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1. Automated Discovery of Denial-of-Service Vulnerabilities in Connected Vehicle Protocols

   Hu, Shengtuo ; Chen, Qi Alfred ; Sun, Jiachen ; Feng, Yiheng ; Mao, Z. Morley ; Liu, Henry X. ( January 2021 , USENIX Security Symposium)

   null (Ed.)

   With the development of the emerging Connected Vehicle (CV) technology, vehicles can wirelessly communicate with traffic infrastructure and other vehicles to exchange safety and mobility information in real time. However, the integrated communication capability inevitably increases the attack surface of vehicles, which can be exploited to cause safety hazard on the road. Thus, it is highly desirable to systematically understand design-level flaws in the current CV network stack as well as in CV applications, and the corresponding security/safety consequences so that these flaws can be proactively discovered and addressed before large-scale deployment. In this paper, we design CVAnalyzer, a system for discovering design-level flaws for availability violations of the CV network stack, as well as quantifying the corresponding security/safety consequences. To achieve this, CVAnalyzer combines the attack discovery capability of a general model checker and the quantitative threat assessment capability of a probabilistic model checker. Using CVAnalyzer, we successfully uncovered 4 new DoS (Denial-of-Service) vulnerabilities of the latest CV network protocols and 14 new DoS vulnerabilities of two CV platoon management protocols. Our quantification results show that these attacks can have as high as 99% success rates, and in the worst case can at least double the delay in packet processing, violating the latency requirement in CV communication.We implemented and validated all attacks in a real-world testbed, and also analyzed the fundamental causes to propose potential solutions. We have reported our findings in the CV network protocols to the IEEE 1609 Working Group, and the group has acknowledged the discovered vulnerabilities and plans to adopt our solutions. 

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2. On the Cybersecurity of Traffic Signal Control System With Connected Vehicles

   Feng, Yiheng ; Huang, Shihong ; Wong, Wai ; Chen, Qi Alfred ; Mao, Z ; Liu, Henry X. ( January 2022 , IEEE transactions on intelligent transportation systems)

   Connected vehicle (CV) technology brings both opportunities and challenges to the traffic signal control (TSC) system. While safety and mobility performance could be greatly improved by adopting CV technologies, the connectivity between vehicles and transportation infrastructure may increase the risks of cyber threats. In the past few years, studies related to cybersecurity on the TSC systems were conducted. However, there still lacks a systematic investigation that provides a comprehensive analysis framework. In this study, our aim is to fill the research gap by proposing a comprehensive analysis framework for the cybersecurity problem of the TSC in the CV environment. With potential threats towards the major components of the system and their corresponding impacts on safety and efficiency analyzed, data spoofing attack is considered the most plausible and realistic attack approach. Based on this finding, different attack strategies and defense solutions are discussed. A case study is presented to show the impact of the data spoofing attacks towards a selected CV based TSC system and corresponding mitigation countermeasures. This case study is conducted on a hybrid security testing platform, with virtual traffic and a real V2X communication network. To the best of our knowledge, this is the first study to present a comprehensive analysis framework to the cybersecurity problem of the CV-based TSC systems. 

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3. GPS Spoofing Attack Detection on Intersection Movement Assist using One-Class Classification

   Ying, Jun ; Feng, Yiheng ; Chen, Qi Alfred ; Mao, Z. ( January 2023 , ISOC Symposium on Vehicle Security and Privacy (VehicleSec))

   Intersection movement assist (IMA) is a connected vehicle (CV) application to improve vehicle safety. GPS spoofing attack is one major threat to the IMA application since inaccurate localization results may generate fake warnings that increase rear-end crashes, or cancel real warnings that may lead to angle or swipe crashes. In this work, we first develop a GPS spoofing attack model to trigger the IMA warning of entry vehicles at a roundabout driving scenario. The attack model can generate realistic trajectories while achieving the attack goal. To defend against such attacks, we further design a one-class classifier to distinguish the normal vehicle trajectories from the trajectories under attack. The proposed model is validated with a real-world data set collected from Ann Arbor, Michigan. Results show that although the attack model triggers the IMA warning in a short time (i.e., in a few seconds), the detection model can still identify the abnormal trajectories before the attack succeeds with low false positive and false negative rates. 

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4. Evaluating the mobility performance of autonomous vehicles at a signalized traffic intersection

   https://doi.org/10.1117/12.2606614  

   Ahmad, Salman ; Rogers, Joshua ; Nelson, Martha ; Huang, Ying ; Ajmera, Beena ; Lu, Pan ( April 2022 , Proceedings Volume 12046, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2022)

   Zonta, Daniele ; Su, Zhongqing ; Glisic, Branko (Ed.)

   Recent developments in autonomous vehicle (AV) or connected AVs (CAVs) technology have led to predictions that fully self-driven vehicles could completely change the transportation network over the next decades. However, at this stage, AVs and CAVs are still in the development stage which requires various trails in the field and machine learning through autonomous driving miles on real road networks. Until the complete market adoption of autonomous technology, a long transition period of coexistence between conventional and autonomous cars would exist. It is important to study and develop the expected driving behavior of future autonomous cars and the traffic simulation platforms provide an opportunity for researchers and technology developers to implement and assess the different behaviors of self-driving vehicle technology before launching it to the actual ground. This study utilizes PTV VISSIM microsimulation platform to evaluate the mobility performance of unmanned vehicles at a 4-way signalized traffic intersection. The software contains three different AV-ready driving logics such as AV-cautious, AV-normal, and AV-aggressive which were tested against the performance of the conventional vehicles, and the results of the study revealed that the overall network operational performance improves with the progressive introduction of AVs using AV-normal, and AV-aggressive driving behaviors while the AV-cautious driving behavior stays conservative and deteriorates the traffic performance. 

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5. Detecting Data Spoofing in Connected Vehicle based Intelligent Traffic Signal Control using Infrastructure-Side Sensors and Traffic Invariants

   Shen, Junjie ; Wan, Ziwen ; Luo, Yunpeng ; Feng, Yiheng ; Mao, Z. ; Chen, Qi Alfred ( January 2023 , IEEE Intelligent Vehicles Symposium (IV))

   Connected Vehicle (CV) technologies are under rapid deployment across the globe and will soon reshape our transportation systems, bringing benefits to mobility, safety, environment, etc. Meanwhile, such technologies also attract attention from cyberattacks. Recent work shows that CV-based Intelligent Traffic Signal Control Systems are vulnerable to data spoofing attacks, which can cause severe congestion effects in intersections. In this work, we explore a general detection strategy for infrastructure-side CV applications by estimating the trustworthiness of CVs based on readily-available infrastructureside sensors. We implement our detector for the CV-based traffic signal control and evaluate it against two representative congestion attacks. Our evaluation in the industrial-grade traffic simulator shows that the detector can detect attacks with at least 95% true positive rates while keeping false positive rate below 7% and is robust to sensor noises. 

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