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1. VIVE: Virtualization of Vehicular Electronics for System-level Exploration

   Kabir, R; Mishra, N; Ray, S. (January 2021, 24th IEEE International Conference on Intelligent Transportation (ITSC 2021))

   null (Ed.)

   We develop a virtual prototyping infrastructure for modeling and simulation of automotive systems. We focus on exercising and exploring use cases involving system-level coordination of vehicular electronics, sensors, and software. In current practice, such use cases can only be explored late in the design when all the relevant hardware components are available. Any design change, e.g., for optimization or security or even functional errors found during the exploration, incurs prohibitive cost at that stage. Our solution is a flexible, configurable prototyping platform that enables the user to seamlessly add new system-level use cases. Unlike other related prototyping environments, the focus of our platform is on communication and coordination among different components, not the computation of individual Electronic Control Units. We report on the use of the platform for implementing several realistic usage scenarios on automotive platforms and exploring the effects of their interaction. In particular, we show how to use the platform to develop real-time in-vehicle communication optimizers for different optimization targets. 

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2. Towards an Integrated Vehicle Management System in DriveOS

   https://doi.org/10.1145/3477013  

   Sinha, Soham; West, Richard (October 2021, ACM Transactions on Embedded Computing Systems)

   Modern automotive systems feature dozens of electronic control units (ECUs) for chassis, body and powertrain functions. These systems are costly and inflexible to upgrade, requiring ever increasing numbers of ECUs to support new features such as advanced driver assistance (ADAS), autonomous technologies, and infotainment. To counter these challenges, we propose DriveOS, a safe, secure, extensible, and timing-predictable system for modern vehicle management in a centralized platform. DriveOS is based on a separation kernel, where timing and safety-critical ECU functions are implemented in a real-time OS (RTOS) alongside non-critical software in Linux or Android. The system enforces the separation, or partitioning, of both software and hardware among different OSes. DriveOS runs on a relatively low-cost embedded PC-class platform, supporting multiple cores and hardware virtualization capabilities. Instrument cluster, in-vehicle infotainment and advanced driver assistance system services are implemented in a Yocto Linux guest, which communicates with critical real-time services via secure shared memory. The RTOS manages a real-time controller area network (CAN) interface that is inaccessible to Linux services except via well-defined and legitimate communication channels. In this work, we integrate three Qt-based services written for Yocto Linux, running in parallel with a real-time longitudinal controller task and multiple CAN bus concentrators, for vehicular sensor data processing and actuation. We demonstrate the benefits and performance of DriveOS with a hardware-in-the-loop CARLA simulation using a real car dataset. 

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3. Reverse Engineering Controller Area Network Messages using Unsupervised Machine Learning

   https://doi.org/10.1109/MCE.2020.3023538  

   Ezeobi, Uchenna; Olufowobi, Habeeb; Young, Clinton; Zambreno, Joseph; Bloom, Gedare (September 2020, IEEE Consumer Electronics Magazine)

   The smart city landscape is rife with opportunities for mobility and economic optimization, but also presents many security concerns spanning the range of components and systems in the smart ecosystem. One key enabler for this ecosystem is smart transportation and transit, which is foundationally built upon connected vehicles. Ensuring vehicular security, while necessary to guarantee passenger and pedestrian safety, is itself challenging due to the broad attack surfaces of modern automotive systems. A single car contains dozens to hundreds of small embedded computing devices known as electronic control units (ECUs) executing 100s of millions of lines of code; the inherent complexity of this tightly-integrated cyber-physical system (CPS) is one of the key problems that frustrates effective security. We describe an approach to help reduce the complexity of security analyses by leveraging unsupervised machine learning to learn clusters of messages passed between ECUs that correlate with changes in the CPS state of a vehicle as it moves through the world. Our approach can help to improve the security of vehicles in a smart city, and can leverage smart city infrastructure to further enrich and refine the quality of the machine learning output. 

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4. Security Vulnerabilities of Smart Meters in Smart Grid

   https://doi.org/10.1109/IECON.2019.8926992  

   Gui, Yutian; Siddiqui, Ali Shuja; Tamore, Suyash Mohan; Saqib, Fareena (October 2019, Security Vulnerabilities of Smart Meters in Smart Grid)

   Integration of complex and high-speed electronic components in the state of art electric power system enhances the need for improved security infrastructure and resilience against invasive and non-invasive attacks on the smart grid. A modern smart grid system integrates a variety of instruments and standards to achieve cost-effective and time-effective energy measurement and management. As the fundamental component in the smart grid, the smart meter supports real-time monitoring, automatic control, and high-speed communication along with power consumption recording. However, the wide use of smart meters also increases privacy and security concerns. In this paper, we demonstrate the vulnerability of side-channel attacks on secure communication in smart grids for software-based and hardware-based implementations. 

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5. Stealthy Tracking of Autonomous Vehicles with Cache Side Channels

   Luo, Mulong Luo; Myers, Andrew C.; Suh, G. Edward (August 2000, USENIX Security Symposium)

   null (Ed.)

   Autonomous vehicles are becoming increasingly popular, but their reliance on computer systems to sense and operate in the physical world introduces new security risks. In this paper, we show that the location privacy of an autonomous vehicle may be compromised by software side-channel attacks if localization software shares a hardware platform with an attack program. In particular, we demonstrate that a cache side-channel attack can be used to infer the route or the location of a vehicle that runs the adaptive Monte-Carlo localization (AMCL) algorithm. The main contributions of the paper are as follows. First, we show that adaptive behaviors of perception and control algorithms may introduce new side-channel vulnerabilities that reveal the physical properties of a vehicle or its environment. Second, we introduce statistical learning models that infer the AMCL algorithm's state from cache access patterns and predict the route or the location of a vehicle from the trace of the AMCL state. Third, we implement and demonstrate the attack on a realistic software stack using real-world sensor data recorded on city roads. Our findings suggest that autonomous driving software needs strong timing-channel protection for location privacy. 

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