SaTC: CORE: Small: Linking2Source: Security of In-Vehicle Networks via Source Identification

互联自动驾驶汽车(AV)可能会为数百万人提供新的移动性选择。将连接功能集成到现代车辆中是互联AV攻击面不断扩大的主要驱动力，使它们容易受到黑客攻击和数据失窃。关键漏洞源于不安全的汽车控制网络与多媒体网络的耦合增加，以及蓝牙和Wi-Fi网络等无线接口的集成。因此，开发强大可靠的解决方案来识别、本地化和缓解对联网AV的网络安全威胁具有重要的社会意义。然而，现有的解决方案在它们的能力和范围上是有限的，因为它们不能可靠地将接收的数据链接到发送设备。该项目的目标是通过三个无缝集成的防御层开发名为Linking2Source框架的整体解决方案，以保护AVs免受不断增长的攻击面和媒介的攻击，每一层旨在缓解一组特定的攻击。该项目还有一个重要的教育组成部分，包括一系列探索性的动手活动，涉及车辆数据采集、解码和数据分析、网络数据包注入和入侵检测，旨在拓展和扩大STEM学科的参与，包括汽车网络安全、网络物理系统安全、统计数据分析和数字取证。拟议的Linking2Source框架的第一层旨在通过开发基于不可克隆信号属性的实时消息验证、入侵检测和本地化工具来保护车载网络，用于电子控制单元(ECU)的物理指纹识别。该方法利用物理信号属性的唯一性，利用统计信号处理和参数建模技术进行物理指纹估计，并使用统计机器学习方法来传输ECU识别和定位。第二层旨在通过数据驱动的统计图分析，使用ECU行为指纹来保护车载网络免受固件/软件级攻击。这里的研究团队的目标是将连续的车载网络数据转换为有向图，以利用统计图分析来进行ECU行为建模和入侵检测。第三层防御旨在通过使用动态观测器来保护自动驾驶系统在传感和执行层免受攻击，这些动态观测器依赖于基于车辆物理的建模来检测和隔离故障。错误的信号，如错误的转向角命令，由流氓ECU发出，并与车辆物理不一致，可能会导致不安全的操纵，如过度偏航运动。该项目利用基于物理的车辆模型来验证通过车载网络总线发出的ECU信号的正确性。通过利用Dempster-Shafer证据理论，来自这些防御层的决策被最佳地融合，以在Linking2Source框架中集成三种防御解决方案。该项目的一个关键组件是在物理层和数据链路层使用车载网络数据对物理、行为和车辆状态指纹进行建模，并使用它们进行攻击检测，并使用主动消除策略定位和缓解恶意ECU的影响。除了从网络试验台和真实车辆驾驶中收集大规模数据并与研究社区共享外，研究团队还将在密歇根大学-迪尔伯恩航天飞机、密歇根大学MCity测试设施和商业工具上对建议的解决方案进行原型和评估。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。

Connected autonomous vehicles (AVs) may offer new mobility options to millions of people. Integration of connectivity features into modern vehicles is a main driving force behind the ever-expanding attack surface of connected AVs, rendering them vulnerable to hacking and data theft. Key vulnerabilities arise from the increased coupling of unsecured automotive control networks with multimedia networks and the integration of wireless interfaces such as Bluetooth and Wi-Fi networks. As such, developing robust and reliable solutions to identify, localize, and mitigate cybersecurity threats to connected AVs is of societal importance. Existing solutions, however, are limited in their ability and scope as they are unable to reliably link the received data to the transmitting devices. The goal of this project is to safeguard AVs against growing attack surfaces and vectors by developing a holistic solution called the Linking2Source framework through three seamlessly integrated layers of defense, with each layer aiming to mitigate a specific set of attacks. The project also has a significant educational component, consisting of a set of inquisitive hands-on activities involving vehicle data acquisition, decoding, and data analytics, network packet injection, and intrusion detection aimed at outreach and broadening participation in STEM disciplines, including automotive cybersecurity, cyber-physical system security, statistical data analysis and digital forensics.The first layer of the proposed Linking2Source framework aims to protect in-vehicle networks by developing real-time message authentication, intrusion detection, and localization tools based on unclonable signal attributes for physical fingerprinting of electronic control units (ECUs). The approach exploits uniqueness in physical signal attributes, leverages statistical signal processing and parameter modeling techniques for physical fingerprint estimation, and uses statistical machine learning methods for transmitting ECU identification and localization. The second layer aims to protect in-vehicle networks against firmware/software-level attacks using ECU behavioral fingerprinting through data-driven statistical graph analytics. The approach targeted by the research team here is the transformation of sequential in-vehicle network data into a directed-graph to leverage statistical graph analytics for ECU behavior modeling and intrusion detection. The third layer of defense aims to protect AVs against attacks at the sensing and actuation layer by using dynamical observers that rely on vehicle-physics-based modeling for fault detection and isolation. The faulty signals such as incorrect steering angle commands that are issued by the rogue ECUs and are not in agreement with the vehicle physics could cause unsafe maneuvers such as excessive yaw motions. The project exploits the physics-based vehicle model for verifying the correctness of the issued ECU signals over the in-vehicle network bus. By leveraging the Dempster-Shafer evidence theory, the decisions from these layers of defense are optimally fused to integrate the three defense solutions in the Linking2Source framework. A key component of this project is to use in-vehicle network data both at the physical and datalink layers for modeling physical, behavioral, and vehicle-state fingerprints and using them for attack detection and localization and mitigation of the impact of malicious ECUs using a proactive cancellation policy. The research team will prototype the proposed solutions and evaluate them on the University of Michigan-Dearborn shuttle, on the University of Michigan MCity Test Facility, and on commercial tools, in addition to collecting large-scale data from a network testbed and from a real vehicle driving and sharing it with the research community.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.