{"title":"扩展摘要:基于自学习布隆滤波器的ddos防御技术","authors":"C. Y. Tseung, Kam-pui Chow, X. Zhang","doi":"10.1109/ISI.2017.8004917","DOIUrl":null,"url":null,"abstract":"DDoS attack is still one of the major threats from Internet. We propose a new technique to mitigate different types of DDoS, combining and taking advantages of both machine learning algorithms and Bloom filter. We use machine learning to extract features of attacks, then use a customized Bloom filter to defend attacks based on selected features. We implemented and tested the performance of the proposed technique in a lab environment.","PeriodicalId":423696,"journal":{"name":"2017 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Extended abstract: Anti-DDoS technique using self-learning bloom filter\",\"authors\":\"C. Y. Tseung, Kam-pui Chow, X. Zhang\",\"doi\":\"10.1109/ISI.2017.8004917\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"DDoS attack is still one of the major threats from Internet. We propose a new technique to mitigate different types of DDoS, combining and taking advantages of both machine learning algorithms and Bloom filter. We use machine learning to extract features of attacks, then use a customized Bloom filter to defend attacks based on selected features. We implemented and tested the performance of the proposed technique in a lab environment.\",\"PeriodicalId\":423696,\"journal\":{\"name\":\"2017 IEEE International Conference on Intelligence and Security Informatics (ISI)\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE International Conference on Intelligence and Security Informatics (ISI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISI.2017.8004917\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Conference on Intelligence and Security Informatics (ISI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISI.2017.8004917","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Extended abstract: Anti-DDoS technique using self-learning bloom filter
DDoS attack is still one of the major threats from Internet. We propose a new technique to mitigate different types of DDoS, combining and taking advantages of both machine learning algorithms and Bloom filter. We use machine learning to extract features of attacks, then use a customized Bloom filter to defend attacks based on selected features. We implemented and tested the performance of the proposed technique in a lab environment.