Raymond Muller, Yanmao Man, Z. B. Celik, M. Li, Ryan M. Gerdes
{"title":"DRIVETRUTH: Automated Autonomous Driving Dataset Generation for Security Applications","authors":"Raymond Muller, Yanmao Man, Z. B. Celik, M. Li, Ryan M. Gerdes","doi":"10.14722/autosec.2022.23032","DOIUrl":"https://doi.org/10.14722/autosec.2022.23032","url":null,"abstract":"—With emerging vision-based autonomous driving (AD) systems, it becomes increasingly important to have datasets to evaluate their correct operation and identify potential security flaws. However, when collecting a large amount of data, either human experts manually label potentially hundreds of thousands of image frames or systems use machine learning algorithms to label the data, with the hope that the accuracy is good enough for the application. This can become especially problematic when tracking the context information, such as the location and velocity of surrounding objects, useful to evaluate the correctness and improve stability and robustness of the AD systems. In this paper, we introduce D RIVE T RUTH , a data collection framework built on CARLA, an open-source simulator for AD research, which constructs datasets with automatically generated accurate object labels, bounding boxes of objects and their contextual information through accessing simulation state and using semantic LiDAR raycasts. By leveraging the actual state of the simulation and the agents within it, we guarantee complete accuracy in all labels and gathered contextual information. Further, the use of the simulator provides easily collecting data in diverse environmental conditions and agent behaviors, with lighting, weather, and traffic behavior being configurable within the simulation. Through this effort, we provide users a means to extracting actionable simulated data from CARLA to test and explore attacks and defenses for AD systems.","PeriodicalId":399600,"journal":{"name":"Proceedings Fourth International Workshop on Automotive and Autonomous Vehicle Security","volume":"274-275 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121040490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yulong Cao, Yanan Guo, Takami Sato, Qi Alfred Chen, Z. Mao, Yueqiang Cheng
{"title":"Demo: Remote Adversarial Attack on Automated Lane Centering","authors":"Yulong Cao, Yanan Guo, Takami Sato, Qi Alfred Chen, Z. Mao, Yueqiang Cheng","doi":"10.14722/autosec.2022.23015","DOIUrl":"https://doi.org/10.14722/autosec.2022.23015","url":null,"abstract":"—Advanced driver-assistance systems (ADAS) are widely used by modern vehicle manufacturers to automate, adapt and enhance vehicle technology for safety and better driving. In this work, we design a practical attack against automated lane centering (ALC), a crucial functionality of ADAS, with remote adversarial patches. We identify that the back of a vehicle is an effective attack vector and improve the attack robustness by considering various input frames. The demo includes videos that show our attack can divert victim vehicle out of lane on a representative ADAS, Openpilot, in a simulator.","PeriodicalId":399600,"journal":{"name":"Proceedings Fourth International Workshop on Automotive and Autonomous Vehicle Security","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126451338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"WIP: Infrastructure-Aided Defense for Autonomous Driving Systems: Opportunities and Challenges","authors":"Y. Luo","doi":"10.14722/autosec.2022.23048","DOIUrl":"https://doi.org/10.14722/autosec.2022.23048","url":null,"abstract":"—Autonomous Driving (AD) is a rapidly developing technology and its security issues have been studied by various recent research works. With the growing interest and investment in leveraging intelligent infrastructure support for practical AD, AD system may have new opportunities to defend against existing AD attacks. In this paper, we are the first t o systematically explore such a new AD security design space leveraging emerging infrastructure-side support, which we call Infrastructure-Aided Autonomous Driving Defense (I-A2D2). We first taxonomize existing AD attacks based on infrastructure-side capabilities, and then analyze potential I-A2D2 design opportunities and requirements. We further discuss the potential design challenges for these I-A2D2 design directions to be effective in practice.","PeriodicalId":399600,"journal":{"name":"Proceedings Fourth International Workshop on Automotive and Autonomous Vehicle Security","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132101563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Bouchouia, J. Monteuuis, H. Labiod, Ons Jelassi, Wafa Ben Jaballah, J. Petit
{"title":"Demo: A Simulator for Cooperative and Automated Driving Security","authors":"M. Bouchouia, J. Monteuuis, H. Labiod, Ons Jelassi, Wafa Ben Jaballah, J. Petit","doi":"10.14722/autosec.2022.23014","DOIUrl":"https://doi.org/10.14722/autosec.2022.23014","url":null,"abstract":"","PeriodicalId":399600,"journal":{"name":"Proceedings Fourth International Workshop on Automotive and Autonomous Vehicle Security","volume":"129 17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133462132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ben Nassi, Elad Feldman, Aviel Levy, Yaron Pirutin, A. Shabtai, R. Masuoka, Y. Elovici
{"title":"Demo: Identifying Drones Based on Visual Tokens","authors":"Ben Nassi, Elad Feldman, Aviel Levy, Yaron Pirutin, A. Shabtai, R. Masuoka, Y. Elovici","doi":"10.14722/autosec.2022.23002","DOIUrl":"https://doi.org/10.14722/autosec.2022.23002","url":null,"abstract":"flashes from the LED and LCD were intercepted by the camera and decoded by a machine learning model to a message. Two different types of authentication devices – a flashing light and an LCD screen – were used to transmit the message; we also attempted to send the message using both devices in combination. Results We achieved 90% precision, on average, in decoding the content of the messages used to authenticate the drone. This results can be further improve using error correction code. An extended version of this paper, which includes the algorithm used to receive the message and distinguish between the drones, is available [1].","PeriodicalId":399600,"journal":{"name":"Proceedings Fourth International Workshop on Automotive and Autonomous Vehicle Security","volume":"81 3 Suppl 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123267611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zachariah Threet, C. Papadopoulos, Proyash Poddar, A. Afanasyev, W. Lambert, Haley Burnell, Sheikh Ghafoor, Susmit Shannigrahi
{"title":"Demo: In-Vehicle Communication Using Named Data Networking","authors":"Zachariah Threet, C. Papadopoulos, Proyash Poddar, A. Afanasyev, W. Lambert, Haley Burnell, Sheikh Ghafoor, Susmit Shannigrahi","doi":"10.14722/autosec.2022.23011","DOIUrl":"https://doi.org/10.14722/autosec.2022.23011","url":null,"abstract":"—Data-centric architectures are a candidate for in- vehicle communication. They add naming standardization, data provenance, security, and improve interoperability between dif- ferent ECUs and networks. In this demo, We demonstrate the feasibility and advantages of data-centric architectures through Named Data Networking (NDN). We deploy a bench-top testbed using Raspberry PIs and replay real CAN data.","PeriodicalId":399600,"journal":{"name":"Proceedings Fourth International Workshop on Automotive and Autonomous Vehicle Security","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122461980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hyungsub Kim, Muslum Ozgur Ozmen, Antonio Bianchi, Z. Berkay Celik, Dongyan Xu
{"title":"Demo: Policy-based Discovery and Patching of Logic Bugs in Robotic Vehicles","authors":"Hyungsub Kim, Muslum Ozgur Ozmen, Antonio Bianchi, Z. Berkay Celik, Dongyan Xu","doi":"10.14722/autosec.2022.23029","DOIUrl":"https://doi.org/10.14722/autosec.2022.23029","url":null,"abstract":"This demo is based on PGF UZZ [1], a policy-guided fuzzer that discovers logic bugs in a robotic vehicle (RV), and PGP ATCH [2], a policy-guided logic bug patching for the RV. Logic bugs cause deviations in the RVs’ behavior from the developer’s expectations but do not cause the program to stop execution. Discovering and patching logic bugs is challenging","PeriodicalId":399600,"journal":{"name":"Proceedings Fourth International Workshop on Automotive and Autonomous Vehicle Security","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123218447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Generation of CAN-based Wheel Lockup Attacks on the Dynamics of Vehicle Traction","authors":"Alireza Mohammadi, Hafiz Abid Mahmood Malik","doi":"10.14722/autosec.2022.23025","DOIUrl":"https://doi.org/10.14722/autosec.2022.23025","url":null,"abstract":"—Recent automotive hacking incidences have demon- strated that when an adversary manages to gain access to a safety-critical CAN, severe safety implications will ensue. Under such threats, this paper explores the capabilities of an adversary who is interested in engaging the car brakes at full speed and would like to cause wheel lockup conditions leading to catastrophic road injuries. This paper shows that the physical capabilities of a CAN attacker can be studied through the lens of closed-loop attack policy design. In particular, it is demon- strated that the adversary can cause wheel lockups by means of closed-loop attack policies for commanding the frictional brake actuators under a limited knowledge of the tire-road interaction characteristics. The effectiveness of the proposed wheel lockup attack policy is shown via numerical simulations under different road conditions.","PeriodicalId":399600,"journal":{"name":"Proceedings Fourth International Workshop on Automotive and Autonomous Vehicle Security","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123634134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Vehicle Lateral Motion Stability Under Wheel Lockup Attacks","authors":"Alireza Mohammadi, Hafiz Abid Mahmood Malik","doi":"10.14722/autosec.2022.23010","DOIUrl":"https://doi.org/10.14722/autosec.2022.23010","url":null,"abstract":"—Motivated by ample evidence in the automotive cybersecurity literature that the car brake ECUs can be mali-ciously reprogrammed, it has been shown that an adversary who can directly control the frictional brake actuators can induce wheel lockup conditions despite having a limited knowledge of the tire-road interaction characteristics [1]. In this paper, we investigate the destabilizing effect of such wheel lockup attacks on the lateral motion stability of vehicles from a robust stability perspective. Furthermore, we propose a quadratic programming (QP) problem that the adversary can solve for finding the optimal destabilizing longitudinal slip reference values.","PeriodicalId":399600,"journal":{"name":"Proceedings Fourth International Workshop on Automotive and Autonomous Vehicle Security","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115617303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhisheng Hu, Junjie Shen, Shengjian Guo, Xinyang Zhang, Zhenyu Zhong, Qi Alfred Chen, Kang Li
{"title":"PASS: A System-Driven Evaluation Platform for Autonomous Driving Safety and Security","authors":"Zhisheng Hu, Junjie Shen, Shengjian Guo, Xinyang Zhang, Zhenyu Zhong, Qi Alfred Chen, Kang Li","doi":"10.14722/autosec.2022.23018","DOIUrl":"https://doi.org/10.14722/autosec.2022.23018","url":null,"abstract":"","PeriodicalId":399600,"journal":{"name":"Proceedings Fourth International Workshop on Automotive and Autonomous Vehicle Security","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116576323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}