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Low-level system software support for information security

Low-level system software support for information security
信息安全底层系统软件支撑
批准号:
293209-2007
负责人:
Lie, David
金额:
$1.82万
依托单位:
依托单位国家:
加拿大
项目类别:
Discovery Grants Program - Individual
财政年份:
2007
资助国家:
加拿大
项目状态:
已结题
起止时间:
2007-01-01 至 2008-12-31

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中文摘要
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英文摘要
Computer security is of paramount importance in our highly technology-reliant society.  Many people rely on computer systems to conduct business, communicate with friends and family, and manage their personal information.  Unfortunately, the safety and security of our current computing systems is not adequate for amount of trust people place in them.  The proposed research aims to improve the security of our computer systems by securing the foundation upon which they run, which is composed of low-level systems software such as the operating system and the software layers below it.  Securing this low-level software will not only make our computing infrastructure secure, but will reduce the cost of adoption by leaving higher-level software such as applications and servers intact.This proposed research will achieve these goals by exploring mechanisms and techniques that can be incorporated into two low-level systems layers: operating system kernels and virtual machine monitors (VMM).  The VMM is software layer that runs underneath the operating system kernel.  The proposal will aim to improve computer system security through three main thrusts of research.  One thrust will explore the use of VMMs to create secure containers that will be able to protect critical software and data from attack.  Another thrust will explore the use of VMMs to detect and eradicate malicious software that has infected critical software on a system, including the operating system.  The final thrust will leverage current commodity multi-core processors to allow the operating system kernel to better fend off attacks.  This research will produce a more secure foundation for our current computing infrastructure, thus allowing businesses, individuals and governments to have more confidence in the safety and security of their computing systems.
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A Machine Learning Approach to Detecting Security Vulnerabilities in Software.
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