{"title":"利用机器学习方法检测流氓基站","authors":"Jian Jin, ChangLiang Lian, Ming Xu","doi":"10.1109/WOCC.2019.8770554","DOIUrl":null,"url":null,"abstract":"In this paper, a novel machine learning approach is proposed to identify the rogue base station in the mobile networks based on the key base station parameters. This paper also analyzes in detail the reasons and ways of rogue base station attack user equipment from the perspective of the 3GPP protocol standard. A well-known machine learning model “lightGBM” is used as the learning model. From the experiments, we can conclude that the proposed approach can reach the desired result.","PeriodicalId":285172,"journal":{"name":"2019 28th Wireless and Optical Communications Conference (WOCC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Rogue Base Station Detection Using A Machine Learning Approach\",\"authors\":\"Jian Jin, ChangLiang Lian, Ming Xu\",\"doi\":\"10.1109/WOCC.2019.8770554\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a novel machine learning approach is proposed to identify the rogue base station in the mobile networks based on the key base station parameters. This paper also analyzes in detail the reasons and ways of rogue base station attack user equipment from the perspective of the 3GPP protocol standard. A well-known machine learning model “lightGBM” is used as the learning model. From the experiments, we can conclude that the proposed approach can reach the desired result.\",\"PeriodicalId\":285172,\"journal\":{\"name\":\"2019 28th Wireless and Optical Communications Conference (WOCC)\",\"volume\":\"48 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-05-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 28th Wireless and Optical Communications Conference (WOCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WOCC.2019.8770554\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 28th Wireless and Optical Communications Conference (WOCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WOCC.2019.8770554","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Rogue Base Station Detection Using A Machine Learning Approach
In this paper, a novel machine learning approach is proposed to identify the rogue base station in the mobile networks based on the key base station parameters. This paper also analyzes in detail the reasons and ways of rogue base station attack user equipment from the perspective of the 3GPP protocol standard. A well-known machine learning model “lightGBM” is used as the learning model. From the experiments, we can conclude that the proposed approach can reach the desired result.
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