{"title":"基于gpu的入侵检测系统高效模式匹配","authors":"Antonino Tumeo, Oreste Villa, D. Sciuto","doi":"10.1145/1787275.1787296","DOIUrl":null,"url":null,"abstract":"In this paper we present an efficient implementation of the Aho-Corasick pattern matching algorithm on Graphics Processing Units (GPU), showing how we redesigned the algorithm and the data structures to fit on the architecture and comparing it with an equivalent implementation on the CPU. We show that with a synthetic dataset, our implementation obtains a speedup up to 6.67 with respect to the CPU solution.","PeriodicalId":151791,"journal":{"name":"Proceedings of the 7th ACM international conference on Computing frontiers","volume":"24 2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"50","resultStr":"{\"title\":\"Efficient pattern matching on GPUs for intrusion detection systems\",\"authors\":\"Antonino Tumeo, Oreste Villa, D. Sciuto\",\"doi\":\"10.1145/1787275.1787296\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we present an efficient implementation of the Aho-Corasick pattern matching algorithm on Graphics Processing Units (GPU), showing how we redesigned the algorithm and the data structures to fit on the architecture and comparing it with an equivalent implementation on the CPU. We show that with a synthetic dataset, our implementation obtains a speedup up to 6.67 with respect to the CPU solution.\",\"PeriodicalId\":151791,\"journal\":{\"name\":\"Proceedings of the 7th ACM international conference on Computing frontiers\",\"volume\":\"24 2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-05-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"50\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 7th ACM international conference on Computing frontiers\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1787275.1787296\",\"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 7th ACM international conference on Computing frontiers","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1787275.1787296","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Efficient pattern matching on GPUs for intrusion detection systems
In this paper we present an efficient implementation of the Aho-Corasick pattern matching algorithm on Graphics Processing Units (GPU), showing how we redesigned the algorithm and the data structures to fit on the architecture and comparing it with an equivalent implementation on the CPU. We show that with a synthetic dataset, our implementation obtains a speedup up to 6.67 with respect to the CPU solution.
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