The initial phase of the deployment of Vehicular Ad-Hoc Networks (VANETs) has begun and many research challenges still need to be addressed. Location privacy continues to be in the top of these challenges. Indeed, both academia and industry agreed to apply the pseudonym changing approach as a solution to protect the location privacy of VANETs’ users. However, due to the pseudonyms linking attack, a simple changing of pseudonym shown to be inefficient to provide the required protection. For this reason, many pseudonym changing strategies have been suggested to provide an effective pseudonym changing. Unfortunately, the development of an effective pseudonym changing strategy for VANETs is still an open issue. In this paper, we present a comprehensive survey and classification of pseudonym changing strategies. We then discuss and compare them with respect to some relevant criteria. Finally, we highlight some current researches, and open issues and give some future directions.

Advances in Vehicle-to-Everything (V2X) technology and onboard sensors have significantly accelerated deploying Connected and Automated Vehicles (CAVs). Integrating V2X with 5G has enabled Ultra-Reliable Low Latency Communications (URLLC) to CAVs. However, while communication performance has been enhanced, security and privacy issues have increased. Attacks have become more aggressive, and attackers have become more strategic. Public Key Infrastructure (PKI) proposed by standardization bodies cannot solely defend against these attacks. Thus, in complementary of that, sophisticated systems should be designed to detect such attacks and attackers. Machine Learning (ML) has recently emerged as a key enabler to secure future roads. Various V2X Misbehavior Detection Systems (MDSs) have adopted this paradigm. However, analyzing these systems is a research gap, and developing effective ML-based MDSs is still an open issue. To this end, this paper comprehensively surveys and classifies ML-based MDSs as well as discusses and analyses them from security and ML perspectives. It also provides some learned lessons and recommendations for guiding the development, validation, and deployment of ML-based MDSs. Finally, this paper highlighted open research and standardization issues with some future directions.

The Internet of Vehicles (IoV) is a crucial technology for Intelligent Transportation Systems (ITS) that integrates vehicles with the Internet and other entities. The emergence of 5 G and the forthcoming 6 G networks presents an enormous potential to transform the IoV by enabling ultra-reliable, low-latency, and high-bandwidth communications. Nevertheless, as connectivity expands, cybersecurity threats have become a significant concern. The issue has been further exacerbated by the rising number of zero-day (0-day) attacks, which can exploit unknown vulnerabilities and bypass existing Intrusion Detection Systems (IDSs). In this paper, we propose Zero-X, an innovative security framework that effectively detects both 0-day and N-day attacks. The framework achieves this by combining deep neural networks with Open-Set Recognition (OSR). Our approach introduces a novel scheme that uses blockchain technology to facilitate trusted and decentralized federated learning (FL) of the Zero-X framework. This scheme also prioritizes privacy preservation, enabling both CAVs and Security Operation Centers (SOCs) to contribute their unique knowledge while protecting the privacy of their sensitive data. To the best of our knowledge, this is the first work to leverage OSR in combination with privacy-preserving FL to identify both 0-day and N-day attacks in the realm of IoV. The in-depth experiments on two recent network traffic datasets show that the proposed framework achieved a high detection rate while minimizing the false positive rate. Comparison with related work showed that the Zero-X framework outperforms existing solutions.

Protecting the location privacy is one of the main challenges in vehicular ad-hoc networks (VANETs). Although, standardization bodies, such as IEEE and ETSI, have adopted a pseudonym-based scheme as a solution for this problem, an efficient pseudonym changing and management is still an open issue. In this paper, we propose PRIVANET, a complete and efficient pseudonym changing and management framework. The PRIVANET has a hierarchical structure and considers the vehicular geographic area as a grid. Each cell of this grid contains one or many logical zones, called vehicular location privacy zones (VLPZs). These zones can easily be deployed over the widespread roadside infrastructures (RIs), such as gas stations, to provide a secure changing and management of pseudonyms. The proposed framework consists of different building blocks: 1) an effective VLPZ-based pseudonym changing strategy; 2) a reputation-based mechanism to motivate selfish vehicles to enter VLPZs; 3) an adapted user-centric privacy model; 4) a secure hybrid mechanism for the distribution of pseudonyms sets and CRLs; 5) a method to generate the IP and MAC addresses from the pseudonym; 6) a stochastic model to estimate the number of VLPZs required at a given cell; and 7) a mathematical model for an optimal placement of the VLPZs over RIs to reduce the transportation cost of vehicles in terms of time. An extensive simulation study using a realistic map and with real traffic mobility measurements is carried out to evaluate and validate the performance of the PRIVANET. The simulation results demonstrate the effectiveness of the proposed framework.