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

联网自动驾驶汽车（AVs）可能会为数百万人提供新的出行选择。将连接功能集成到现代车辆中是互联自动驾驶汽车不断扩大攻击面背后的主要推动力，使其容易受到黑客攻击和数据窃取。主要漏洞来自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、定位和减轻联网自动驾驶汽车的网络安全威胁具有重要的社会意义。然而，现有解决方案的能力和范围有限，因为它们无法可靠地将接收到的数据连接到发送设备。该项目的目标是通过开发一个名为Linking2Source框架的整体解决方案，通过三个无缝集成的防御层来保护自动驾驶汽车免受不断增长的攻击面和向量的攻击，每一层旨在减轻一组特定的攻击。该项目还有一个重要的教育组成部分，包括一系列探究性的实践活动，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。提出的link2source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认证、入侵检测和定位工具来保护车载网络，这些工具用于电子控制单元（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传输ECU识别和定位。第二层旨在通过数据驱动的统计图形分析，利用ECU行为指纹保护车载网络免受固件/软件级攻击。研究团队的目标是将连续的车载网络数据转换为有向图，利用统计图分析进行ECU行为建模和入侵检测。第三层防御旨在通过使用依赖于基于车辆物理建模的故障检测和隔离的动态观察者来保护自动驾驶汽车免受传感和驱动层的攻击。错误的信号，如不正确的转向角度指令，是由流氓ecu发出的，与车辆物理不一致，可能导致不安全的操作，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，这些防御层的决策被最佳地融合到Linking2Source框架中的三个防御解决方案中。该项目的一个关键组成部分是在物理和数据链路层使用车载网络数据来建模物理、行为和车辆状态指纹，并使用它们进行攻击检测和定位，并使用主动取消策略减轻恶意ecu的影响。研究团队将对提出的解决方案进行原型设计，并在密歇根大学迪尔伯恩分校的班车、密歇根大学MCity测试设施和商业工具上进行评估，此外还将从网络测试平台和真实车辆驾驶中收集大规模数据，并与研究社区分享。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.