{"title":"基于深度学习的对抗和无意碰撞检测","authors":"H. Nguyen, T. Vo-Huu, Triet Vo Huu, G. Noubir","doi":"10.1145/3324921.3328784","DOIUrl":null,"url":null,"abstract":"We introduce a set of techniques to achieve transfer learning from computer vision to RF spectrum analysis. In this paper, we demonstrate the usefulness of this approach to scale the learning, accuracy, and efficiency of detection of adversarial and unintentional communications collisions using VGG-16. We achieve high accuracy (94% collisions detected) on a DARPA Spectrum Collaboration Challenge (SC2) dataset.","PeriodicalId":435733,"journal":{"name":"Proceedings of the ACM Workshop on Wireless Security and Machine Learning","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Towards Adversarial and Unintentional Collisions Detection Using Deep Learning\",\"authors\":\"H. Nguyen, T. Vo-Huu, Triet Vo Huu, G. Noubir\",\"doi\":\"10.1145/3324921.3328784\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We introduce a set of techniques to achieve transfer learning from computer vision to RF spectrum analysis. In this paper, we demonstrate the usefulness of this approach to scale the learning, accuracy, and efficiency of detection of adversarial and unintentional communications collisions using VGG-16. We achieve high accuracy (94% collisions detected) on a DARPA Spectrum Collaboration Challenge (SC2) dataset.\",\"PeriodicalId\":435733,\"journal\":{\"name\":\"Proceedings of the ACM Workshop on Wireless Security and Machine Learning\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-05-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the ACM Workshop on Wireless Security and Machine Learning\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3324921.3328784\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ACM Workshop on Wireless Security and Machine Learning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3324921.3328784","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Towards Adversarial and Unintentional Collisions Detection Using Deep Learning
We introduce a set of techniques to achieve transfer learning from computer vision to RF spectrum analysis. In this paper, we demonstrate the usefulness of this approach to scale the learning, accuracy, and efficiency of detection of adversarial and unintentional communications collisions using VGG-16. We achieve high accuracy (94% collisions detected) on a DARPA Spectrum Collaboration Challenge (SC2) dataset.
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