{"title":"基于1d - cnn的安全PON光谱分析实时流氓ONU识别","authors":"Yanlong Li, Nan Hua, Chen Zhao, Haotao Wang, Ruijie Luo, Xiaoping Zheng","doi":"10.1364/OFC.2019.TU3B.3","DOIUrl":null,"url":null,"abstract":"We proposed a real-time optical spectrum analysis method with one-dimensional convolutional neural network to identify rogue ONUs in PON. Experimental results show that 100% rogue ONU identification accuracy is achieved within 12.6 milliseconds on average.","PeriodicalId":6704,"journal":{"name":"2019 Optical Fiber Communications Conference and Exhibition (OFC)","volume":"72 1","pages":"1-3"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Real-Time Rogue ONU Identification with 1D-CNN-Based Optical Spectrum Analysis for Secure PON\",\"authors\":\"Yanlong Li, Nan Hua, Chen Zhao, Haotao Wang, Ruijie Luo, Xiaoping Zheng\",\"doi\":\"10.1364/OFC.2019.TU3B.3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We proposed a real-time optical spectrum analysis method with one-dimensional convolutional neural network to identify rogue ONUs in PON. Experimental results show that 100% rogue ONU identification accuracy is achieved within 12.6 milliseconds on average.\",\"PeriodicalId\":6704,\"journal\":{\"name\":\"2019 Optical Fiber Communications Conference and Exhibition (OFC)\",\"volume\":\"72 1\",\"pages\":\"1-3\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-03-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 Optical Fiber Communications Conference and Exhibition (OFC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1364/OFC.2019.TU3B.3\",\"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 Optical Fiber Communications Conference and Exhibition (OFC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1364/OFC.2019.TU3B.3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Real-Time Rogue ONU Identification with 1D-CNN-Based Optical Spectrum Analysis for Secure PON
We proposed a real-time optical spectrum analysis method with one-dimensional convolutional neural network to identify rogue ONUs in PON. Experimental results show that 100% rogue ONU identification accuracy is achieved within 12.6 milliseconds on average.
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