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Abstract Aircraft cybersecurity efforts have tended to focus at the strategic or tactical levels without a clear connection between the two. ...CSSEP’s process model postulates that security is best achieved by a balance of cybersecurity, cyber resiliency, defensibility, and recoverability and that control is best established by developing security constraints versus attempting to find every vulnerability. ...CSSEP identifies the major functions needed to do effective aircraft cybersecurity and provides a flexible framework as the “missing link” to connect the strategic and tactical levels of aircraft cybersecurity.

Abstract Connected autonomous vehicles that employ internet connectivity are technologically complex, which makes them vulnerable to cyberattacks. Many cybersecurity researchers, white hat hackers, and black hat hackers have discovered numerous exploitable vulnerabilities in connected vehicles. ...This study expanded the technology acceptance model (TAM) to include cybersecurity and level of trust as determinants of technology acceptance. This study surveyed a diverse sample of 209 licensed US drivers over 18 years old.

Abstract The United Nation Economic Commission for Europe (UNECE) Regulation 155—Cybersecurity and Cybersecurity Management System (UN R155) mandates the development of cybersecurity management systems (CSMS) as part of a vehicle’s lifecycle. ...Due to the focus of R155 and its suggested implementation guideline, ISO/SAE 21434:2021—Road Vehicle Cybersecurity Engineering, mainly centering on the alignment of cybersecurity risk management to the vehicle development lifecycle, there is a gap in knowledge of proscribed activities for validation and verification testing. ...An inherent component of the CSMS is cybersecurity risk management and assessment. Validation and verification testing is a key activity for measuring the effectiveness of risk management, and it is mandated by UN R155 for type approval.

Ensuring cybersecurity in an ECU network is challenging as there is no centralized authority in the vehicle to provide security as a service. ...While progress has been made to address cybersecurity vulnerabilities, many of these approaches have focused on enterprise, software-centric systems and require more computational resources than typically available for onboard vehicular devices.

Abstract Automotive cybersecurity is steadily becoming a key factor in the worldwide adoption of connected and self-driving vehicles.

Abstract Cyberattacks on financial and government institutions, critical infrastructure, voting systems, businesses, modern vehicles, and so on are on the rise. Fully connected autonomous vehicles are more vulnerable than ever to hacking and data theft. This is due to the fact that the industry still relies on controller area network (CAN) protocol for in-vehicle control networks. The CAN protocol lacks basic security features such as message authentication, which makes it vulnerable to a wide range of attacks including spoofing attacks. This article presents a novel method to protect CAN protocol against packet spoofing, replay, and denial of service (DoS) attacks. The proposed method exploits physical uncolonable attributes in the physical channel between transmitting and destination nodes and uses them for linking the received packet to the source.

Abstract ICVs are expected to make the transportation safer, cleaner, and more comfortable in the near future. However, the trend of connectivity has greatly increased the attack surfaces of vehicles, which makes in-vehicle networks more vulnerable to cyberattacks which then causes serious security and safety issues. In this article, we therefore systematically analyzed cyberattacks and corresponding countermeasures for in-vehicle networks of intelligent and connected vehicles (ICVs). Firstly, we analyzed the security risk of ICVs and proposed an in-vehicle network model from a hierarchical point of view. Then, we discussed possible cyberattacks at each layer of proposed network model.

Abstract In the automotive domain, the overall complexity of technical components has increased enormously. Formerly isolated, purely mechanical cars are now a multitude of cyber-physical systems that are continuously interacting with other IT systems, for example, with the smartphone of their driver or the backend servers of the car manufacturer. This has huge security implications as demonstrated by several recent research papers that document attacks endangering the safety of the car. However, there is, to the best of our knowledge, no holistic overview or structured description of the complex automotive domain. Without such a big picture, distinct security research remains isolated and is lacking interconnections between the different subsystems. Hence, it is difficult to draw conclusions about the overall security of a car or to identify aspects that have not been sufficiently covered by security analyses.

Abstract Threat identification and security analysis have become mandatory steps in the engineering design process of high-assurance systems, where successful cyberattacks can lead to hazardous property damage or loss of lives. This article describes a novel approach to perform security analysis on embedded systems modeled at the architectural level. The tool, called Security Threat Evaluation and Mitigation (STEM), associates threats from the Common Attack Pattern Enumeration and Classification (CAPEC) library with components and connections and suggests potential defense patterns from the National Institute of Standards and Technology (NIST) Special Publication (SP) 800-53 security standard. This article also provides an illustrative example based on a drone package delivery system modeled in AADL.

Abstract Automotive software is increasingly complex and critical to safe vehicle operation, and related embedded systems must remain up to date to ensure long-term system performance. Update mechanisms and data modification tools introduce opportunities for malicious actors to compromise these cyber-physical systems, and for trusted actors to mistakenly install incompatible software versions. A distributed and stratified “black box” audit trail for automotive software and data provenance is proposed to assure users, service providers, and original equipment manufacturers (OEMs) of vehicular software integrity and reliability. The proposed black box architecture is both layered and diffuse, employing distributed hash tables (DHT), a parity system and a public blockchain to provide high resilience, assurance, scalability, and efficiency for automotive and other high-assurance systems.

Abstract The importance of in-vehicle network security has increased with an increase in automated and connected vehicles. Hence, many attacks and countermeasures have been proposed to secure the controller area network (CAN), which is an existent in-vehicle network protocol. At the same time, new protocols-such as FlexRay and Ethernet-which are faster and more reliable than CAN have also been proposed. European OEMs have adopted FlexRay as a control network that can perform the fundamental functions of a vehicle. However, there are few studies regarding FlexRay security. In particular, studies on attacks against FlexRay are limited to theoretical studies or simulation-based experiments. Hence, the vulnerability of FlexRay is unclear. Understanding this vulnerability is necessary for the application of countermeasures and improving the security of future vehicles. In this article, we highlight the vulnerability of FlexRay found in the experiments conducted on a real FlexRay network.

Abstract Vehicle to Everything (V2X) allows vehicles, pedestrians, and infrastructure to share information for the purpose of preventing accidents, enhancing road safety, and improving the efficiency and energy consumption of transportation. Although V2X messages are authenticated, their content is not validated. Sensor errors or adversarial attacks can cause messages to be perturbed and, therefore, increase the likelihood of traffic jams, compromise the decision process of other vehicles, or provoke fatal crashes. In this article, we introduce V2X Core Anomaly Detection System (VCADS), a system based on the theory presented in [1] and built for the fields provided in the periodic messages shared across vehicles (i.e., Basic Safety Messages, BSMs). VCADS uses physics-based models to constrain the values in each field and detect anomalies by finding the numerical difference between a field and independent derivations of the same field.

Abstract Testing and verifying the security of connected and autonomous vehicles (CAVs) under cyber-physical attacks is a critical challenge for ensuring their safety and reliability. Proposed in this article is a novel testing framework based on a model of computation that generates scenarios and attacks in a closed-loop manner, while measuring the safety of the unit under testing (UUT), using a verification vector. The framework was applied for testing the performance of two cooperative adaptive cruise control (CACC) controllers under false data injection (FDI) attacks. Serving as the baseline controller is one of a traditional design, while the proposed controller uses a resilient design that combines a model and learning-based algorithm to detect and mitigate FDI attacks in real-time.

Abstract The rapid development of connected and automated vehicle technologies together with cloud-based mobility services is transforming the transportation industry. As a result, huge amounts of consumer data are being collected and utilized to provide personalized mobility services. Using big data poses serious challenges to data privacy. To that end, the risks of privacy leakage are amplified by data aggregations from multiple sources and exchanging data with third-party service providers, in face of the recent advances in data analytics. This article provides a review of the connected vehicle landscape from case studies, system characteristics, and dataflows. It also identifies potential challenges and countermeasures.

Abstract Over the past forty years, the Electronic Control Unit (ECU) technology has grown in both sophistication and volume in the automotive sector, and modern vehicles may comprise hundreds of ECUs. ECUs typically communicate via a bus-based network architecture to collectively support a broad range of safety-critical capabilities, such as obstacle avoidance, lane management, and adaptive cruise control. However, this technology evolution has also brought about risks: if ECU firmware is compromised, then vehicle safety may be compromised. Recent experiments and demonstrations have shown that ECU firmware is not only poorly protected but also that compromised firmware may pose safety risks to occupants and bystanders.

Abstract Vehicular communications face unique security issues in wireless communications. While new vehicles are equipped with a large set of communication technologies, product life cycles are long and software updates are not widespread. The result is a host of outdated and unpatched technologies being used on the street. This has especially severe security impacts because autonomous vehicles are pushing into the market, which will rely, at least partly, on the integrity of the provided information. We provide an overview of the currently deployed communication systems and their security weaknesses and features to collect and compare widely used security mechanisms. In this survey, we focus on technologies that work in an ad hoc manner. This includes Long-Term Evolution mode 4 (LTE-PC5), Wireless Access in Vehicular Environments (WAVE), Intelligent Transportation Systems at 5 Gigahertz (ITS-G5), and Bluetooth.

Abstract In the pursuit of advancing autonomous vehicles (AVs), data-driven algorithms have become pivotal in replacing human perception and decision-making. While deep neural networks (DNNs) hold promise for perception tasks, the potential for catastrophic consequences due to algorithmic flaws is concerning. A well-known incident in 2016, involving a Tesla autopilot misidentifying a white truck as a cloud, underscores the risks and security vulnerabilities. In this article, we present a novel threat model and risk assessment (TARA) analysis on AV data storage, delving into potential threats and damage scenarios. Specifically, we focus on DNN parameter manipulation attacks, evaluating their impact on three distinct algorithms for traffic sign classification and lane assist.