CAREER: Language-based Security for Polymorphic Malware Protection
职业:基于语言的多态恶意软件保护安全
基本信息
- 批准号:1054629
- 负责人:
- 金额:$ 50.37万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2011
- 资助国家:美国
- 起止时间:2011-08-01 至 2017-07-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Viruses, worms, and other self-propagating malware remain significant ongoing security threats to almost all sectors of the nation's cyber-infrastructure, including government, business, and home consumers. The escalating rate of new malware appearances increasingly threatens to outpace the defense community's ability to maintain effective detection systems. This is in part because many malware detection algorithms identify malicious software based on syntactic features. Polymorphic malware continually evolves new syntaxes at it propagates, introducing hundreds or thousands of new syntaxes per day that implement the same malicious behavior. Discovering practical, scalable techniques for reliably detecting new polymorphic malware variants is therefore one of the most significant challenges currently facing the computer security industry.This project develops hybrid static-dynamic technologies that detect malware based on semantic rather than purely syntactic code features. Thus, malware is identified based on the meaning of its malicious programming rather than the syntax with which it implements it. Malicious payloads are identified by applying traditionally static code analyses to decrypted memory pages intercepted dynamically at runtime. A major goal of the project is to develop technologies that are scalable and practical for standard computer hardware and operating systems. This will allow wide-scale deployment of results, and help to protect the nation from distributed attacks that compromise large numbers of low-priority targets to attack higher-priority targets. Results from the research will lead to powerful new strategies, concepts, and practical tools that give defenders a significant new advantage in the virus-antivirus arms race, and improving the national cyber-infrastructure's resilience against cyber-attacks.
病毒、蠕虫和其他自我传播的恶意软件仍然是对国家网络基础设施几乎所有部门的重大持续安全威胁,包括政府、企业和家庭消费者。 新恶意软件出现的速度越来越快,威胁到国防界维持有效检测系统的能力。 这部分是因为许多恶意软件检测算法基于语法特征来识别恶意软件。 多态恶意软件在传播时不断发展新的语法,每天引入数百或数千种实现相同恶意行为的新语法。 因此,发现实用的,可扩展的技术,可靠地检测新的多态恶意软件变种是目前面临的最重大的挑战之一,计算机安全industry.This项目开发的混合静态动态技术,检测恶意软件的语义,而不是纯粹的语法代码功能的基础上。 因此,恶意软件是基于其恶意编程的含义而不是其实现的语法来识别的。通过将传统的静态代码分析应用于在运行时动态截取的解密的存储器页面来识别恶意有效载荷。 该项目的一个主要目标是开发适用于标准计算机硬件和操作系统的可扩展和实用的技术。 这将允许结果的大规模部署,并有助于保护国家免受分布式攻击,这些攻击危及大量低优先级目标以攻击高优先级目标。 研究结果将产生强大的新战略、概念和实用工具,为防御者在病毒-反病毒军备竞赛中提供重要的新优势,并提高国家网络基础设施对网络攻击的抵御能力。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Kevin Hamlen其他文献
Kevin Hamlen的其他文献
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{{ truncateString('Kevin Hamlen', 18)}}的其他基金
TWC: TTP Option: Medium: Collaborative: ENCORE - ENhanced program protection through COmpiler-REwriter cooperation
TWC:TTP 选项:中:协作:ENCORE - 通过 COmpiler-REwriter 合作增强程序保护
- 批准号:
1513704 - 财政年份:2015
- 资助金额:
$ 50.37万 - 项目类别:
Standard Grant
TC: Medium: Collaborative Research: Securing Web Advertisements: Fixing the Short-term Crisis and Addressing Long-term Challenges
TC:媒介:协作研究:保护网络广告:解决短期危机并应对长期挑战
- 批准号:
1065216 - 财政年份:2011
- 资助金额:
$ 50.37万 - 项目类别:
Standard Grant
EAGER: Secure Peer-to-peer Data Management
EAGER:安全的点对点数据管理
- 批准号:
0959096 - 财政年份:2009
- 资助金额:
$ 50.37万 - 项目类别:
Standard Grant
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