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CloudCFI: Context-Sensitive and Incremental CFI in the Cloud Environment

机译:CloudCFI:云环境中的上下文敏感和增量CFI

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摘要

Control-Flow Integrity (CFI) is one of the most promising techniques against control-flow hijacking attacks. For Commercial Off-the-Shelf (COTS) binaries, a number of solutions provide coarse-grained CFI and thus are context-insensitive, while having the benefit of introducing a low runtime overhead. However, they can hardly defend against elaborately designed attacks due to the inaccuracy of the Control-Flow Graphs (CFGs). This paper presents CloudCFI, a context-sensitive and incremental CFI, which specifically makes full use of the characteristic of the cloud environment, where multiple instances of a software run on multiple virtual machines, and the control flow checking result from one software instance could be utilized to handle the control-hijacking occurred on other sibling instances. In CloudCFI, the accuracy of the control flow checking can be continuously increased to offer the incremental CFI, and a context-sensitive CFI policy is enforced to determine the validity of the control flow of the execution path through checking the entire execution path instead of the single edge or partial edges in the execution path. CloudCFI includes the static phase and the runtime phase respectively. Control-flow information and basic-block information is collected through emulation execution in the static phase, and the execution paths are tracked in runtime phase to collect process-tracking information. Next, it recovers the execution path by using basic-block information and process-tracking information, and checks the validity of the control flow by using the control-flow information. A prototype system is implemented and evaluated from several aspects using RIPE and SPEC benchmarks, as well as real-world cloud applications, Memcached and Redis. The evaluation results show that CloudCFI can defend against most common control-flow hijacking attacks. Meanwhile, it only introduces a low runtime performance overhead.
机译:控制流程完整性(CFI)是对控制流动劫持攻击的最有前途的技术之一。对于商业现成(COTS)二进制文件,许多解决方案提供了粗粒化的CFI,因此是上下文不区分敏感的,同时具有引入低运行时开销的益处。然而,由于控制流程图(CFGS)的不准确性,它们可能几乎无法防御精心设计的攻击。本文介绍了CloudCFI,一个上下文敏感和增量CFI,该CFI专门充分利用了云环境的特征,其中软件在多个虚拟机上运行的多个实例,以及来自一个软件实例的控制流程检查结果可以是用于处理其他兄弟实例上发生控制劫持。在CloudCFI中,可以持续增加控制流检查的准确性以提供增量CFI,并强制执行上下文敏感的CFI策略,以确定执行路径的控制流程的有效性,通过检查整个执行路径而不是检查执行路径执行路径中的单边或部分边缘。 CloudCFI分别包括静态相位和运行时相位。通过静态阶段中的仿真执行来收集控制流程信息和基本块信息,并且在运行时阶段跟踪执行路径以收集处理跟踪信息。接下来,它通过使用基本块信息和处理跟踪信息来恢复执行路径,并通过使用控制流信息检查控制流的有效性。使用成熟和规范基准的几个方面实现和评估原型系统,以及现实世界云应用程序,Memcached和Redis。评估结果表明,CloudCFI可以防御最常见的控制流程劫持攻击。同时,它只引入了低运行时性能开销。

著录项

  • 来源
    《Cloud Computing, IEEE Transactions on》 |2021年第3期|938-957|共20页
  • 作者单位

    Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Serv Comp Technol & Syst Lab Sch Comp Sci & Techn Cluster & Grid Comp Lab Big Data Secur Engn Res C Wuhan 430074 Peoples R China|Shenzhen Huazhong Univ Sci & Technol Res Inst Shenzhen 518057 Peoples R China;

    Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Serv Comp Technol & Syst Lab Sch Comp Sci & Techn Cluster & Grid Comp Lab Big Data Secur Engn Res C Wuhan 430074 Peoples R China;

    Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Serv Comp Technol & Syst Lab Sch Comp Sci & Techn Cluster & Grid Comp Lab Big Data Secur Engn Res C Wuhan 430074 Peoples R China;

    Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan Peoples R China|St Francis Xavier Univ Dept Comp Sci Antigonish NS B2G 2W5 Canada;

    Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Serv Comp Technol & Syst Lab Sch Comp Sci & Techn Cluster & Grid Comp Lab Big Data Secur Engn Res C Wuhan 430074 Peoples R China;

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

    Control-flow integrity; context-sensitive; cloud; entire execution path;

    机译:控制流程;上下文敏感;云;整个执行路径;

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