On-Demand Security Framework for 5GB Vehicular Networks

Building accurate Machine Learning (ML) at-tack 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 trustedinteractions between FL servers (edge nodes) with FL workers (vehicles). Moreover, it also provides an ef…

Federated Learning for Zero-Day Attack Detection in 5G and Beyond V2X Networks

Deploying Connected and Automated Vehicles (CAVs) on top of 5G and Beyond networks (5GB) makes them vulnerable to increasing vectors of security and privacy attacks. In this context, a wide range of advanced machine/deep learning-based solutions have been designed to accurately detect security attacks. Specifically, supervised learning techniques have been widely applied to train attack detection models. However, the main limitation of such solutions is their inability to detect attacks different from those seen during the training phase, or new attacks, also called zero-day attacks. Moreover, training the detection model requires significant data collection and labeling, which increases th…

Edge Computing-enabled Intrusion Detection for C-V2X Networks using Federated Learning

Intrusion detection systems (IDS) have already demonstrated their effectiveness in detecting various attacks in cellular vehicle-to-everything (C-V2X) networks, especially when using machine learning (ML) techniques. However, it has been shown that generating ML-based models in a centralized way consumes a massive quantity of network resources, such as CPU/memory and bandwidth, which may represent a critical issue in such networks. To avoid this problem, the new concept of Federated Learning (FL) emerged to build ML-based models in a distributed and collaborative way. In such an approach, the set of nodes, e.g., vehicles or gNodeB, collaborate to create a global ML model trained across thes…

A Trusted Hybrid Learning Approach to Secure Edge Computing

Securing edge computing has drawn much attention due to the vital role of edge computing in Fifth Generation (5G) wireless networks. Artificial Intelligence (AI) has been adopted to protect networks against attackers targeting the connected edge devices or the wireless channel. However, the proposed detection mechanisms could generate a high false detection rate, especially against unknown attacks defined as zero-day threats. Thereby, we propose and conceive a new hybrid learning security framework that combines the expertise of security experts and the strength of machine learning to protect the edge computing network from known and unknown attacks, while minimizing the false detection rat…

PRIVANET: An Efficient Pseudonym Changing and Management Framework for Vehicular Ad-Hoc Networks

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), su…

Hpdm : A Hybrid Pseudonym Distribution Method for Vehicular Ad-Hoc Networks

Abstract Protecting the location privacy of drivers is still one of the main challenges in Vehicular Ad-hoc Networks (VANETs). The changing of pseudonym is commonly accepted as a solution to this problem. The pseudonyms represent fake vehicle identifiers. Roadside Units (RSUs) play a central role in the existing pseudonyms distribution solutions. Indeed, the VANET area should totally be covered by RSUs in order to satisfy the demand of vehicles in terms of pseudonyms. However, the total coverage is costly and hard to be achieved, especially in the first phase of VANETs deployment. In addition, RSUs could be overloaded due to the large number of pseudonyms requests that could be received fro…

VLPZ: The Vehicular Location Privacy Zone

International audience; One of the key challenges in the success of vehicular ad hoc networks (VANETs) is to consider the location privacy of drivers. Although, the pseudonym changing approach is suggested by standardization development organizations such as IEEE and ETSI, the development of an effective pseudonym changing strategy is still an open issue. The existing pseudonym changing strategies are either not effective to protect against the pseudonyms linking attacks or can have a negative impact on the VANETs’ applications. To address these issues, this paper proposes a new pseudonym changing strategy based on the Vehicular Location Privacy Zone (VLPZ), which is a roadside infrastructu…

DRIVE-B5G: A Flexible and Scalable Platform Testbed for B5G-V2X Networks

A survey on pseudonym changing strategies for Vehicular Ad-Hoc Networks

International audience; 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 st…