EAGER: SARE: Collaborative Research: Exploring and Mitigating Attacks of Millimeter-wave Radar Sensors in Autonomous Vehicles

自动驾驶汽车在改善道路安全、出行体验和交通足迹以及所有人的机动性和可达性方面具有巨大的潜力。然而，近年来，AVS本身的安全和安保问题引起了许多关注。由于自动驾驶系统使用包括摄像头和雷达在内的大量传感器来检测、分类和跟踪道路上的物体和障碍物，其可靠性和弹性对自动驾驶系统的安全性具有重要影响。虽然已经研究了针对无人机系统中的摄像头和其他传感器的恶意攻击的风险，但对无人机中毫米波雷达传感器的脆弱性缺乏深入的了解。这个早期概念探索性研究资助(AGER)项目旨在通过研究基于毫米波雷达传感器的系统的先进攻击和防御方法，探索提高AVS安全性和弹性的见解。由于这些雷达目前仅用于自适应巡航控制、盲点检测和碰撞避免，该项目将使许多安全关键应用受益。PIS计划扩大K-12和代表性不足学生的研究机会，并通过布法罗大学国家夏季运输研究所(NSTI)、可持续运输和物流研究所(ISTL)和路易斯·斯托克斯少数民族参与联盟(LSAMP)的教育项目，在扩大工程参与方面整合多样性。私人投资促进机构将通过出版物、讲座和演示来传播项目成果，并将研究材料纳入具体的课程和教育课程。所有新开发的研究和教学材料都将在项目网站上向公众开放。这个项目将首先展示毫米波雷达传感器可以通过一种非合作的空中同步方法在无人机上路时被欺骗和干扰。它可以准确识别受害者雷达的频段、调制方案和波形模式，从而发动隐身攻击。该团队将进行一个概念验证演示，用快速线性调频信号攻击真实世界的AV雷达。其次，该项目将探索基于硬件和软件/算法的防御机制来避免此类攻击，包括基于波束特征的物理层容量估计、机器学习物理识别、非合作无源前端架构和带限相干噪声雷达。该项目的创新不仅来自于使用基于自适应有限状态机的方法，这种方法结合了线性调频序列间和线性调频序列内的同步，而且还来自数据建模技术，以有效地调整攻击者的波形参数。此外，该项目是首批探索使用独特的辐射测量、噪声雷达和被动雷达作为针对AVS攻击的有效防御机制的项目之一。该项目的研究重点将显著推进毫米波传感器安全的最新知识，并为开发更不可欺骗、抗披露和强大的AV雷达解决方案提供见解。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。

Autonomous vehicles (AVs) have significant potentials to improve road safety, travel experience and transportation footprint, as well as the mobility and accessibility for all. However, the safety and security of AVs themselves have raised many concerns in recent years. As AVs use a plethora of sensors, including cameras and radars to detect, classify and track objects and obstacles on the road, their reliability and resilience have significant impacts on AV safety. Although risks from malicious attacks targeted at cameras and other sensors in AV systems have been studied, there is a lack of in-depth understanding of the vulnerability of millimeter-wave radar sensors in AVs. This EArly-concept Grant for Exploratory Research (EAGER) project seeks to explore insights to improve the security and resilience of AVs by investigating advanced attack and defense methods for millimeter-wave radar sensor-based systems. Since these radars are used exclusively today for adaptive cruise control, blind-spot detection, and collision avoidance, this project will benefit many safety-critical applications. The PIs plan to expand research opportunities for K-12 and underrepresented students, and integrate diversity in broadening participation in engineering through the educational programs of National Summer Transportation Institute (NSTI), Institute for Sustainable Transportation and Logistics (ISTL) and Louis Stokes Alliance for Minority Participation (LSAMP) at the University at Buffalo. The PIs will disseminate the results of the project through publications, talks, and demos, and integrate research materials into specific courses and education curricula. All newly developed research and teaching materials will be publicly accessible on the project website. This project will first demonstrate that the millimeter-wave radar sensors can be spoofed and jammed while AVs are on the road through a non-cooperative over-the-air synchronization method. It can accurately identify the frequency band, modulation scheme and waveform patterns of victim radars to launch stealthy attacks. The team will conduct a proof-of-concept demo to attack real-world AV radars with fast-chirp signals. Second, the project will explore both hardware and software/algorithm-based defense mechanisms for avoiding such attacks, including beam feature based physical layer capacity estimation, machine learning physical identification, non-cooperative passive front-end architectures and band-limited coherent noise radars. The innovation of the project comes from not only the use of adaptive finite state machine based approaches that combine inter- and intra-chirp-sequence synchronization, but also data modeling techniques to efficiently adjust the attacker’s waveform parameters. Moreover, the project is among the first to explore the use of unique radiometrics, noise radars and passive radars as effective defense mechanisms against the attacks to AVs. Research thrusts in this project will significantly advance the state-of-the-art knowledge of the security of millimeter-wave sensors, and provide insights on developing more undeceivable, disclosure-resistant and robust AV radar solutions.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.