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Security, Privacy and Trust in Connected and Automated Vehicles
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Security, Privacy and Trust in Connected and Automated Vehicles

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Vehicular Sensing".

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 6588

Special Issue Editors

Dr. Ioannis Krontiris

E-Mail Website
Guest Editor
Huawei Technologies Duesseldorf GmbH, 80992 Munich, Germany
Interests: security in connected smart objects
Dr. Thanassis Giannetsos

E-Mail Website
Guest Editor
Ubitech Ltd, Digital Security & Trusted Computing Group, Thessalias 8 & Etolias 10, 15232 Chalandri, Athesn, Greece
Interests: trusted computing; applied cryptography; information security; privacy; Internet of Things; secure systems; intrusion detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent technologies of wireless cooperative vehicular networks supporting Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications are expected to greatly enhance autonomous driving through perception sharing, path planning, real-time local updates, and coordinated driving. These features facilitate the next generation of Intelligent Transport Systems solutions for cooperative autonomous driving applications. Moreover, with the future adoption of 5G and beyond, connected and autonomous vehicles are likely to generate and exchange a huge volume of data, provided that they are able to guarantee the required quality of this data for functional safety. At the same time, Multi-access Edge Computing (MEC) brings processing power near the vehicle to meet ultra-low-latency requirements. So overall, the new services will be operated in a Multi-MNO, Multi-OEM and multi-vendor environments, which makes it imperative to study not only security and privacy challenges, but also the establishment of trust to data and entities.

This Special Issue aims to cover the most recent advances in security, privacy and trustworthiness in next generation cooperative vehicular networks. The Special Issue has an interest in presenting insights into the threats and attacks facing such systems, alongside new advances in the safeguards that can be used to protect against them. Within this, we are also keen to receive submissions that span the breadth of the domain, encompassing technical, organizational, and human perspectives on the topic. Authors are invited to contact the guest editor—prior to submission—if they are uncertain whether their work falls within the Special Issue’s general scope.

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

  • Trust Management and computing mechanisms to assess and enhance trust in the interactions between vehicles and between vehicles and infrastructure
  • Security, privacy and trust issues in all layers of 5G V2X infrustructures (MEC, backend, vehicle)
  • Security and privacy in Intelligent Transportation Systems, e.g., vehicle platooning
  • Identity management approaches for V2X, based on VPKI, Verifiable Credentials, etc.
  • Authentication and misbehavior detection
  • Security threats and attacks facing connected and cooperative vehicular networks
  • Authenticity and integrity of hardware and software for connected vehicles
  • Privacy-preserving data sharing and analysis in automotive settings
  • Location privacy approaches to cooperative vehicular networks
  • Cyber security management system, incl. regulation, standardization, certification, interoperability, connection to other management systems
  • Cryptographic tools and protocols including post-quantum cryptography

Dr. Ioannis Krontiris
Dr. Thanassis Giannetsos
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • V2X communication
  • cooperative, connected and automated mobility
  • security
  • privacy and privacy-enhancing technologies
  • Trust and Trusted Computing
 

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Published Papers (3 papers)

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Research

11 pages, 30719 KiB  
Article
Performance Evaluation of Carrier-Frequency Offset as a Radiometric Fingerprint in Time-Varying Channels
by Abdulsahib Albehadili and Ahmad Y. Javaid
Sensors 2024, 24(17), 5670; https://doi.org/10.3390/s24175670 - 31 Aug 2024
Viewed by 653
Abstract
The authentication of wireless devices through physical layer attributes has attracted a fair amount of attention recently. Recent work in this area has examined various features extracted from the wireless signal to either identify a uniqueness in the channel between the transmitter–receiver pair [...] Read more.
The authentication of wireless devices through physical layer attributes has attracted a fair amount of attention recently. Recent work in this area has examined various features extracted from the wireless signal to either identify a uniqueness in the channel between the transmitter–receiver pair or more robustly identify certain transmitter behaviors unique to certain devices originating from imperfect hardware manufacturing processes. In particular, the carrier frequency offset (CFO), induced due to the local oscillator mismatch between the transmitter and receiver pair, has exhibited good detection capabilities in stationary and low-mobility transmission scenarios. It is still unclear, however, how the CFO detection capability would hold up in more dynamic time-varying channels where there is a higher mobility. This paper experimentally demonstrates the identification accuracy of CFO for wireless devices in time-varying channels. To this end, a software-defined radio (SDR) testbed is deployed to collect CFO values in real environments, where real transmission and reception are conducted in a vehicular setup. The collected CFO values are used to train machine-learning (ML) classifiers to be used for device identification. While CFO exhibits good detection performance (97% accuracy) for low-mobility scenarios, it is found that higher mobility (35 miles/h) degrades (72% accuracy) the effectiveness of CFO in distinguishing between legitimate and non-legitimate transmitters. This is due to the impact of the time-varying channel on the quality of the exchanged pilot signals used for CFO detection at the receivers. Full article
(This article belongs to the Special Issue Security, Privacy and Trust in Connected and Automated Vehicles)
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25 pages, 4418 KiB  
Article
A Framework for Cybersecurity Requirements Management in the Automotive Domain
by Feng Luo, Yifan Jiang, Jiajia Wang, Zhihao Li and Xiaoxian Zhang
Sensors 2023, 23(10), 4979; https://doi.org/10.3390/s23104979 - 22 May 2023
Cited by 1 | Viewed by 2428
Abstract
The rapid development of intelligent connected vehicles has increased the attack surface of vehicles and made the complexity of vehicle systems unprecedented. Original equipment manufacturers (OEMs) need to accurately represent and identify threats and match corresponding security requirements. Meanwhile, the fast iteration cycle [...] Read more.
The rapid development of intelligent connected vehicles has increased the attack surface of vehicles and made the complexity of vehicle systems unprecedented. Original equipment manufacturers (OEMs) need to accurately represent and identify threats and match corresponding security requirements. Meanwhile, the fast iteration cycle of modern vehicles requires development engineers to quickly obtain cybersecurity requirements for new features in their developed systems in order to develop system code that meets cybersecurity requirements. However, existing threat identification and cybersecurity requirement methods in the automotive domain cannot accurately describe and identify threats for a new feature while also quickly matching appropriate cybersecurity requirements. This article proposes a cybersecurity requirements management system (CRMS) framework to assist OEM security experts in conducting comprehensive automated threat analysis and risk assessment and to help development engineers identify security requirements prior to software development. The proposed CRMS framework enables development engineers to quickly model their systems using the UML-based (i.e., capable of describing systems using UML) Eclipse Modeling Framework and security experts to integrate their security experience into a threat library and security requirement library expressed in Alloy formal language. In order to ensure accurate matching between the two, a middleware communication framework called the component channel messaging and interface (CCMI) framework, specifically designed for the automotive domain, is proposed. The CCMI communication framework enables the fast model of development engineers to match with the formal model of security experts for threat and security requirement matching, achieving accurate and automated threat and risk identification and security requirement matching. To validate our work, we conducted experiments on the proposed framework and compared the results with the HEAVENS approach. The results showed that the proposed framework is superior in terms of threat detection rates and coverage rates of security requirements. Moreover, it also saves analysis time for large and complex systems, and the cost-saving effect becomes more pronounced with increasing system complexity. Full article
(This article belongs to the Special Issue Security, Privacy and Trust in Connected and Automated Vehicles)
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33 pages, 2686 KiB  
Article
XACML for Mobility (XACML4M)—An Access Control Framework for Connected Vehicles
by Ashish Ashutosh, Armin Gerl, Simon Wagner, Lionel Brunie and Harald Kosch
Sensors 2023, 23(4), 1763; https://doi.org/10.3390/s23041763 - 4 Feb 2023
Cited by 5 | Viewed by 2418
Abstract
The automotive industry is experiencing a transformation with the rapid integration of software-based systems inside vehicles, which are complex systems with multiple sensors. The use of vehicle sensor data has enabled vehicles to communicate with other entities in the connected vehicle ecosystem, such [...] Read more.
The automotive industry is experiencing a transformation with the rapid integration of software-based systems inside vehicles, which are complex systems with multiple sensors. The use of vehicle sensor data has enabled vehicles to communicate with other entities in the connected vehicle ecosystem, such as the cloud, road infrastructure, other vehicles, pedestrians, and smart grids, using either cellular or wireless networks. This vehicle data are distributed, private, and vulnerable, which can compromise the safety and security of vehicles and their passengers. It is therefore necessary to design an access control mechanism around the vehicle data’s unique attributes and distributed nature. Since connected vehicles operate in a highly dynamic environment, it is important to consider context information such as location, time, and frequency when designing a fine-grained access control mechanism. This leads to our research question: How can Attribute-Based Access Control (ABAC) fulfill connected vehicle requirements of Signal Access Control (SAC), Time-Based Access Control (TBAC), Location-Based Access Control (LBAC), and Frequency-Based Access Control (FBAC)? To address the issue, we propose a data flow model based on Attribute-Based Access Control (ABAC) called eXtensible Access Control Markup Language for Mobility (XACML4M). XACML4M adds additional components to the standard eXtensible Access Control Markup Language (XACML) to satisfy the identified requirements of SAC, TBAC, LBAC, and FBAC in connected vehicles. Specifically, these are: Vehicle Data Environment (VDE) integrated with Policy Enforcement Point (PEP), Time Extensions, GeoLocation Provider, Polling Frequency Provider, and Access Log Service. We implement a prototype based on these four requirements on a Raspberry Pi 4 and present a proof-of-concept for a real-world use case. We then perform a functional evaluation based on the authorization policies to validate the XACML4M data flow model. Finally, we conclude that our proposed XACML4M data flow model can fulfill all four of our identified requirements for connected vehicles. Full article
(This article belongs to the Special Issue Security, Privacy and Trust in Connected and Automated Vehicles)
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