ERI: EMRadar: A Practical Sensing System on the Electromagnetic Side-Channel of IoT
ERI:EMRadar:物联网电磁侧信道的实用传感系统
基本信息
- 批准号:2347409
- 负责人:
- 金额:$ 19.75万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2024
- 资助国家:美国
- 起止时间:2024-10-01 至 2026-09-30
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
The Internet of Things (IoT) has revolutionized daily life, yet securing IoT devices remains a significant challenge due to limited resources on IoT devices and the lack of visibility into their internal operations once deployed. Unlike conventional side-channel vulnerabilities, the electromagnetic (EM) side-channel emission leaks information over wireless channel, offering a unique medium to investigate IoT security without accessing or instrumenting the device. Unfortunately, current research on the EM side-channel attacks of IoT devices has been severely limited by the lack of a practical sensing system that can extract weak EM signals emitted by computer processors or memories, separate those EM signals of co-located devices, and robustly unravel their semantics. To tackle these challenges, this project will design, implement, and evaluate a practical EM sensing system named EMRadar to enable research on EM side-channel attacks and defenses in real environments. The results of this project will deepen our understanding of EM side-channel attacks and inform emission security standards. Practical EM side-channel defenses enabled by this project will transform various security-critical IoT applications, including detecting program deviations in medical devices that are highly resource-constrained, such as the implantable cardiac devices and the smart insulin pumps. The project is dedicated to fostering diversity and inclusion in STEM by blending research with education and outreach activities. The PI will actively involve local high school students by providing hands-on activities and presentations elucidating fundamental concepts of IoT technologies and cybersecurity. To bridge the gap in STEM participation, the project will engage students from underrepresented minority groups in Detroit and Pontiac as well as various Michigan-based organizations for girls. This endeavor is supported by collaborations with the Oakland University's STEM summer camps and field trips, as well as the Michigan Aspirations in Computing Committee. Through these initiatives, the project will inspire a broad spectrum of students to pursue futures in engineering and science.This project will design, implement, and evaluate the EMRadar, a practical EM sensing system that enables research on EM side-channel attacks and defenses in realistic environments. The proposed research will build on a three-layer architecture model. (1) In the sensing layer, EMRadar will incorporate innovative signal processing techniques to extract weak EM signals that are deeply buried in noise and contaminated by interference. To achieve this, the EMRadar will exploit the unique characteristics of EM emissions from a processor or memory and IoT software behavior, enabling novel methods that can adapt and optimize signal processing techniques used in classic Radar systems for highly sensitive EM sensing. (2) In the representation layer, this project will explore universal representation and classification of EM signals to unravel EM side-channel emissions of processors and memories. Despite extensive studies on EM side-channel emissions, accurate explanation of the semantics of EM side-channel signals remains highly challenging due to significant variation of EM signal pattern and the EM interference produced by workloads running on the same device. This project will leverage the recent advances in time series analysis and machine learning to revisit the representation of EM signals, and conduct a systematic measurement study to understand the robustness of universal representation across IoT devices with different architectures. (3) In the application layer, the EMRadar will be evaluated in real-world setups and applications. The PI will collaborate with domain experts to explore applications of the system developed in this project, such as supporting efficient IoT-based applications in healthcare, automobile industry, and intelligent buildings.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
物联网(IoT)已经给日常生活带来了革命性的变化,但由于物联网设备上的资源有限,而且部署后无法了解其内部运营,因此确保IoT设备的安全仍然是一项重大挑战。与传统的侧通道漏洞不同,电磁(EM)侧通道发射通过无线通道泄漏信息,提供了一种独特的媒介来研究物联网安全,而无需访问或检测设备。不幸的是,目前对物联网设备电磁侧通道攻击的研究已经受到严重限制,因为缺乏实用的传感系统来提取计算机处理器或存储器发出的微弱电磁信号,分离共置设备的电磁信号,并稳健地解开它们的语义。为了应对这些挑战,该项目将设计、实施和评估一个名为EMRadar的实用EM传感系统,以便能够在真实环境中研究EM侧通道攻击和防御。该项目的结果将加深我们对EM侧通道攻击的理解,并为排放安全标准提供参考。该项目实现的实用EM侧通道防御将改变各种安全关键物联网应用,包括检测资源高度受限的医疗设备中的程序偏差,如植入式心脏设备和智能胰岛素泵。该项目致力于通过将研究与教育和外联活动相结合来促进STEM的多样性和包容性。PI将通过提供实践活动和演示文稿,积极吸引当地高中生参与,阐明物联网技术和网络安全的基本概念。为了缩小STEM参与的差距,该项目将吸引底特律和庞蒂亚克代表不足的少数群体以及密歇根州的各种女孩组织的学生参与。这一努力得到了与奥克兰大学STEM夏令营和实地考察以及密歇根计算委员会的合作的支持。通过这些举措,该项目将激励广泛的学生追求工程和科学的未来。该项目将设计、实施和评估EMRadar,这是一个实用的电磁传感系统,能够在现实环境中研究电磁旁路攻击和防御。拟议的研究将建立在三层体系结构模型的基础上。(1)在感测层,EMRadar将结合创新的信号处理技术,提取深埋在噪声和干扰中的微弱电磁信号。为了实现这一目标,EMRadar将利用处理器或存储器的电磁辐射的独特特性和物联网软件行为,实现新的方法,这些方法可以调整和优化用于高灵敏度EM传感的经典雷达系统中的信号处理技术。(2)在表示层,本项目将探索电磁信号的通用表示和分类,以揭示处理器和存储器的电磁侧通道发射。尽管对电磁侧通道发射进行了广泛的研究,但由于电磁信号模式的显著变化以及运行在同一设备上的工作负载所产生的电磁干扰,准确解释电磁侧通道信号的语义仍然具有极大的挑战性。该项目将利用时间序列分析和机器学习的最新进展来重新审视EM信号的表示,并进行系统的测量研究,以了解跨不同架构的物联网设备的通用表示的稳健性。(3)在应用层,EMRadar将在真实的设置和应用中进行评估。PI将与领域专家合作,探索在该项目中开发的系统的应用,例如支持医疗保健、汽车行业和智能建筑中基于物联网的高效应用。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。
项目成果
期刊论文数量(0)
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会议论文数量(0)
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Jingshu Chen其他文献
Resolution Matters: Revisiting Prediction-Based Job Co-location in Public Clouds
解决方案很重要:重新审视公共云中基于预测的工作共置
- DOI:
10.1109/ucc56403.2022.00029 - 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
Justin Kur;Jingshu Chen;Ji Xue;Jun Huang - 通讯作者:
Jun Huang
FRI-384 Disease-associated transcriptomic profiles of liver sinusoidal endothelial cells at single nucleus resolution in a non-human primate model of metabolic dysfunction-associated steatohepatitis
FRI - 384 在代谢功能障碍相关脂肪性肝炎的非人灵长类动物模型中单核分辨率下肝窦内皮细胞的疾病相关转录组图谱
- DOI:
10.1016/s0168-8278(25)01613-7 - 发表时间:
2025-05-01 - 期刊:
- 影响因子:33.000
- 作者:
Joanne Hsieh;Heather Burkart;Susan Appt;Vinay Kartha;Jingshu Chen;Hikaru Miyazaki;Smitha Shambhu;Dimitry Popov;Kylie Kavanagh;Francisco LePort;Martin Borch Jensen - 通讯作者:
Martin Borch Jensen
Towards scalable model checking of self-stabilizing programs
迈向自稳定程序的可扩展模型检查
- DOI:
10.1016/j.jpdc.2012.12.009 - 发表时间:
2013 - 期刊:
- 影响因子:0
- 作者:
Jingshu Chen;Fuad Abujarad;S. Kulkarni - 通讯作者:
S. Kulkarni
A Reflective Framework for Authentication in Grid Computing Environments
网格计算环境中身份验证的反射框架
- DOI:
10.1109/gcc.2006.15 - 发表时间:
2006 - 期刊:
- 影响因子:0
- 作者:
Jingshu Chen;H. Wu;Qingyang Wang;Qingguan Wang;Xue - 通讯作者:
Xue
TOP-427 Characterizing the response to high-dose gene therapy in nonhuman primates with a healthy or fibrotic liver at single nucleus resolution
TOP - 427 在单核分辨率下描述具有健康肝脏或纤维化肝脏的非人灵长类动物对高剂量基因治疗的反应
- DOI:
10.1016/s0168-8278(25)01567-3 - 发表时间:
2025-05-01 - 期刊:
- 影响因子:33.000
- 作者:
Joanne Hsieh;Linda Chio;Jingshu Chen;Vinay Kartha;Heather Burkart;Susan Appt;Daniel Fuentes;Chris Carrico;Amber Lim;Smitha Shambhu;Dimitry Popov;Kylie Kavanagh;Chris Towne;Martin Borch Jensen - 通讯作者:
Martin Borch Jensen
Jingshu Chen的其他文献
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