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Electronics
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Secure Integration of Artificial Intelligence (AI) and Autonomous Vehicular Networks
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Secure Integration of Artificial Intelligence (AI) and Autonomous Vehicular Networks

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

Deadline for manuscript submissions: 15 August 2024 | Viewed by 3054

Special Issue Editors

Dr. Zhiquan Liu

E-Mail Website
Guest Editor
College of Cyber Security, Jinan University, Guangzhou 510632, China
Interests: telematics security; drone security; web security; trust and privacy; artificial intelligence; blockchain
Special Issues, Collections and Topics in MDPI journals
Dr. Zuobin Ying

E-Mail Website
Guest Editor
Faculty of Data Science, City University of Macau, Macau 999078, China
Interests: blockchain; federal learning; attribute encryption
Special Issues, Collections and Topics in MDPI journals
Dr. Jingjing Guo

E-Mail Website
Guest Editor
School of Cyber Engineering, Xidian University, Xi'an 710071, China
Interests: UAV cybersecurity; artificial intelligence system security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the advancement of sensing, communication, and networking, autonomous vehicular networks are expected to play a vital role in a variety of areas, including industry 4.0, smart logistics, smart transportation, and public safety. The application of artificial intelligence (AI) technologies can provide significant benefits for automating sensing, computing, and communication tasks in autonomous vehicular networks.

However, in order to realize real-time perception and autonomous control, AI-enabled autonomous vehicular networks will need to be more complex and heterogeneous than before. AI-enabled autonomous vehicular networks will be extremely challenging in terms of security and privacy due to complex features such as high mobility of nodes, unreliable link connections, vulnerable terminal equipments, limited resources, and heterogeneous topologies. For example, distributed AI models are very important in autonomous vehicular networks with multiple self-organizing vehicles. However, malicious attacks on AI models trained on edge devices are still an important problem to be solved in AI-enabled autonomous vehicular networks.

This Special Issue specifically focuses on the latest advances, challenges, and approaches to the secure integration of AI and autonomous vehicular networks. We encourage original and high-quality contributions that address both the theoretical and practical aspects of the above challenges. Topics of interest include, but are not limited to:

  • Deep learning and reinforcement learning for autonomous vehicular networks;
  • Edge learning and distributed machine learning for autonomous vehicular networks;
  • Privacy-preserving federated learning for AI-enabled autonomous vehicular networks;
  • New network architecture for AI-enabled autonomous vehicular networks;
  • Sensing data falsification and countermeasures for AI-enabled autonomous vehicular networks;
  • Cyber physical system security for AI-enabled autonomous vehicular networks;
  • Intrusion detection and incident response for AI-enabled autonomous vehicular networks;
  • Data security and privacy preservation for AI-enabled autonomous vehicular networks;
  • Risk assessment and reputation management for AI-enabled autonomous vehicular networks;
  • Distributed data fusion for AI-enabled autonomous vehicular networks.

Dr. Zhiquan Liu
Dr. Zuobin Ying
Dr. Jingjing Guo
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. Electronics 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 2400 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

  • autonomous vehicular networks
  • artificial intelligence
  • machine learning
  • security
  • privacy
  • risk assessment
  • data fusion

Published Papers (3 papers)

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Research

22 pages, 4172 KiB  
Article
BOppCL: Blockchain-Enabled Opportunistic Federated Learning Applied in Intelligent Transportation Systems
by Qiong Li, Wennan Wang, Yizhao Zhu and Zuobin Ying
Electronics 2024, 13(1), 136; https://doi.org/10.3390/electronics13010136 - 28 Dec 2023
Viewed by 745
Abstract
In this paper, we present a novel blockchain-enabled approach to opportunistic federated learning (OppCL) for intelligent transportation systems (ITS). Our approach integrates blockchain with OppCL to streamline the learning of autonomous vehicle models while addressing data privacy and trust challenges. We deploy resilient [...] Read more.
In this paper, we present a novel blockchain-enabled approach to opportunistic federated learning (OppCL) for intelligent transportation systems (ITS). Our approach integrates blockchain with OppCL to streamline the learning of autonomous vehicle models while addressing data privacy and trust challenges. We deploy resilient countermeasures, incentivized mechanisms, and a secure gradient distribution to combat single-point failure verification attacks. Additionally, we integrate the Byzantine fault-tolerant algorithm (BFT) into the node verification component of the delegated proof of stake (DPoS) to minimize verification delays. We validate our approach through experiments on the MNIST, SVHN, and CIFAR-10 datasets, showing convergence rates and prediction accuracy comparable to traditional OppCL approaches. Full article
(This article belongs to the Special Issue Secure Integration of Artificial Intelligence (AI) and Autonomous Vehicular Networks)
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20 pages, 3000 KiB  
Article
Attribute and User Trust Score-Based Zero Trust Access Control Model in IoV
by Jiuru Wang, Zhiyuan Wang, Jingcheng Song and Hongyuan Cheng
Electronics 2023, 12(23), 4825; https://doi.org/10.3390/electronics12234825 - 29 Nov 2023
Viewed by 937
Abstract
The Internet of Vehicles (IoV) is an innovative area of interest in modern mobility that is rapidly evolving while facing complex challenges. Traditional IoV networks are susceptible to intrusion threats, which can lead to data leakage and seizure of vehicle control by attackers, [...] Read more.
The Internet of Vehicles (IoV) is an innovative area of interest in modern mobility that is rapidly evolving while facing complex challenges. Traditional IoV networks are susceptible to intrusion threats, which can lead to data leakage and seizure of vehicle control by attackers, thereby endangering vehicle users’ privacy and personal safety. An Attribute and User Trust Score-based Zero Trust Access Control Model (AU-ZTAC) is proposed, combining the zero-trust and attribute-based access control models to meet network protection requirements while achieving fine-grained dynamic access control and incorporating trust evaluation in the access control process to better reflect users’ intent. Experimental results demonstrate the effectiveness and feasibility of trust assessment through the proposed model. A comparison with the classical schemes illustrates that AU-ZTAC allows for more flexible and fine-grained access control in complex access control environments while improving IoV security. Full article
(This article belongs to the Special Issue Secure Integration of Artificial Intelligence (AI) and Autonomous Vehicular Networks)
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21 pages, 4050 KiB  
Article
IoV Vulnerability Classification Algorithm Based on Knowledge Graph
by Jiuru Wang, Yifang Wang, Jingcheng Song and Hongyuan Cheng
Electronics 2023, 12(23), 4749; https://doi.org/10.3390/electronics12234749 - 23 Nov 2023
Cited by 1 | Viewed by 734
Abstract
With the rapid development of smart technologies, the Internet of Vehicles (IoV) is revolutionizing transportation and mobility. However, the complexity and interconnectedness of IoV systems lead to a growing number of security incidents caused by vulnerabilities. Current vulnerability classification algorithms often struggle to [...] Read more.
With the rapid development of smart technologies, the Internet of Vehicles (IoV) is revolutionizing transportation and mobility. However, the complexity and interconnectedness of IoV systems lead to a growing number of security incidents caused by vulnerabilities. Current vulnerability classification algorithms often struggle to address the low occurrence frequency and incomplete information associated with IoV vulnerabilities, resulting in decreased precision and recall rates of classifiers. To address these challenges, an effective vulnerability classification algorithm (KG-KNN), is proposed, designed to handle imbalanced sample data. KG-KNN integrates the vulnerability information of IoV and the association relationship between features by constructing a feature knowledge graph to form a complete knowledge system. It adds the correlation relationship between features to the similarity calculation, calculates vulnerability similarity from multiple dimensions, and improves the prediction performance of the classifier. The experimental results show that compared to the k-NearestNeighbor (KNN), Support Vector Machine (SVM), Deep Nueral Network (DNN) and TFI-DNN classification algorithms, KG-KNN can effectively deal with imbalanced sample data and has different degrees of improvement in precision, recall, and the F1 score. Full article
(This article belongs to the Special Issue Secure Integration of Artificial Intelligence (AI) and Autonomous Vehicular Networks)
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