Safety applications in Vehicular Ad-hoc Networks (VANETs) often require vehicles to share information such as current position, speed, and vehicle status on a regular basis. This information can be collected to obtain private information about vehicles/drivers, such as home or office locations and frequently visited places, creating serious privacy vulnerabilities. The use of pseudonyms, rather than actual vehicle IDs, can alleviate this problem and several different Pseudonym Management Techniques (PMTs) have been proposed in the literature. These PMTs are typically evaluated assuming a random placement of attacking stations. However, an adversary can utilize knowledge of traffic patterns and PMTs to place eavesdropping stations in a more targeted manner, leading to an increased tracking success rate. In this paper, we propose two new adversary placement strategies and study the impact of intelligent adversary placement on tracking success using different PMTs. The results indicate that targeted placement of attacking stations, based on traffic patterns, road type, and knowledge of PMT used, can significantly increase tracking success. Therefore, it is important to take this into consideration when developing PMTs that can protect vehicle privacy even in the presence of targeted placement techniques.

In a vehicular ad‐hoc network, vehicles periodically broadcast safety messages to neighboring vehicles containing information about their identity, location, speed, and other relevant information. The time‐critical nature of these messages means they are typically sent without encryption, which can compromise the privacy of the vehicle/driver. The use of pseudonyms, rather than the actual identity, has been suggested as a way to implement location privacy for drivers. However, such pseudonyms need to change frequently in order to prevent vehicle tracking. A pseudonym changing strategy (PCS) determines how frequently and under which conditions a vehicle should change its pseudonym. Many different PCS have been proposed in the literature over the last decade. However, there is a lack of a systematic analysis of the performance of these techniques, using a set of well‐defined privacy metrics. In this article, we provide a thorough comparison of four main types of PCS, in terms of standard privacy metrics, and identify the advantages and limitations of each approach under different traffic conditions and attacker capabilities. The ultimate aim of this study is to help in the determination of the best PCS to use in different situations, to maximize the location privacy.

The automotive industry’s rapid adoption of electric vehicles (EVs) and advanced networking introduces new avenues for performance enhancement but also creates significant cybersecurity risks. As EVs integrate complex cyber-physical systems, ensuring their safety and reliable operation especially within the context of Connected and Autonomous Vehicles (CAVs) is critical, given the substantial threat posed by cyberattacks to both vehicle infrastructure and power grids. A single compromised Electronic Control Unit (ECU) or infected vehicular network could cascade into a fleet-wide security risk. This paper performs a comprehensive vulnerability analysis to evaluate the cyber risks associated with various modern vehicle modules and assesses their potential risk impact levels, detailing several common attack pathways. We also discuss security testing methods, including penetration testing and dynamic analysis, and highlight the critical role and potential of fuzzing in automotive cybersecurity. Furthermore, we conduct a detailed comparative analysis of existing fuzzers, assessing their current suitability for automotive industrial use. Finally, we propose a novel fuzzing-based security framework that specifically integrates and addresses automotive security concerns by evaluating the security performance against the fundamental pillars of Confidentiality, Integrity, and Availability, ensuring reliability and safety of vehicle systems.