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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 Trust in the digital data from heavy vehicle event data recorders (HVEDRs) is paramount to using the data in legal contests. Ensuring the trust in the HVEDR data requires an examination of the ways the digital information can be attacked, both purposefully and inadvertently. The goal or objective of an attack on HVEDR data will be to have the data omitted in a case. To this end, we developed an attack tree and establish a model for violating the trust needed for HVEDR data. The attack tree provides context for mitigations and also for functional requirements. A trust model is introduced as well as a discussion on what constitutes forensically sound data. The main contribution of this article is an attack tree-based model of both malicious and accidental events contributing to compromised event data recorder (EDR) data. A comprehensive list of mitigations for HVEDR systems results from this analysis.

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 “It cannot happen to us” is one of many common myths regarding cybersecurity in the transportation industry. The traditional view that the threats to transportation are low probability and low impact keep agencies from mitigating security threats to transportation critical infrastructure.

Abstract Despite more and more rigorous defense mechanisms in place for cyber-physical systems, cybercriminals are increasingly attacking systems for benefits using a variety of means including malware, phishing, ransomware, and denial of service. Cyberattacks could not only cause significant economic loss but also disastrous consequences for individuals and organizations. Therefore, it is advantageous to detect and fix potential cyber vulnerabilities before the system is fielded. To this end, this article presents a language, VERDICT, and a novel framework, Cyber Vulnerability Analysis Framework (CyVAF) to (i) define cyber threats and mitigation defenses based on system properties, (ii) detect cyber vulnerabilities of system architecture automatically, and also (iii) suggest mitigation defenses. VERDICT is developed as an annex to the Architecture Analysis and Design Language (AADL) but can also be used independently.

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 Connected vehicle technology consisting of Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication falls under the umbrella of V2X, or Vehicle-to-Everything, communication. This enables vehicles and infrastructure to exchange safety-related information to enable smarter, safer roads. If driver alerts are raised or automated action is taken as a result of these messages, it is critical that messages are trustworthy and reliable. To this end, the Security Credential Management System (SCMS) and Cooperative Intelligent Transportation Systems (C-ITS) Credential Management System (CCMS) have been proposed to enable authentication and authorization of V2X messages without compromising individual user privacy. This is accomplished by issuing each vehicle a large set of “pseudonyms,” unrelated to any real-world identity. During operation, the vehicle periodically switches pseudonyms, thereby changing its identity to others in the network.

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 Heavy vehicles are essential for the modern economy, delivering critical food, supplies, and freight throughout the world. Connected heavy vehicles are also driven by embedded computers that utilize internal communication using common standards. However, some implementations of the standards leave an opening for a malicious actor to abuse the system. One such abuse case is a cyber-attack known as the “Address Claim Attack.” Proposed in 2018, this attack uses a single network message to disable all communication to and from a target electronic control unit, which may have a detrimental effect on operating the vehicle. This article demonstrates the viability of the attack and then describes the implementation of a solution to prevent this attack in real time without requiring any intervention from the manufacturer of the target devices. The defense technique uses a bit-banged Controller Area Network (CAN) filter to detect the attack.

Abstract Connected vehicles and intelligent transportation systems are currently evolving into highly interconnected digital environments. Due to the interconnectivity of different systems and complex communication flows, a joint risk analysis for combining safety and security from a system perspective does not yet exist. We introduce a novel method for joint risk assessment in the automotive sector as a combination of the Diamond Model, Failure Mode and Effects Analysis (FMEA), and Factor Analysis of Information Risk (FAIR). These methods have been sequentially composed, which results in a comprehensive risk management approach to information security in an intelligent transport system (ITS). The Diamond Model serves to identify and structurally describe threats and scenarios, the widely accepted FMEA provides threat analysis by identifying possible error combinations, and FAIR provides a quantitative estimation of probabilities for the frequency and magnitude of risk events.

Abstract With the recent advancement in technologies, researchers worldwide have a growing interest in unmanned aerial vehicles (UAVs). The last few years have been significant in terms of its global awareness, adoption, and applications across industries. In UAV-aided wireless networks, there are some limitations in terms of power consumption, data computation, data processing, endurance, and security. So, the idea of UAVs and Edge or Fog computing together deals with the limitations and provides intelligence at the network’s edge, which makes it more valuable to use in emergency applications. Fog computing distributes data in a decentralized way and blockchain also works on the principle of decentralization. Blockchain, as a decentralized database, uses cryptographic methods including hash functions and public key encryption to secure the user information. It is a prominent solution to secure the user’s information in blocks and maintain privacy.

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 With the adoption of Vehicle-to-everything (V2X) technology, security and privacy of vehicles are paramount. To avoid tracking while preserving vehicle/driver’s privacy, modern vehicular public key infrastructure provision vehicles with multiple short-term pseudonym certificates. However, provisioning a large number of pseudonym certificates can lead to an enormous growth of Certificate Revocation Lists (CRLs) during its revocation process. One possible approach to avoid such CRL growth is by relying on activation code (AC)-based solutions. In such solutions, the vehicles are provisioned with batches of encrypted certificates, which are decrypted periodically via the ACs (broadcasted by the back-end system). When the system detects a revoked vehicle, it simply does not broadcast the respective vehicle’s AC. As a result, revoked vehicles do not receive their respective AC and are prevented from decrypting their certificates.

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.