Modern vehicles are more complex and interconnected than ever before, which also means that attack surfaces for vehicles have increased significantly. Malicious cyberattacks will not only exploit personal privacy and property, but also affect the functional safety of electrical/electronic (E/E) safety-critical systems by controlling the driving functionality, which is life-threatening. Therefore, it is necessary to conduct cybersecurity testing on vehicles to reveal and address relevant security threats and vulnerabilities. Cybersecurity standards and regulations issued in recent years, such as ISO/SAE 21434 and UNECE WP.29 regulations (R155 and R156), also emphasize the indispensability of cybersecurity verification and validation in the development lifecycle but lack specific technical details. Thus, this paper conducts a systematic and comprehensive review of the research and practice in the field of automotive cybersecurity testing, which can provide reference and advice for automotive security researchers and testers. We classify and discuss the security testing methods and testbeds in automotive engineering. Furthermore, we identify gaps and limitations in existing research and point out future challenges.

Developments in connected and autonomous vehicle technologies provide drivers with many convenience and safety benefits. Unfortunately, as connectivity and complexity within vehicles increase, more entry points or interfaces that may directly or indirectly access in-vehicle networks (IVNs) have been introduced, causing a massive rise in security risks. An intrusion detection system (IDS) is a practical method for controlling malicious attacks while guaranteeing real-time communication. Regarding the ever-evolving security attacks on IVNs, researchers have paid more attention to employing deep learning-based techniques to deal with privacy concerns and security threats in the IDS domain. Therefore, this article comprehensively reviews all existing deep IDS approaches on in-vehicle networks and conducts fine-grained classification based on applied deep network architecture. It investigates how deep-learning techniques are utilized to implement different IDS models for better performance and describe their possible contributions and limitations. Further compares and discusses the studied schemes concerning different facets, including input data strategy, benchmark datasets, classification technique, and evaluation criteria. Furthermore, the usage preferences of deep learning in IDS, the influence of the dataset, and the selection of feature segments are discussed to illuminate the main potential properties for designing. Finally, possible research directions for follow-up studies are provided.

The rapid development of intelligent connected vehicles has increased the attack surface of vehicles and made the complexity of vehicle systems unprecedented. Original equipment manufacturers (OEMs) need to accurately represent and identify threats and match corresponding security requirements. Meanwhile, the fast iteration cycle of modern vehicles requires development engineers to quickly obtain cybersecurity requirements for new features in their developed systems in order to develop system code that meets cybersecurity requirements. However, existing threat identification and cybersecurity requirement methods in the automotive domain cannot accurately describe and identify threats for a new feature while also quickly matching appropriate cybersecurity requirements. This article proposes a cybersecurity requirements management system (CRMS) framework to assist OEM security experts in conducting comprehensive automated threat analysis and risk assessment and to help development engineers identify security requirements prior to software development. The proposed CRMS framework enables development engineers to quickly model their systems using the UML-based (i.e., capable of describing systems using UML) Eclipse Modeling Framework and security experts to integrate their security experience into a threat library and security requirement library expressed in Alloy formal language. In order to ensure accurate matching between the two, a middleware communication framework called the component channel messaging and interface (CCMI) framework, specifically designed for the automotive domain, is proposed. The CCMI communication framework enables the fast model of development engineers to match with the formal model of security experts for threat and security requirement matching, achieving accurate and automated threat and risk identification and security requirement matching. To validate our work, we conducted experiments on the proposed framework and compared the results with the HEAVENS approach. The results showed that the proposed framework is superior in terms of threat detection rates and coverage rates of security requirements. Moreover, it also saves analysis time for large and complex systems, and the cost-saving effect becomes more pronounced with increasing system complexity.