Special Issue "Wireless Communications Security and Privacy for Connected and Autonomous Vehicles"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: 30 April 2023 | Viewed by 853

Special Issue Editors

European Commission, Joint Research Centre, 21027 Ispra, Italy
Interests: Android security; network security; intrusion detection; machine learning
European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy
Interests: information security and privacy; network security; critical infrastructure protection
European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy
Interests: computer security and reliability; computer communications; internet of things (IoT); federated learning; blockchain
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Special Issue Information

Dear Colleagues,

Modern radio access technologies have the potential to greatly improve the autonomous driving experience. Autonomous vehicles must be capable of several different modes of communication, so they can interact with their environment, and will require reliable, robust, and secure networks. Simply put, wireless technologies, including cellular and Wi-Fi networks, will undoubtedly play a vital role in the future of autonomous vehicles, promising high-speed data rates and low latencies for a variety of autonomous driving scenarios. Dedicated short-range communications (DSRC) (based on IEEE 802.11p) and cellular V2X (C-V2X) are two contemporary technologies that are capable of supporting vehicular applications. Moreover, evolutions of both or these technologies, namely IEEE 802.11bd for the DSRC and NR-V2X for C-V2X, are currently under constant development and refinement in order to support advanced vehicular applications characterized by high reliability, low latency, and high throughput requirements. Unfortunately, along with the rise of these radio access technologies, new security and privacy threats are being introduced. This Special Issue aims to convene state-of-the-art research in the area and help in proposing and examining advanced communication technologies for connected and autonomous vehicles.

Topics of interest include but are not limited to the following:

  • Vehicular communications with emphasis on security and privacy;
  • Advanced security technologies and applications in V2X;
  • Security and privacy threats in vehicular communication technologies;
  • Security considerations in V2X communications;
  • Security and privacy issues in C-V2X, NR-V2X, 802.11p, 802.11bd communications;
  • Security and privacy issues in vehicle-to-vehicle (V2V), vehicle-to-pedestrian (V2P), vehicle-to-infrastructure (V2I) communications;
  • Secure edge computing for V2X;
  • Intrusion detection and misbehavior detection in CAVs.

Dr. Vasileios Kouliaridis
Dr. Georgios Karopoulos
Dr. Jose Luis Hernandez-Ramos
Guest Editors

Manuscript Submission Information

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  • networks
  • smart cities
  • connected vehicles
  • wireless communications
  • network security
  • 5G
  • 802.11p
  • 3GPP
  • intelligent transport systems

Published Papers (1 paper)

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Voltage Based Electronic Control Unit (ECU) Identification with Convolutional Neural Networks and Walsh–Hadamard Transform
Electronics 2023, 12(1), 199; https://doi.org/10.3390/electronics12010199 - 31 Dec 2022
Viewed by 584
This paper proposes an identification approach for the Electronic Control Units (ECUs) in the vehicle, which are based on the physical characteristics of the ECUs extracted from their voltage output. Then, the identification is not based on cryptographic means, but it could be [...] Read more.
This paper proposes an identification approach for the Electronic Control Units (ECUs) in the vehicle, which are based on the physical characteristics of the ECUs extracted from their voltage output. Then, the identification is not based on cryptographic means, but it could be used as an alternative or complementary means to strengthen cryptographic solutions for vehicle cybersecurity. While previous research has used hand-crafted features such as mean voltage, max voltage, skew or variance, this study applies Convolutional Neural Networks (CNNs) in combination with the Walsh–Hadamard Transform (WHT), which has useful properties of compactness and robustness to noise. These properties are exploited by the CNN, and in particular, the pooling layers, to reduce the size of the feature maps in the CNN. The proposed approach is applied to a recently public data set of ECU voltage fingerprints extracted from different automotive vehicles. The results show that the combination of CNN and the WHT outperforms, in terms of identification accuracy, robustness to noise and computing times, and other approaches proposed in the literature based on shallow machine learning and tailor-made features, as well as CNN with other linear transforms such as the Discrete Fourier Transform (DFT) or CNN with the original time domain representations. Full article
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