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Internet of Vehicles and Autonomous Connected Vehicle: Privacy and Security
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Internet of Vehicles and Autonomous Connected Vehicle: Privacy and Security

A special issue of World Electric Vehicle Journal (ISSN 2032-6653).

Deadline for manuscript submissions: 30 April 2025 | Viewed by 6595

Special Issue Editor

Dr. Jiangtao Li

E-Mail Website
Guest Editor
1. School of Computer Engineering and Science, Shanghai University, Shanghai, China
2. Purple Mountain Laboratories, Nanjing, China
Interests: security and privacy of Internet of Vehicles; autonomous connected vehicles

Special Issue Information

Dear Colleagues,

The rapid advancement of technology in the field of the Internet of Vehicles (IoV) and autonomous connected vehicles (ACV) has brought about numerous benefits, including enhanced transportation efficiency, improved safety, and increased convenience. However, these advancements have also raised significant concerns regarding security and privacy issues, as they can pose life-threatening risks.

The Internet of Vehicles heavily relies on open wireless channels for message transmission, making it more vulnerable to network attacks compared to the traditional Internet. Common threats include counterfeiting, tampering, man-in-the-middle attacks, and Sybil attacks. Additionally, the presence of software and hardware vulnerabilities in vehicles creates potential entry points for cybercriminals, introducing risks such as extortion, malicious manipulation of vehicles, and data leakage.

This special issue aims to explore the latest advancements and challenges in the area of privacy and security for the Internet of Vehicles and Autonomous Connected Vehicles. We invite researchers and practitioners from academia and industry to contribute original research articles, case studies, and review articles addressing various aspects of privacy and security in the IoV and ACV domains.

Dr. Jiangtao Li
Guest Editor

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. World Electric Vehicle Journal is an international peer-reviewed open access monthly 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 1400 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

  • Internet of Vehicles (IoV)
  • security
  • privacy
  • IoV attacks
  • authentication
  • vulnerability

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Published Papers (3 papers)

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Research

25 pages, 970 KiB  
Article
Fuzzy Logic-Based Autonomous Lane Changing Strategy for Intelligent Internet of Vehicles: A Trajectory Planning Approach
by Chao He, Wenhui Jiang, Junting Li, Jian Wei, Jiang Guo and Qiankun Zhang
World Electr. Veh. J. 2024, 15(9), 403; https://doi.org/10.3390/wevj15090403 - 3 Sep 2024
Viewed by 1429
Abstract
The autonomous lane change maneuver is a critical component in the advancement of intelligent transportation systems (ITS). To enhance safety and efficiency in dynamic traffic environments, this study introduces a novel autonomous lane change strategy leveraging a quintic polynomial function. To optimize the [...] Read more.
The autonomous lane change maneuver is a critical component in the advancement of intelligent transportation systems (ITS). To enhance safety and efficiency in dynamic traffic environments, this study introduces a novel autonomous lane change strategy leveraging a quintic polynomial function. To optimize the trajectory, we formulate an objective function that balances the time required for lane changes with the peak acceleration experienced during the maneuver. The proposed method addresses key challenges such as driver discomfort and prolonged lane change durations by considering the entire lane change process rather than just the initiation point. Utilizing a fifth-order polynomial for trajectory planning, the strategy ensures smooth and continuous vehicle movement, reducing the risk of collisions. The effectiveness of the method is validated through comprehensive simulations and real-world vehicle tests, demonstrating significant improvements in lane change performance. Despite its advantages, the model requires further refinement to address limitations in mixed traffic conditions. This research provides a foundation for developing intelligent vehicle systems that prioritize safety and adaptability. Full article
(This article belongs to the Special Issue Internet of Vehicles and Autonomous Connected Vehicle: Privacy and Security)
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21 pages, 5779 KiB  
Article
An Intelligent Attack Detection Framework for the Internet of Autonomous Vehicles with Imbalanced Car Hacking Data
by Samah Alshathri, Amged Sayed and Ezz El-Din Hemdan
World Electr. Veh. J. 2024, 15(8), 356; https://doi.org/10.3390/wevj15080356 - 8 Aug 2024
Cited by 1 | Viewed by 1789
Abstract
The modern Internet of Autonomous Vehicles (IoVs) has enabled the development of autonomous vehicles that can interact with each other and their surroundings, facilitating real-time data exchange and communication between vehicles, infrastructure, and the external environment. The lack of security procedures in vehicular [...] Read more.
The modern Internet of Autonomous Vehicles (IoVs) has enabled the development of autonomous vehicles that can interact with each other and their surroundings, facilitating real-time data exchange and communication between vehicles, infrastructure, and the external environment. The lack of security procedures in vehicular networks and Controller Area Network (CAN) protocol leaves vehicles exposed to intrusions. One common attack type is the message injection attack, which inserts fake messages into original Electronic Control Units (ECUs) to trick them or create failures. Therefore, this paper tackles the pressing issue of cyber-attack detection in modern IoV systems, where the increasing connectivity of vehicles to the external world and each other creates a vast attack surface. The vulnerability of in-vehicle networks, particularly the CAN protocol, makes them susceptible to attacks such as message injection, which can have severe consequences. To address this, we propose an intelligent Intrusion detection system (IDS) to detect a wide range of threats utilizing machine learning techniques. However, a significant challenge lies in the inherent imbalance of car-hacking datasets, which can lead to misclassification of attack types. To overcome this, we employ various imbalanced pre-processing techniques, including NearMiss, Random over-sampling (ROS), and TomLinks, to pre-process and handle imbalanced data. Then, various Machine Learning (ML) techniques, including Logistic Regression (LR), Linear Discriminant Analysis (LDA), Naive Bayes (NB), and K-Nearest Neighbors (k-NN), are employed in detecting and predicting attack types on balanced data. We evaluate the performance and efficacy of these techniques using a comprehensive set of evaluation metrics, including accuracy, precision, F1_Score, and recall. This demonstrates how well the suggested IDS detects cyberattacks in external and intra-vehicle vehicular networks using unbalanced data on vehicle hacking. Using k-NN with various resampling techniques, the results show that the proposed system achieves 100% detection rates in testing on the Car-Hacking dataset in comparison with existing work, demonstrating the effectiveness of our approach in protecting modern vehicle systems from advanced threats. Full article
(This article belongs to the Special Issue Internet of Vehicles and Autonomous Connected Vehicle: Privacy and Security)
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12 pages, 340 KiB  
Article
A Blockchain-Based Data Authentication Algorithm for Secure Information Sharing in Internet of Vehicles
by Amjad Aldweesh
World Electr. Veh. J. 2023, 14(8), 223; https://doi.org/10.3390/wevj14080223 - 15 Aug 2023
Cited by 4 | Viewed by 2536
Abstract
Secure communication between connected electric vehicles is critical for realizing the full potential of the Internet of Vehicles. However, the authentication and security of the information shared between vehicles remains a major challenge. In this work, we propose a blockchain-based data authentication algorithm [...] Read more.
Secure communication between connected electric vehicles is critical for realizing the full potential of the Internet of Vehicles. However, the authentication and security of the information shared between vehicles remains a major challenge. In this work, we propose a blockchain-based data authentication algorithm to enable secure information sharing between electric vehicles. Our algorithm leverages the distributed ledger and consensus mechanism of blockchain technology to overcome limitations of traditional public key infrastructure schemes for large-scale vehicle networks. Each electric vehicle has a unique key pair and address on the blockchain network. Vehicles generate digital signatures using their private keys to share data, while recipients verify the signatures using corresponding public keys for authentication. Experimental results demonstrate that the proposed algorithm achieves high authentication success rates with acceptable latency and computation overhead. The algorithm provides benefits like decentralization, transparency and non-repudiation compared to existing approaches. Our work indicates the potential of blockchain to enhance security, trust and cooperation in Internet of Vehicles applications. Full article
(This article belongs to the Special Issue Internet of Vehicles and Autonomous Connected Vehicle: Privacy and Security)
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