{"title":"基于混合深度神经网络的DDoS检测","authors":"Vanlalruata Hnamte, J. Hussain","doi":"10.1109/I2CT57861.2023.10126434","DOIUrl":null,"url":null,"abstract":"In this study, we provide Deep Neural Network (DNN) based approaches to detecting Distributed Denial-of-Service (DDoS) attacks. In order to improve the DNN’s accuracy, the suggested approaches use two different hybrid DNN scenario detections to demonstrate the possibilities. As training and testing data, we use the publicly available Intrusion Detection datasets; CIC-IDS2017 and CIC-DDoS2019. Experiments have shown that the presented approaches are 99.9% effective at detecting attacks.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DDoS Detection Using Hybrid Deep Neural Network Approaches\",\"authors\":\"Vanlalruata Hnamte, J. Hussain\",\"doi\":\"10.1109/I2CT57861.2023.10126434\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this study, we provide Deep Neural Network (DNN) based approaches to detecting Distributed Denial-of-Service (DDoS) attacks. In order to improve the DNN’s accuracy, the suggested approaches use two different hybrid DNN scenario detections to demonstrate the possibilities. As training and testing data, we use the publicly available Intrusion Detection datasets; CIC-IDS2017 and CIC-DDoS2019. Experiments have shown that the presented approaches are 99.9% effective at detecting attacks.\",\"PeriodicalId\":150346,\"journal\":{\"name\":\"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/I2CT57861.2023.10126434\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/I2CT57861.2023.10126434","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
DDoS Detection Using Hybrid Deep Neural Network Approaches
In this study, we provide Deep Neural Network (DNN) based approaches to detecting Distributed Denial-of-Service (DDoS) attacks. In order to improve the DNN’s accuracy, the suggested approaches use two different hybrid DNN scenario detections to demonstrate the possibilities. As training and testing data, we use the publicly available Intrusion Detection datasets; CIC-IDS2017 and CIC-DDoS2019. Experiments have shown that the presented approaches are 99.9% effective at detecting attacks.
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