{"title":"Security Applications of GPUs","authors":"G. Vasiliadis","doi":"10.5772/INTECHOPEN.81885","DOIUrl":null,"url":null,"abstract":"Despite the recent advances in software security hardening techniques, vulner-abilities can always be exploited if the attackers are really determined. Regardless the protection enabled, successful exploitation can always be achieved, even though admittedly, today, it is much harder than it was in the past. Since securing software is still under ongoing research, the community investigates detection methods in order to protect software. Three of the most promising such methods are monitoring the (i) network, (ii) the filesystem, and (iii) the host memory, for possible exploitation. Whenever a malicious operation is detected then the monitor should be able to terminate it and/or alert the administrator. In this chapter, we explore how to utilize the highly parallel capabilities of modern commodity graphics processing units (GPUs) in order to improve the performance of different security tools operating at the network, storage, and memory level, and how they can offload the CPU whenever possible. Our results show that modern GPUs can be very efficient and highly effective at accelerating the pattern matching operations of network intrusion detection systems and antivirus tools, as well as for monitoring the integrity of the base computing systems.","PeriodicalId":20515,"journal":{"name":"Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing","volume":"43 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5772/INTECHOPEN.81885","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Despite the recent advances in software security hardening techniques, vulner-abilities can always be exploited if the attackers are really determined. Regardless the protection enabled, successful exploitation can always be achieved, even though admittedly, today, it is much harder than it was in the past. Since securing software is still under ongoing research, the community investigates detection methods in order to protect software. Three of the most promising such methods are monitoring the (i) network, (ii) the filesystem, and (iii) the host memory, for possible exploitation. Whenever a malicious operation is detected then the monitor should be able to terminate it and/or alert the administrator. In this chapter, we explore how to utilize the highly parallel capabilities of modern commodity graphics processing units (GPUs) in order to improve the performance of different security tools operating at the network, storage, and memory level, and how they can offload the CPU whenever possible. Our results show that modern GPUs can be very efficient and highly effective at accelerating the pattern matching operations of network intrusion detection systems and antivirus tools, as well as for monitoring the integrity of the base computing systems.
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gpu的安全应用
尽管最近在软件安全强化技术方面取得了进展,但如果攻击者确实确定,漏洞总是可以被利用的。不管启用了保护,成功的利用总是可以实现的,尽管不可否认,今天,它比过去困难得多。由于保护软件的研究仍在进行中,社区研究检测方法以保护软件。这类方法中最有前途的三种是监视(i)网络、(ii)文件系统和(iii)主机内存,以防止可能的利用。每当检测到恶意操作时,监视器应该能够终止它和/或向管理员发出警报。在本章中,我们将探讨如何利用现代商品图形处理单元(gpu)的高度并行能力,以提高在网络、存储和内存级别上运行的不同安全工具的性能,以及它们如何尽可能卸载CPU。我们的研究结果表明,现代gpu在加速网络入侵检测系统和反病毒工具的模式匹配操作以及监控基础计算系统的完整性方面可以非常高效和高效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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