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Securing Soft IPs against Hardware Trojan Insertion

机译:保护软IP免受硬件木马插入

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摘要

Due to the increasing complexity of hardware designs, third-party hardware Intellectual Property (IP) blocks are often incorporated in order to alleviate the burden on hardware designers. However, the prevalence use of third-party IPs has raised security concerns such as Trojans inserted by attackers. Hardware Trojans in these soft IPs are extremely difficult to detect through functional testing and no single detection methodology has been able to completely address this issue. Based on a Register-Transfer Level (RTL) and gate-level soft IP analysis method named Structural Checking, this dissertation presents a hardware Trojan detection methodology and tool by detailing the implementation of a Golden Reference Library for matching an unknown IP to a functionally similar Golden Reference. The matching result is quantified in percentages so that two different IPs with similar functions have a high percentage match. A match of the unknown IP to a whitelisted IP advances it to be identified with a known functionality while a match to a blacklisted IP causes it to be detected with Trojan. Examples are given on how this methodology can successfully identify hardware Trojans inserted in unknown third-party IPs. In addition to soft IPs analysis, Structural Checking provides data flow tracking capability to help users discover vulnerable nodes of the soft IPs. Structural Checking is implemented with a graphical user interface, so it does not take users much time to use the tool.
机译:由于硬件设计的复杂性不断增加,通常会合并第三方硬件知识产权(IP)块,以减轻硬件设计人员的负担。但是,第三方IP的普遍使用引发了安全性问题,例如攻击者插入的特洛伊木马。这些软IP中的硬件木马极其难以通过功能测试进行检测,并且没有一种检测方法能够完全解决此问题。基于寄存器传输级(RTL)和门级软IP分析方法(称为结构检查),本文通过详细描述用于将未知IP与功能相似的IP匹配的黄金参考库的实现,提出了一种硬件木马检测方法和工具。黄金参考。匹配结果以百分比进行量化,因此具有相似功能的两个不同IP的匹配百分比很高。如果将未知IP与列入白名单的IP进行匹配,则可以使用已知功能对其进行识别,而如果将其与列入黑名单的IP进行了匹配,则可以使用Trojan对其进行检测。给出了有关此方法如何成功识别插入未知第三方IP的硬件木马的示例。除软IP分析外,结构检查还提供数据流跟踪功能,以帮助用户发现软IP的易受攻击节点。结构检查是通过图形用户界面实现的,因此用户无需花费大量时间即可使用该工具。

著录项

  • 作者

    Le, Thao.;

  • 作者单位

    University of Arkansas.;

  • 授予单位 University of Arkansas.;
  • 学科 Computer engineering.
  • 学位 Ph.D.
  • 年度 2018
  • 页码 54 p.
  • 总页数 54
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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