{"title":"一个用于异常检测和分析的数据流系统","authors":"A. Bara, Xinyu Niu, W. Luk","doi":"10.1109/FPT.2014.7082793","DOIUrl":null,"url":null,"abstract":"This paper proposes DeADA, a dataflow architecture incorporating an automated, unsupervised and online learning algorithm. Compared with 24 core software implementations, DeADA achieves up to 6.17 times lower data drop rate and 10.7 times higher power efficiency. More importantly, experimental results for the Heartbleed case study suggest that DeADA is capable of detecting unknown attacks under network speeds of at least 18Mbps, a feature which is essential for modern network intrusion detection.","PeriodicalId":6877,"journal":{"name":"2014 International Conference on Field-Programmable Technology (FPT)","volume":"63 1","pages":"276-279"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"A dataflow system for anomaly detection and analysis\",\"authors\":\"A. Bara, Xinyu Niu, W. Luk\",\"doi\":\"10.1109/FPT.2014.7082793\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes DeADA, a dataflow architecture incorporating an automated, unsupervised and online learning algorithm. Compared with 24 core software implementations, DeADA achieves up to 6.17 times lower data drop rate and 10.7 times higher power efficiency. More importantly, experimental results for the Heartbleed case study suggest that DeADA is capable of detecting unknown attacks under network speeds of at least 18Mbps, a feature which is essential for modern network intrusion detection.\",\"PeriodicalId\":6877,\"journal\":{\"name\":\"2014 International Conference on Field-Programmable Technology (FPT)\",\"volume\":\"63 1\",\"pages\":\"276-279\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 International Conference on Field-Programmable Technology (FPT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FPT.2014.7082793\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Conference on Field-Programmable Technology (FPT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FPT.2014.7082793","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A dataflow system for anomaly detection and analysis
This paper proposes DeADA, a dataflow architecture incorporating an automated, unsupervised and online learning algorithm. Compared with 24 core software implementations, DeADA achieves up to 6.17 times lower data drop rate and 10.7 times higher power efficiency. More importantly, experimental results for the Heartbleed case study suggest that DeADA is capable of detecting unknown attacks under network speeds of at least 18Mbps, a feature which is essential for modern network intrusion detection.
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