5G Vehicle-to-Everything at the Cross-Borders: Security Challenges and Opportunities

in IEEE Internet of Things Journal (2022)

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 ... [more ▼]

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. [less ▲]

The Impact Of Distributed Data Preprocessing On Automotive Data Streams

in 2022 IEEE 96th Vehicular Technology Conference: (VTC2022-Fall) (2022, September)

Vehicles have transformed into sophisticated com- puting machines that not only serve the objective of transporta- tion from point A to point B but serve other objectives including improved experience ... [more ▼]

Vehicles have transformed into sophisticated com- puting machines that not only serve the objective of transporta- tion from point A to point B but serve other objectives including improved experience, safer journey, automated and more efficient and sustainable transportation. With such sophistication comes complex applications and enormous volumes of data generated from diverse types of vehicle sensors and components. Automotive data is not sedentary but moves from the edge (the vehicle) to the cloud (e.g., infrastructure of the vehicle manufacturers, national highway agencies, insurance companies, etc.). The exponential increase in data volume and variety generated in modern vehicles far exceeds the rate of infrastructure scaling and expansion. To mitigate this challenge, the computational and storage capacities of vehicle components can be leveraged to perform in-vehicle operations on the data to either prepare and transform (prepro- cess) the data or extract information from (process) the data. This paper focuses on distributing data preprocessing to the vehicle and highlights the benefits and impact of the distribution including on the consumption of resources (e.g., energy). [less ▲]

Preventing Frame Fingerprinting in Controller Area Network Through Traffic Mutation

in IEEE ICC 2022 Workshop - DDINS, Seoul 16-20 May 2022 (2022, May)

The continuous increase of connectivity in commercial vehicles is leading to a higher number of remote access points to the Controller Area Network (CAN) – the most popular in-vehicle network system. This ... [more ▼]

The continuous increase of connectivity in commercial vehicles is leading to a higher number of remote access points to the Controller Area Network (CAN) – the most popular in-vehicle network system. This factor, coupled with the absence of encryption in the communication protocol, poses serious threats to the security of the CAN bus. Recently, it has been demonstrated that CAN data can be reverse engineered via frame fingerprinting, i.e., identification of frames based on statistical traffic analysis. Such a methodology allows fully remote decoding of in-vehicle data and paves the way for remote pre-compiled vehicle-agnostic attacks. In this work, we propose a first solution against CAN frame fingerprinting based on mutating the traffic without applying modifications to the CAN protocol. The results show that the proposed methodology halves the accuracy of CAN frame fingerprinting. [less ▲]

Transforming IoT Data Preprocessing: A Holistic, Normalized and Distributed Approach

in The Fifth International Workshop on Data: Acquisition To Analysis (2022)

Data preprocessing is an integral part of Artificial Intelligence (AI) pipelines. It transforms raw data into input data that fulfill algorithmic criteria and improve prediction accuracy. As the adoption ... [more ▼]

Data preprocessing is an integral part of Artificial Intelligence (AI) pipelines. It transforms raw data into input data that fulfill algorithmic criteria and improve prediction accuracy. As the adoption of Internet of Things (IoT) gains more momentum, the data volume generated from the edge is exponentially increasing that far exceeds any expansion of infrastructure. Social responsibilities and regulations (e.g., GDPR) must also be adhered when handling IoT data. In addition, we are currently witnessing a shift towards distributing AI to the edge. The aforementioned reasons render the distribution of data preprocessing to the edge an urgent requirement. In this paper, we introduce a modern data preprocessing framework that consists of two main parts. Part1 is a design tool that reduces the complexity and costs of the data preprocessing phase for AI via generalization and normalization. The design tool is a standard template that maps specific techniques into abstract categories and highlights dependencies between them. In addition, it presents a holistic notion of data preprocessing that is not limited to data cleaning. The second part is an IoT tool that adopts the edge-cloud collaboration model to progressively improve the quality of the data. It includes a synchronization mechanism that ensures adaptation to changes in data characteristics and a coordination mechanism that ensures correct and complete execution of preprocessing plans between the cloud and the edge. The paper includes an empirical analysis of the framework using a developed prototype and an automotive use-case. Our results demonstrate reductions in resource consumption (e.g., energy, bandwidth) while maintaining the value and integrity of the data. [less ▲]

CANMatch: A Fully Automated Tool for CAN Bus Reverse Engineering based on Frame Matching

in IEEE Transactions on Vehicular Technology (2021)

Controller Area Network (CAN) is the most frequently used in-vehicle communication system in the automotive industry today. The communication inside the CAN bus is typically encoded using proprietary ... [more ▼]

Controller Area Network (CAN) is the most frequently used in-vehicle communication system in the automotive industry today. The communication inside the CAN bus is typically encoded using proprietary formats in order to prevent easy access to the information exchanged on the bus. However, it is still possible to decode this information through reverse engineering, performed either manually or via automated tools. Existing automated CAN bus reverse engineering methods are still time-consuming and require some manual effort, i.e., to inject diagnostic messages in order to trigger specific responses. In this paper, we propose CANMatch a fully automated CAN bus reverse engineering framework that does not require any manual effort and significantly decreases the execution time by exploiting the reuse of CAN frames across different vehicle models. We evaluate the proposed solution on a dataset of CAN logs, or traces, related to 479 vehicles from 29 different automotive manufacturers, demonstrating its improved performance with respect to the state of the art. [less ▲]

A Near-Field-based TPMS Solution for Heavy Commercial Vehicle Environement

in 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall) proceedings (2021, September 27)

Evaluation of TPMS Signal Propagation in a Heavy Commercial Vehicle Environement

E-print/Working paper (2021)

Federated Learning-based Scheme for Detecting Passive Mobile Attackers in 5G Vehicular Edge Computing

in Annals of Telecommunications (2021)

Detecting passive attacks is always considered difficult in vehicular networks. Passive attackers can eavesdrop on the wireless medium to collect beacons. These beacons can be exploited to track the ... [more ▼]

Detecting passive attacks is always considered difficult in vehicular networks. Passive attackers can eavesdrop on the wireless medium to collect beacons. These beacons can be exploited to track the positions of vehicles not only to violate their location privacy but also for criminal purposes. In this paper, we propose a novel federated learning-based scheme for detecting passive mobile attackers in 5G Vehicular Edge Computing. We first identify a set of strategies that can be used by attackers to efficiently track vehicles without being visually detected. We then build an efficient Machine Learning (ML) model to detect tracking attacks based only on the receiving beacons. Our scheme enables Federated Learning (FL) at the edge to ensure collaborative learning while preserving the privacy of vehicles. Moreover, FL clients use a semi-supervised learning approach to ensure accurate self-labeling. Our experiments demonstrate the effectiveness of our proposed scheme to detect passive mobile attackers quickly and with high accuracy. Indeed, only 20 received beacons are required to achieve 95\% accuracy. This accuracy can be achieved within 60 FL rounds using 5 FL clients in each FL round. The obtained results are also validated through simulations. [less ▲]

Intelligent Misbehavior Detection System for Detecting False Position Attacks in Vehicular Networks

in Hawlader, Faisal; Boualouache, Abdelwahab; Faye, Sébastien (Eds.) et al The 2021 IEEE International Conference on Communications (the 4th Workshop on 5G and Beyond Wireless Security) (2021, June)

Position falsification attacks are one of the most dangerous internal attacks in vehicular networks. Several Machine Learning-based Misbehavior Detection Systems (ML-based MDSs) have recently been proposed ... [more ▼]

Position falsification attacks are one of the most dangerous internal attacks in vehicular networks. Several Machine Learning-based Misbehavior Detection Systems (ML-based MDSs) have recently been proposed to detect these attacks and mitigate their impact. However, existing ML-based MDSs require numerous features, which increases the computational time needed to detect attacks. In this context, this paper introduces a novel ML-based MDS for the early detection of position falsification attacks. Based only on received positions, our system provides real-time and accurate predictions. Our system is intensively trained and tested using a publicly available data set, while its validation is done by simulation. Six conventional classification algorithms are applied to estimate and construct the best model based on supervised learning. The results show that the proposed system can detect position falsification attacks with almost 100% accuracy. [less ▲]

Adaptive Content Seeding for Information-Centric Networking under High Topology Dynamics: Where You Seed Matters

in IEEE Vehicular Technology Magazine (2021), 16(2),

High-fidelity content distribution and other emerging applications of 5G and beyond-5G mobile broadband networking can put massive load on the core and Radio Access Network (RAN). To address this, direct ... [more ▼]

High-fidelity content distribution and other emerging applications of 5G and beyond-5G mobile broadband networking can put massive load on the core and Radio Access Network (RAN). To address this, direct Device to Device (D2D) communication has recently become a first-class citizen of these networks. While Information-Centric Vehicular Networking (ICVN) based on fog computing can indeed exploit such D2D links to alleviate the load on the RAN by proactively seeding content in the network, it has been shown that such seeding can cause even more load if performed where not needed. In addition, trying to determine where to seed content often causes additional load, negating the benefit of seeding. In this work, we therefore propose to adaptively seed fog nodes based on a purely virtual clustering approach. Here, vehicles are unaware of clustering decisions, thus no longer requiring an explicit exchange of control messages. We show that the benefit of such an adaptive approach goes beyond simply being able to flexibly trade off performance metrics versus each other: instead, it can consistently lower the load on the RAN link. We also show that this property even holds if node location information is only available as coarsely-grained as macro-scale grid cells. [less ▲]