Malicious Cognitive User Identification Algorithm in Centralized Spectrum Sensing System

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 u...
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Autor*in:	Jingbo Zhang [verfasserIn] Lili Cai [verfasserIn] Shufang Zhang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	collaborative spectrum sensing spectrum-sensing false data β function feedback iteration false alarm probability of malicious users

Übergeordnetes Werk:	In: Future Internet - MDPI AG, 2010, 9(2017), 4, p 79
Übergeordnetes Werk:	volume:9 ; year:2017 ; number:4, p 79

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/fi9040079

Katalog-ID:	DOAJ000903426

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520			\|a 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 β function and the feedback iteration mathematical method, this paper proposes a malicious user identification algorithm under multi-channel cooperative conditions (β-MIAMC), which involves comprehensively assessing the cognitive user’s performance on multiple sub-channels to identify the malicious user. Simulation results show under the same attack probability, compared with the traditional algorithm, the β-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.
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allfields	10.3390/fi9040079 doi (DE-627)DOAJ000903426 (DE-599)DOAJb773c305e0124e0590e4cb0faa1a3477 DE-627 ger DE-627 rakwb eng T58.5-58.64 Jingbo Zhang verfasserin aut Malicious Cognitive User Identification Algorithm in Centralized Spectrum Sensing System 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 β function and the feedback iteration mathematical method, this paper proposes a malicious user identification algorithm under multi-channel cooperative conditions (β-MIAMC), which involves comprehensively assessing the cognitive user’s performance on multiple sub-channels to identify the malicious user. Simulation results show under the same attack probability, compared with the traditional algorithm, the β-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. collaborative spectrum sensing spectrum-sensing false data β function feedback iteration false alarm probability of malicious users Information technology Lili Cai verfasserin aut Shufang Zhang verfasserin aut In Future Internet MDPI AG, 2010 9(2017), 4, p 79 (DE-627)610604147 (DE-600)2518385-0 19995903 nnns volume:9 year:2017 number:4, p 79 https://doi.org/10.3390/fi9040079 kostenfrei https://doaj.org/article/b773c305e0124e0590e4cb0faa1a3477 kostenfrei https://www.mdpi.com/1999-5903/9/4/79 kostenfrei https://doaj.org/toc/1999-5903 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2017 4, p 79
spelling	10.3390/fi9040079 doi (DE-627)DOAJ000903426 (DE-599)DOAJb773c305e0124e0590e4cb0faa1a3477 DE-627 ger DE-627 rakwb eng T58.5-58.64 Jingbo Zhang verfasserin aut Malicious Cognitive User Identification Algorithm in Centralized Spectrum Sensing System 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 β function and the feedback iteration mathematical method, this paper proposes a malicious user identification algorithm under multi-channel cooperative conditions (β-MIAMC), which involves comprehensively assessing the cognitive user’s performance on multiple sub-channels to identify the malicious user. Simulation results show under the same attack probability, compared with the traditional algorithm, the β-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. collaborative spectrum sensing spectrum-sensing false data β function feedback iteration false alarm probability of malicious users Information technology Lili Cai verfasserin aut Shufang Zhang verfasserin aut In Future Internet MDPI AG, 2010 9(2017), 4, p 79 (DE-627)610604147 (DE-600)2518385-0 19995903 nnns volume:9 year:2017 number:4, p 79 https://doi.org/10.3390/fi9040079 kostenfrei https://doaj.org/article/b773c305e0124e0590e4cb0faa1a3477 kostenfrei https://www.mdpi.com/1999-5903/9/4/79 kostenfrei https://doaj.org/toc/1999-5903 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2017 4, p 79
allfields_unstemmed	10.3390/fi9040079 doi (DE-627)DOAJ000903426 (DE-599)DOAJb773c305e0124e0590e4cb0faa1a3477 DE-627 ger DE-627 rakwb eng T58.5-58.64 Jingbo Zhang verfasserin aut Malicious Cognitive User Identification Algorithm in Centralized Spectrum Sensing System 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 β function and the feedback iteration mathematical method, this paper proposes a malicious user identification algorithm under multi-channel cooperative conditions (β-MIAMC), which involves comprehensively assessing the cognitive user’s performance on multiple sub-channels to identify the malicious user. Simulation results show under the same attack probability, compared with the traditional algorithm, the β-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. collaborative spectrum sensing spectrum-sensing false data β function feedback iteration false alarm probability of malicious users Information technology Lili Cai verfasserin aut Shufang Zhang verfasserin aut In Future Internet MDPI AG, 2010 9(2017), 4, p 79 (DE-627)610604147 (DE-600)2518385-0 19995903 nnns volume:9 year:2017 number:4, p 79 https://doi.org/10.3390/fi9040079 kostenfrei https://doaj.org/article/b773c305e0124e0590e4cb0faa1a3477 kostenfrei https://www.mdpi.com/1999-5903/9/4/79 kostenfrei https://doaj.org/toc/1999-5903 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2017 4, p 79
allfieldsGer	10.3390/fi9040079 doi (DE-627)DOAJ000903426 (DE-599)DOAJb773c305e0124e0590e4cb0faa1a3477 DE-627 ger DE-627 rakwb eng T58.5-58.64 Jingbo Zhang verfasserin aut Malicious Cognitive User Identification Algorithm in Centralized Spectrum Sensing System 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 β function and the feedback iteration mathematical method, this paper proposes a malicious user identification algorithm under multi-channel cooperative conditions (β-MIAMC), which involves comprehensively assessing the cognitive user’s performance on multiple sub-channels to identify the malicious user. Simulation results show under the same attack probability, compared with the traditional algorithm, the β-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. collaborative spectrum sensing spectrum-sensing false data β function feedback iteration false alarm probability of malicious users Information technology Lili Cai verfasserin aut Shufang Zhang verfasserin aut In Future Internet MDPI AG, 2010 9(2017), 4, p 79 (DE-627)610604147 (DE-600)2518385-0 19995903 nnns volume:9 year:2017 number:4, p 79 https://doi.org/10.3390/fi9040079 kostenfrei https://doaj.org/article/b773c305e0124e0590e4cb0faa1a3477 kostenfrei https://www.mdpi.com/1999-5903/9/4/79 kostenfrei https://doaj.org/toc/1999-5903 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2017 4, p 79
allfieldsSound	10.3390/fi9040079 doi (DE-627)DOAJ000903426 (DE-599)DOAJb773c305e0124e0590e4cb0faa1a3477 DE-627 ger DE-627 rakwb eng T58.5-58.64 Jingbo Zhang verfasserin aut Malicious Cognitive User Identification Algorithm in Centralized Spectrum Sensing System 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 β function and the feedback iteration mathematical method, this paper proposes a malicious user identification algorithm under multi-channel cooperative conditions (β-MIAMC), which involves comprehensively assessing the cognitive user’s performance on multiple sub-channels to identify the malicious user. Simulation results show under the same attack probability, compared with the traditional algorithm, the β-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. collaborative spectrum sensing spectrum-sensing false data β function feedback iteration false alarm probability of malicious users Information technology Lili Cai verfasserin aut Shufang Zhang verfasserin aut In Future Internet MDPI AG, 2010 9(2017), 4, p 79 (DE-627)610604147 (DE-600)2518385-0 19995903 nnns volume:9 year:2017 number:4, p 79 https://doi.org/10.3390/fi9040079 kostenfrei https://doaj.org/article/b773c305e0124e0590e4cb0faa1a3477 kostenfrei https://www.mdpi.com/1999-5903/9/4/79 kostenfrei https://doaj.org/toc/1999-5903 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2017 4, p 79
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authorswithroles_txt_mv	Jingbo Zhang @@aut@@ Lili Cai @@aut@@ Shufang Zhang @@aut@@
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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 β function and the feedback iteration mathematical method, this paper proposes a malicious user identification algorithm under multi-channel cooperative conditions (β-MIAMC), which involves comprehensively assessing the cognitive user’s performance on multiple sub-channels to identify the malicious user. Simulation results show under the same attack probability, compared with the traditional algorithm, the β-MIAMC algorithm can more accurately identify the malicious users, reducing the false alarm probability of malicious users by more than 20%. 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callnumber-first	T - Technology
author	Jingbo Zhang
spellingShingle	Jingbo Zhang misc T58.5-58.64 misc collaborative spectrum sensing misc spectrum-sensing false data misc β function misc feedback iteration misc false alarm probability of malicious users misc Information technology Malicious Cognitive User Identification Algorithm in Centralized Spectrum Sensing System
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topic_title	T58.5-58.64 Malicious Cognitive User Identification Algorithm in Centralized Spectrum Sensing System collaborative spectrum sensing spectrum-sensing false data β function feedback iteration false alarm probability of malicious users
topic	misc T58.5-58.64 misc collaborative spectrum sensing misc spectrum-sensing false data misc β function misc feedback iteration misc false alarm probability of malicious users misc Information technology
topic_unstemmed	misc T58.5-58.64 misc collaborative spectrum sensing misc spectrum-sensing false data misc β function misc feedback iteration misc false alarm probability of malicious users misc Information technology
topic_browse	misc T58.5-58.64 misc collaborative spectrum sensing misc spectrum-sensing false data misc β function misc feedback iteration misc false alarm probability of malicious users misc Information technology
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title	Malicious Cognitive User Identification Algorithm in Centralized Spectrum Sensing System
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title_full	Malicious Cognitive User Identification Algorithm in Centralized Spectrum Sensing System
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title_sort	malicious cognitive user identification algorithm in centralized spectrum sensing system
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title_auth	Malicious Cognitive User Identification Algorithm in Centralized Spectrum Sensing System
abstract	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 β function and the feedback iteration mathematical method, this paper proposes a malicious user identification algorithm under multi-channel cooperative conditions (β-MIAMC), which involves comprehensively assessing the cognitive user’s performance on multiple sub-channels to identify the malicious user. Simulation results show under the same attack probability, compared with the traditional algorithm, the β-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.
abstractGer	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 β function and the feedback iteration mathematical method, this paper proposes a malicious user identification algorithm under multi-channel cooperative conditions (β-MIAMC), which involves comprehensively assessing the cognitive user’s performance on multiple sub-channels to identify the malicious user. Simulation results show under the same attack probability, compared with the traditional algorithm, the β-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.
abstract_unstemmed	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 β function and the feedback iteration mathematical method, this paper proposes a malicious user identification algorithm under multi-channel cooperative conditions (β-MIAMC), which involves comprehensively assessing the cognitive user’s performance on multiple sub-channels to identify the malicious user. Simulation results show under the same attack probability, compared with the traditional algorithm, the β-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.
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title_short	Malicious Cognitive User Identification Algorithm in Centralized Spectrum Sensing System
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