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Static program analysis assisted dynamic taint tracking for software vulnerability discovery

机译:静态程序分析辅助动态污点跟踪,以发现软件漏洞

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摘要

The evolution of computer science has exposed us to the growing gravity of security problems and threats. Dynamic taint analysis is a prevalent approach to protect a program from malicious behaviors, but fails to provide any information about the code which is not executed. This paper describes a novel approach to overcome the limitation of traditional dynamic taint analysis by integrating static analysis into the system and presents framework SDCF to detect software vulnerabilities with high code coverage. Our experiments show that SDCF is not only able to provide efficient runtime protection by introducing an overhead of 4.16x based on the taint tracing technique, but is also capable of discovering latent software vulnerabilities which have not been exploited, and achieve code coverage of more than 90%.
机译:计算机科学的发展使我们面临安全问题和威胁日益严重的问题。动态污点分析是一种保护程序免受恶意行为侵害的流行方法,但是无法提供有关未执行代码的任何信息。本文介绍了一种通过将静态分析集成到系统中来克服传统动态污点分析的局限性的新方法,并提出了SDCF框架来检测具有高代码覆盖率的软件漏洞。我们的实验表明,SDCF不仅可以通过引入基于污点跟踪技术的4.16x开销来提供有效的运行时保护,而且还能够发现尚未被利用的潜在软件漏洞,并实现超过90%。

著录项

  • 来源
    《Computers & mathematics with applications》 |2012年第2期|p.469-480|共12页
  • 作者单位

    School of Software, Shanghai Key Laboratory of Scalable Computing and Systems, Shanghai Jiao Tong University, China;

    School of Software, Shanghai Key Laboratory of Scalable Computing and Systems, Shanghai Jiao Tong University, China;

    School of Software, Shanghai Key Laboratory of Scalable Computing and Systems, Shanghai Jiao Tong University, China;

    School of Electronic Information and Electrical Engineering, Shanghai Key Laboratory of Scalable Computing and Systems, Shanghai Jiao Tong University, China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    taint analysis; software vulnerability; code coverage; data flow analysis;

    机译:污点分析;软件漏洞;代码覆盖率;数据流分析;

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