE Trans. Reliab."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Vosoughi, A., Cavallaro, J.R., and Marshall, A. (2015, January 6\u201310). Robust Consensus-Based Cooperative Spectrum Sensing under Insistent Spectrum Sensing Data Falsification Attacks. Proceedings of the 2015 IEEE Global Communications Conference (GLOBECOM), San Diego, CA, USA.","DOI":"10.1109\/GLOCOM.2015.7417492"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Faradji, F., Rezaie, A.H., and Ziaratban, M. (October, January 16). A Morphological-Based License Plate Location. Proceedings of the 2007 IEEE International Conference on Image Processing, San Antonio, TX, USA.","DOI":"10.1109\/ICIP.2007.4378890"}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/9\/4\/79\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:48:39Z","timestamp":1760208519000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/9\/4\/79"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,11,8]]},"references-count":15,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2017,12]]}},"alternative-id":["fi9040079"],"URL":"https:\/\/doi.org\/10.3390\/fi9040079","relation":{},"ISSN":["1999-5903"],"issn-type":[{"type":"electronic","value":"1999-5903"}],"subject":[],"published":{"date-parts":[[2017,11,8]]}}}