Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.6
Journals
Electronics
Special Issues
Secure Integration of Artificial Intelligence (AI) and Autonomous Vehicular Networks
share announcement

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 February 2025 | Viewed by 5980

Special Issue Editors

Prof. 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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

23 pages, 7686 KiB  
Article
Efficient Secure Mechanisms for In-Vehicle Ethernet in Autonomous Vehicles
by Yujing Wu, Liping Xiong, Caiyuan Wang and Yinan Xu
Electronics 2024, 13(18), 3625; https://doi.org/10.3390/electronics13183625 - 12 Sep 2024
Viewed by 645
Abstract
The integration of external devices and network connectivity into autonomous vehicles has raised significant concerns about in-vehicle security vulnerabilities. Existing security mechanisms for in-vehicle bus systems, which mainly rely on appending authentication codes and data encryption, have been extensively studied in the context [...] Read more.
The integration of external devices and network connectivity into autonomous vehicles has raised significant concerns about in-vehicle security vulnerabilities. Existing security mechanisms for in-vehicle bus systems, which mainly rely on appending authentication codes and data encryption, have been extensively studied in the context of CAN and CAN-FD buses. However, these approaches are not directly applicable to Ethernet buses due to the much higher data transmission rates of Ethernet buses compared to other buses. The real-time encryption and decryption required by Ethernet buses cannot be achieved with conventional methods, necessitating an acceleration in the speed of cryptographic operations to match the demands of Ethernet communication. In response to these challenges, our paper introduces a range of cryptographic solutions specifically designed for in-vehicle Ethernet networks. We employ an AES-ECC hybrid algorithm for critical vehicle control signals, combining the efficiency of AES with the security of ECC. For multimedia signals, we propose an improved AES-128 (IAES-128) and an improved MD5 (IMD), which improve encryption time by 15.77%. Our proposed security mechanisms have been rigorously tested through attack simulations on the CANoe (version 10) platform. These tests cover both in-vehicle control signals, such as braking and throttle control, and non-critical systems like multimedia entertainment. The experimental results convincingly demonstrate that our optimized algorithms and security mechanisms ensure the secure and reliable operation of real-time communication in autonomous vehicles. Full article
(This article belongs to the Special Issue Secure Integration of Artificial Intelligence (AI) and Autonomous Vehicular Networks)
►▼ Show Figures

Figure 1

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
Cited by 5 | Viewed by 1361
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)
►▼ Show Figures

Figure 1

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
Cited by 2 | Viewed by 1771
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)
►▼ Show Figures

Figure 1

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 1144
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop