Jipeng Gan, Jun Wu, Pei Li, Zehao Chen, Zehao Chen, Jia Zhang, Jian-Duo He
{"title":"协同频谱感知拜占庭攻击的恶意利用","authors":"Jipeng Gan, Jun Wu, Pei Li, Zehao Chen, Zehao Chen, Jia Zhang, Jian-Duo He","doi":"10.1109/spawc51304.2022.9833978","DOIUrl":null,"url":null,"abstract":"Cooperative spectrum sensing (CSS) is crucial for cognitive radio (CR) to improve spectrum sensing performance. However, the cooperative paradigm is threatened by Byzantine attacks. To ensure the security and energy efficiency (EE) of CSS, in this paper, we propose a malicious exploitation algorithm. Firstly, we distinguish normal users (NUs) from malicious users (MUs) based on the historical performance of secondary users (SUs). Unlike most previous studies, we innovatively improve CSS detection performance by exploiting sensing information from MUs. In addition, we select specific SUs instead of all SUs in data fusion, which reduces the number of samples submitted by SUs to the fusion center (FC). Finally, we further introduce a sequential differential mechanism that substantially reduces samples to improve the EE of CSS. Finally, the numerical simulation results validate the effectiveness of our proposed algorithm.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Malicious Exploitation of Byzantine Attack for Cooperative Spectrum Sensing\",\"authors\":\"Jipeng Gan, Jun Wu, Pei Li, Zehao Chen, Zehao Chen, Jia Zhang, Jian-Duo He\",\"doi\":\"10.1109/spawc51304.2022.9833978\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Cooperative spectrum sensing (CSS) is crucial for cognitive radio (CR) to improve spectrum sensing performance. However, the cooperative paradigm is threatened by Byzantine attacks. To ensure the security and energy efficiency (EE) of CSS, in this paper, we propose a malicious exploitation algorithm. Firstly, we distinguish normal users (NUs) from malicious users (MUs) based on the historical performance of secondary users (SUs). Unlike most previous studies, we innovatively improve CSS detection performance by exploiting sensing information from MUs. In addition, we select specific SUs instead of all SUs in data fusion, which reduces the number of samples submitted by SUs to the fusion center (FC). Finally, we further introduce a sequential differential mechanism that substantially reduces samples to improve the EE of CSS. Finally, the numerical simulation results validate the effectiveness of our proposed algorithm.\",\"PeriodicalId\":423807,\"journal\":{\"name\":\"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)\",\"volume\":\"141 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/spawc51304.2022.9833978\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/spawc51304.2022.9833978","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Malicious Exploitation of Byzantine Attack for Cooperative Spectrum Sensing
Cooperative spectrum sensing (CSS) is crucial for cognitive radio (CR) to improve spectrum sensing performance. However, the cooperative paradigm is threatened by Byzantine attacks. To ensure the security and energy efficiency (EE) of CSS, in this paper, we propose a malicious exploitation algorithm. Firstly, we distinguish normal users (NUs) from malicious users (MUs) based on the historical performance of secondary users (SUs). Unlike most previous studies, we innovatively improve CSS detection performance by exploiting sensing information from MUs. In addition, we select specific SUs instead of all SUs in data fusion, which reduces the number of samples submitted by SUs to the fusion center (FC). Finally, we further introduce a sequential differential mechanism that substantially reduces samples to improve the EE of CSS. Finally, the numerical simulation results validate the effectiveness of our proposed algorithm.