{"title":"无线设备识别与辐射特征","authors":"V. Brik, Suman Banerjee, M. Gruteser, Sangho Oh","doi":"10.1145/1409944.1409959","DOIUrl":null,"url":null,"abstract":"We design, implement, and evaluate a technique to identify the source network interface card (NIC) of an IEEE 802.11 frame through passive radio-frequency analysis. This technique, called PARADIS, leverages minute imperfections of transmitter hardware that are acquired at manufacture and are present even in otherwise identical NICs. These imperfections are transmitter-specific and manifest themselves as artifacts of the emitted signals. In PARADIS, we measure differentiating artifacts of individual wireless frames in the modulation domain, apply suitable machine-learning classification tools to achieve significantly higher degrees of NIC identification accuracy than prior best known schemes.\n We experimentally demonstrate effectiveness of PARADIS in differentiating between more than 130 identical 802.11 NICs with accuracy in excess of 99%. Our results also show that the accuracy of PARADIS is resilient against ambient noise and fluctuations of the wireless channel.\n Although our implementation deals exclusively with IEEE 802.11, the approach itself is general and will work with any digital modulation scheme.","PeriodicalId":378295,"journal":{"name":"ACM/IEEE International Conference on Mobile Computing and Networking","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"768","resultStr":"{\"title\":\"Wireless device identification with radiometric signatures\",\"authors\":\"V. Brik, Suman Banerjee, M. Gruteser, Sangho Oh\",\"doi\":\"10.1145/1409944.1409959\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We design, implement, and evaluate a technique to identify the source network interface card (NIC) of an IEEE 802.11 frame through passive radio-frequency analysis. This technique, called PARADIS, leverages minute imperfections of transmitter hardware that are acquired at manufacture and are present even in otherwise identical NICs. These imperfections are transmitter-specific and manifest themselves as artifacts of the emitted signals. In PARADIS, we measure differentiating artifacts of individual wireless frames in the modulation domain, apply suitable machine-learning classification tools to achieve significantly higher degrees of NIC identification accuracy than prior best known schemes.\\n We experimentally demonstrate effectiveness of PARADIS in differentiating between more than 130 identical 802.11 NICs with accuracy in excess of 99%. Our results also show that the accuracy of PARADIS is resilient against ambient noise and fluctuations of the wireless channel.\\n Although our implementation deals exclusively with IEEE 802.11, the approach itself is general and will work with any digital modulation scheme.\",\"PeriodicalId\":378295,\"journal\":{\"name\":\"ACM/IEEE International Conference on Mobile Computing and Networking\",\"volume\":\"35 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-09-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"768\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM/IEEE International Conference on Mobile Computing and Networking\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1409944.1409959\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM/IEEE International Conference on Mobile Computing and Networking","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1409944.1409959","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Wireless device identification with radiometric signatures
We design, implement, and evaluate a technique to identify the source network interface card (NIC) of an IEEE 802.11 frame through passive radio-frequency analysis. This technique, called PARADIS, leverages minute imperfections of transmitter hardware that are acquired at manufacture and are present even in otherwise identical NICs. These imperfections are transmitter-specific and manifest themselves as artifacts of the emitted signals. In PARADIS, we measure differentiating artifacts of individual wireless frames in the modulation domain, apply suitable machine-learning classification tools to achieve significantly higher degrees of NIC identification accuracy than prior best known schemes.
We experimentally demonstrate effectiveness of PARADIS in differentiating between more than 130 identical 802.11 NICs with accuracy in excess of 99%. Our results also show that the accuracy of PARADIS is resilient against ambient noise and fluctuations of the wireless channel.
Although our implementation deals exclusively with IEEE 802.11, the approach itself is general and will work with any digital modulation scheme.