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As the automotive industry grows, modern vehicles will be connected to 5G networks, creating a new Vehicular-to-Everything (V2X) ecosystem. Network Slicing (NS) supports this 5G-V2X ecosystem by enabling network operators to flexibly provide dedicated logical networks addressing use case specific-requirements on top of a shared physical infrastructure. Despite its benefits, NS is highly vulnerable to privacy and security threats, which can put Connected and Automated Vehicles (CAVs) in dangerous situations. Deep Learning-based Intrusion Detection Systems (DL-based IDSs) have been proposed as the first defense line to detect and report these attacks. However, current DL-based IDSs are processing and memory-consuming, increasing security costs and jeopardizing 5G-V2X acceptance. To this end, this paper proposes a lightweight intrusion detection scheme for 5G-V2X sliced networks. Our scheme leverages DL and Knowledge Distillation (KD) for training in the cloud and offloading knowledge to slice-tailored lightweight DL models running on CAVs. Our results show that our scheme provides an optimal trade-off between detection accuracy and security overhead. Specifically, it can reduce security overhead in computation and memory complexity to more than 50% while keeping almost the same performance as heavy DL-based IDSs.

Building accurate Machine Learning (ML) attack detection models for 5G and Beyond (5GB) vehicular networks requires collaboration between Vehicle-to-Everything (V2X) nodes. However, while operating collaboratively, ensuring the ML model's security and data privacy is challenging. To this end, this article proposes a secure and privacy-preservation on-demand framework for building attack-detection ML models for 5GB vehicular networks. The proposed framework emerged from combining 5GB technologies, namely, Federated Learning (FL), blockchain, and smart contracts to ensure fair and trusted interactions between FL servers (edge nodes) with FL workers (vehicles). Moreover, it also provides an efficient consensus algorithm with an intelligent incentive mechanism to select the best FL workers that deliver highly accurate local ML models. Our experiments demonstrate that the framework achieves higher accuracy on a well-known vehicular dataset with a lower blockchain consensus time than related solutions. Specifically, our framework enhances the accuracy by 14% and decreases the consensus time, at least by 50%, compared to related works. Finally, this article discusses the framework's key challenges and potential solutions.

Unlike previous mobile networks, 5G and beyond (B5G) networks are expected to be the key enabler of various vertical industries such as eHealth, intelligent transportation, and Industrial IoT verticals. To support that, B5G networks enable to sharing of common physical resources (radio, computation, network) among different tenants, thanks to network slicing concept and network softwarization technologies, including Software Defined Networking (SDN) and Network Function Virtualization (NFV). Therefore, new research challenges related to B5G networks have emerged, such as resources management and orchestration, service chaining, security, and QoS management. However, there is a lack of a realistic platform enabling researchers to design and validate their solutions effectively, since B5G networks are still in their early stages. In this paper, we first discuss the different methods for deploying realistic B5G platforms for the V2X vertical, including the key B5G technologies. Then, we describe DRIVE-B5G, a novel platform that serves as an end-to-end test-bed to emulate a vehicular network environment, allowing researchers to provide proof of concept, validate, and evaluate their research approaches.

5G Vehicle-to-Everything (5G-V2X) communications will play a vital role in the development of the automotive industry. Indeed and thanks to the Network Slicing (NS) concept of 5G and beyond networks (B5G), unprecedented new vehicular use–cases can be supported on top of the same physical network. NS promises to enable the sharing of common network infrastructure and resources while ensuring strict traffic isolation and providing necessary network resources to each NS. However, enabling NS in vehicular networks brings new security challenges and requirements that automotive or 5G standards have not yet addressed. Attackers can exploit the weakest link in the slicing chain, connected and automated vehicles, to violate the slice isolation and degrade its performance. Furthermore, these attacks can be more powerful, especially if they are produced in cross-border areas of two countries, which require an optimal network transition from one operator to another. Therefore, this article aims to provide an overview of newly enabled 5G-V2X slicing use cases and their security issues while focusing on cross-border slicing attacks. It also presents the open security issues of 5G-V2X slicing and identifies some opportunities.