CAREER: Untrusted Computing Base: Detecting and Removing Malicious Hardware

职业:不受信任的计算基础:检测和删除恶意硬件

基本信息

  • 批准号:
    0953014
  • 负责人:
  • 金额:
    $ 44.44万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2010
  • 资助国家:
    美国
  • 起止时间:
    2010-03-01 至 2015-02-28
  • 项目状态:
    已结题

项目摘要

Computer systems security is an arms race between defenders andattackers that has mainly been confined to softwaretechnologies. Increases in the complexity of hardware and the risingnumber of transistors per chip have created opportunities forhardware-based security threats. Among the most pernicious aremalicious hardware footholds inserted at design time, which anattacker can use as the basis of a computer system attack.This project explores of the feasibility of foothold attacks and afundamental design-time methodology for defending against them.First, this project looks at techniques for highlighting potentiallymalicious circuits in a design automatically. The basic algorithm,called dead circuit identification (DCI), analyzes hardwaredescription language source code and dynamic execution traces ofdesign verification tests to identify suspicious circuitry whoseresults do not impact the computation.The second aspect of this project is a system for removing suspiciouscircuits from a design automatically. The dead circuit elimination(DCE) tool removes suspicious circuits from a design by pushingpotentially malicious logic up to a higher layer where it can beanalyzed in more detail at runtime.The third aspect of this project is a new technique for generatingtest cases and perturbing existing test cases to search specificallyfor potentially malicious circuits.This project provides a pathway to detection and defense againstsecurity risks from hardware comprising attacks, making the enormousdisruptions possible with such attacks far more difficult than today.
计算机系统安全是防御者和拦截者之间的军备竞赛,主要局限于软件技术。硬件复杂性的增加和每个芯片上晶体管数量的增加为基于硬件的安全威胁创造了机会。其中最有害的是在设计时插入的恶意硬件立足点,攻击者可以将其用作计算机系统攻击的基础。本项目探索立足点攻击的可行性和基本的设计时方法来防御它们。首先,本项目着眼于自动突出设计中潜在恶性电路的技术。基本算法称为死电路识别(DCI),通过分析硬件描述语言源代码和设计验证测试的动态执行轨迹来识别不影响计算的可疑电路。本项目的第二个方面是自动从设计中去除可疑电路。死电路消除(DCE)工具通过将潜在的恶意逻辑推到更高层来移除设计中的可疑电路,在运行时可以对其进行更详细的分析。该项目的第三个方面是一种新技术,用于生成测试用例并扰乱现有测试用例以特定地搜索潜在的恶意电路。该项目提供了一种检测和防御包含攻击的硬件的安全风险的途径,使此类攻击可能造成的巨大中断比现在困难得多。

项目成果

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Samuel King其他文献

Prescribing Non-Opioid Drugs in End-Stage Kidney Disease
  • DOI:
    10.1016/j.jpainsymman.2017.08.014
  • 发表时间:
    2017-11-01
  • 期刊:
  • 影响因子:
  • 作者:
    Andrew Wilcock;Sarah Charlesworth;Robert Twycross;Anne Waddington;Olivia Worthington;Fliss E.M. Murtagh;Jenny Beavis;Samuel King;Mary Mihalyo;Aleksandra Kotlinska-Lemieszek
  • 通讯作者:
    Aleksandra Kotlinska-Lemieszek
A multi-kingdom genetic barcoding system for precise clone isolation
用于精确克隆分离的多域遗传条形码系统
  • DOI:
    10.1038/s41587-025-02649-1
  • 发表时间:
    2025-05-21
  • 期刊:
  • 影响因子:
    41.700
  • 作者:
    Soh Ishiguro;Kana Ishida;Rina C. Sakata;Minori Ichiraku;Ren Takimoto;Rina Yogo;Yusuke Kijima;Hideto Mori;Mamoru Tanaka;Samuel King;Shoko Tarumoto;Taro Tsujimura;Omar Bashth;Nanami Masuyama;Arman Adel;Hiromi Toyoshima;Motoaki Seki;Ju Hee Oh;Anne-Sophie Archambault;Keiji Nishida;Akihiko Kondo;Satoru Kuhara;Hiroyuki Aburatani;Ramon I. Klein Geltink;Takuya Yamamoto;Nika Shakiba;Yasuhiro Takashima;Nozomu Yachie
  • 通讯作者:
    Nozomu Yachie
Research Guide to Decision Support System National Cost Extracts
决策支持系统研究指南国家成本摘录
  • DOI:
  • 发表时间:
    2010
  • 期刊:
  • 影响因子:
    0
  • 作者:
    C. Phibbs;P. Barnett;Angela Fan;Cherisse Harden;Samuel King;J. Scott
  • 通讯作者:
    J. Scott
Developing Interprofessional Primary Care Teams: Alumni Evaluation of the Department of Veterans Affairs Centers of Excellence in Primary Care Education Program
发展跨专业初级保健团队:退伍军人事务部初级保健教育卓越中心的校友评估
Spatiotemporal modeling of molecular holograms
分子全息图的时空建模
  • DOI:
    10.1016/j.cell.2024.10.011
  • 发表时间:
    2024-12-26
  • 期刊:
  • 影响因子:
    42.500
  • 作者:
    Xiaojie Qiu;Daniel Y. Zhu;Yifan Lu;Jiajun Yao;Zehua Jing;Kyung Hoi Min;Mengnan Cheng;Hailin Pan;Lulu Zuo;Samuel King;Qi Fang;Huiwen Zheng;Mingyue Wang;Shuai Wang;Qingquan Zhang;Sichao Yu;Sha Liao;Chao Liu;Xinchao Wu;Yiwei Lai;Yinqi Bai
  • 通讯作者:
    Yinqi Bai

Samuel King的其他文献

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{{ truncateString('Samuel King', 18)}}的其他基金

SBIR Phase I: Adrenaline: A Browser-Based Platform for Mobile Enterprise Apps
SBIR 第一阶段:Adrenaline:基于浏览器的移动企业应用程序平台
  • 批准号:
    1315654
  • 财政年份:
    2013
  • 资助金额:
    $ 44.44万
  • 项目类别:
    Standard Grant
CT-ISG: An Architecture and Policies for Secure Network-facing Applications
CT-ISG:面向安全网络的应用程序的架构和策略
  • 批准号:
    0831212
  • 财政年份:
    2008
  • 资助金额:
    $ 44.44万
  • 项目类别:
    Standard Grant
CSR-PSCE, SM: Recording and Deterministically Replaying Shared-memory Multiprocessor Execution Efficiently
CSR-PSCE、SM:高效记录和确定性重放共享内存多处理器执行
  • 批准号:
    0834738
  • 财政年份:
    2008
  • 资助金额:
    $ 44.44万
  • 项目类别:
    Continuing Grant
+Collaborative Research: CPA-CSA:BlueChip: Security Defenses for Misbehaving Hardware
协作研究:CPA-CSA:BlueChip:行为不当硬件的安全防御
  • 批准号:
    0811268
  • 财政年份:
    2008
  • 资助金额:
    $ 44.44万
  • 项目类别:
    Continuing Grant

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SBIR Phase II: A Software Platform for Assessment of Untrusted Electronics
SBIR 第二阶段:用于评估不可信电子产品的软件平台
  • 批准号:
    2304533
  • 财政年份:
    2023
  • 资助金额:
    $ 44.44万
  • 项目类别:
    Standard Grant
CAREER: Dependable and Secure Machine Learning Acceleration from Untrusted Hardware
职业:来自不受信任的硬件的可靠且安全的机器学习加速
  • 批准号:
    2238873
  • 财政年份:
    2023
  • 资助金额:
    $ 44.44万
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CAREER: Secure Timing Architecture for Untrusted Edge Systems
职业:不受信任的边缘系统的安全时序架构
  • 批准号:
    2237485
  • 财政年份:
    2023
  • 资助金额:
    $ 44.44万
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    Continuing Grant
CAREER: Dependable and Secure Machine Learning Acceleration from Untrusted Hardware
职业:来自不受信任的硬件的可靠且安全的机器学习加速
  • 批准号:
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  • 财政年份:
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CAREER: Trustworthy Machine Learning from Untrusted Models
职业:从不可信模型中进行值得信赖的机器学习
  • 批准号:
    2405136
  • 财政年份:
    2023
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Resilient and Secure Edge Computing for Untrusted Distributed Systems
适用于不可信分布式系统的弹性且安全的边缘计算
  • 批准号:
    NI220100111
  • 财政年份:
    2022
  • 资助金额:
    $ 44.44万
  • 项目类别:
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SaTC: CORE: Medium: Secure outsourced analytics in untrusted clouds
SaTC:核心:中:不受信任的云中的安全外包分析
  • 批准号:
    2209194
  • 财政年份:
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  • 资助金额:
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STTR Phase I: A Software Platform for Assessment of Untrusted Electronics
STTR 第一阶段:用于评估不可信电子产品的软件平台
  • 批准号:
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职业:从不可信模型中进行值得信赖的机器学习
  • 批准号:
    1953893
  • 财政年份:
    2019
  • 资助金额:
    $ 44.44万
  • 项目类别:
    Continuing Grant
CAREER: Trustworthy Machine Learning from Untrusted Models
职业:从不可信模型中进行值得信赖的机器学习
  • 批准号:
    1846151
  • 财政年份:
    2019
  • 资助金额:
    $ 44.44万
  • 项目类别:
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