EAGER: Cyber-Physical Fingerprinting for Internet of Things Authentication: Accelerating IoT Research and Education Under the Global City Teams Challenge

EAGER：用于物联网身份验证的网络物理指纹识别：在全球城市团队挑战下加速物联网研究和教育

• 批准号: 1524634
• 负责人: Walid Saad
• 金额: $ 15万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-15 至 2018-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1524634&HistoricalAwards=false
• 关键词: EAGER; Cyber; Physical; Fingerprinting; Internet

Device authentication and identification has been recently cited as one of the most pressing security challenges facing the Internet of things (IoT). In particular, the open-access nature of the IoT renders it highly susceptible to insider attacks. In such attacks, adversaries can capture or forge the identity of the small, resource constrained IoT devices and, thus, bypass conventional authentication methods. Such attacks are challenging to defend against due to the apparent legitimacy of the adversaries' devices. The primary goal of this research is to overcome this challenge by developing new authentication methods that supplement traditional security solutions with cyber-physical fingerprints extracted from the IoT devices' environment.This project will develop a novel machine learning framework that enables the IoT to dynamically identify, classify, and authenticate devices based on their cyber-physical environment and with limited available prior data. This will result in the creation of environment-based IoT device credentials that can serve as a means of attestation, not only on the legitimacy of a device's identity, but also on the validity of the physical environment it claims to monitor and the actions it claims to be performing over time. The framework will also encompass an experimental IoT software platform that will be built to validate the proposed research. Owing to a partnership with the NIST Global City Teams Challenge (GCTC) project "Bringing Internet of Things Know-How to High School Students", a collaboration with IoT-DC, Arlington County, VA, and other entities, the proposed research will train high school students, STEM educators, and a broad community on a variety of research topics that will include IoT security, cyber-physical systems, and data analytics. The broader impacts will also include the creation of an interdisciplinary workforce focused on securing tomorrow's smart cities.

设备认证和识别最近被认为是物联网（IoT）面临的最紧迫的安全挑战之一。特别是，物联网的开放访问性质使其极易受到内部攻击。在这种攻击中，攻击者可以捕获或伪造小型、资源受限的物联网设备的身份，从而绕过传统的身份验证方法。由于对手设备的明显合法性，这种攻击具有挑战性。本研究的主要目标是通过开发新的身份验证方法来克服这一挑战，该方法利用从物联网设备环境中提取的网络物理指纹来补充传统的安全解决方案。本项目将开发一种新型的机器学习框架，使物联网能够根据其网络物理环境和有限的可用先验数据动态识别，分类和验证设备。这将导致创建基于环境的物联网设备凭据，这些凭据可以作为证明的手段，不仅可以证明设备身份的合法性，还可以证明它声称要监视的物理环境的有效性以及它声称要随着时间的推移执行的操作。该框架还将包括一个实验性的物联网软件平台，该平台将用于验证拟议的研究。由于与NIST全球城市团队挑战赛（GCTC）项目“将物联网技术带给高中生”的合作，与IoT-DC，阿灵顿县，弗吉尼亚州和其他实体的合作，拟议的研究将培训高中生，STEM教育工作者和广泛的社区关于各种研究主题，包括物联网安全，网络物理系统和数据分析。更广泛的影响还将包括创建一支专注于保护未来智慧城市的跨学科劳动力队伍。