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Electronics
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Automotive Cyber Security
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Automotive Cyber Security

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

Deadline for manuscript submissions: closed (15 June 2024) | Viewed by 2508

Special Issue Editors

Dr. Ioanna Kantzavelou

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Guest Editor
Department of Informatics & Computer Engineering, University of West Attica, Egaleo, 122 43 Athens, Greece
Interests: IT security; cybersecurity; intrusion detection in information and communication systems; attacker profiling; attack modeling; game theory
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Leandros Maglaras

E-Mail Website
Guest Editor
School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK
Interests: network security; information security; privacy preservation; risk management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Grammati Pantziou

E-Mail Website
Guest Editor
Department of Informatics & Computer Engineering, University of West Attica, 12241 Athens, Greece
Interests: mobile ad-hoc and wireless sensor networks; distributed computing; Internet of Things applications; security and privacy in pervasive environments
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Technology eruption is rapidly changing the state of the art in automated vehicular navigation systems, significantly affecting the customers’ environment. Connected vehicles increase and generate new demands of OEMs, markets, and governments, many associated with the expanding attack landscape. Hybrid threats, advanced persistent threats (APTs), and skillful hackers threaten large enterprises or governmental networks that focus on new opportunities created by the automotive industry. Common incidents and frequent car recalls by OEMs reveal the extent of the upcoming damage worldwide.

These recent advances are welcome but make Automotive Cybersecurity challenges essential, and they should be addressed before catastrophic irreversible consequences occur. Connectivity is opening up a new era in automotive technology, though in parallel, it multiplies and expands attack vectors and opportunities for criminal deception, with potential consequences for safety hazards and loss of life. Appropriate security measures provided by next-generation security regulations should be adopted by countries to establish an automotive cybersecurity framework. The collaboration of automotive security professionals will support the complete development of secure public intelligent transportation systems within this framework.

Topics of Interest

This Special Issue deals with recent advances in Automotive Cybersecurity and the critical challenges introduced above, in both research and technical aspects. Closely aligned with the interdisciplinary nature of automotive cybersecurity, authors from academia, industry, and government are welcome to contribute.

We seek original and high-quality submissions on, but not limited to, one or more of the following topics:

  • Attacks on EV Charging Infrastructures
  •  Prevention and Management of Fleet-wide Attacks
  • New Attack Vectors
  • Built-in Threat Detection
  • Data Privacy
  • Regulations in Smart Mobility Ecosystem
  • Intrusion Detection
  • Cryptographic Algorithms
  • Automotive Secure Software
  • Secure Onboard Communication
  • Cloud Computing Security
  • Cybersecurity for Heavy Vehicles
  • Vehicle Security Operation Centers (VSOCs)
  • Vehicle Electronic Architecture Security
  • Game Theoretic Approaches to Detect Intrusions

Dr. Ioanna Kantzavelou
Prof. Dr. Leandros Maglaras
Prof. Dr. Grammati Pantziou
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

  • EV charging
  • vehicle
  • automotive cybersecurity
  • cloud computing security

Published Papers (3 papers)

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27 pages, 4029 KiB  
Article
Denial of Service Attack Prevention and Mitigation for Secure Access in IoT GPS-based Intelligent Transportation Systems
by Gheorghe Romeo Andreica, George Lucian Tabacar, Daniel Zinca, Iustin Alexandru Ivanciu and Virgil Dobrota
Electronics 2024, 13(14), 2693; https://doi.org/10.3390/electronics13142693 - 10 Jul 2024
Viewed by 402
Abstract
The widespread use of GPS tracking devices has made them an indispensable solution in various sectors such as transportation, logistics, and security. However, the complexity of cyber attacks such as denial of service (DoS) attacks have made these devices vulnerable, thereby compromising the [...] Read more.
The widespread use of GPS tracking devices has made them an indispensable solution in various sectors such as transportation, logistics, and security. However, the complexity of cyber attacks such as denial of service (DoS) attacks have made these devices vulnerable, thereby compromising the security of the data and devices. This has a significant impact on business and applicable legislation related to essential services. In this paper, we propose the integration of security mechanisms and algorithms into the Teltonika IoT GPS tracking device’s firmware, including DoS protection to detect, prevent, and secure these devices against DoS cyber attacks. Full article
(This article belongs to the Special Issue Automotive Cyber Security)
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22 pages, 819 KiB  
Article
A Novel Dataset and Approach for Adversarial Attack Detection in Connected and Automated Vehicles
by Tae Hoon Kim, Moez Krichen, Meznah A. Alamro and Gabreil Avelino Sampedro
Electronics 2024, 13(12), 2420; https://doi.org/10.3390/electronics13122420 - 20 Jun 2024
Viewed by 407
Abstract
Adversarial attacks have received much attention as communication network applications rise in popularity. Connected and Automated Vehicles (CAVs) must be protected against adversarial attacks to ensure passenger and vehicle safety on the road. Nevertheless, CAVs are susceptible to several types of attacks, such [...] Read more.
Adversarial attacks have received much attention as communication network applications rise in popularity. Connected and Automated Vehicles (CAVs) must be protected against adversarial attacks to ensure passenger and vehicle safety on the road. Nevertheless, CAVs are susceptible to several types of attacks, such as those that target intra- and inter-vehicle networks. These harmful attacks not only cause user privacy and confidentiality to be lost, but they also have more grave repercussions, such as physical harm and death. It is critical to precisely and quickly identify adversarial attacks to protect CAVs. This research proposes (1) a new dataset comprising three adversarial attacks in the CAV network traffic and normal traffic, (2) a two-phased adversarial attack detection technique named TAAD-CAV, where in the first phase, an ensemble voting classifier having three machine learning classifiers and one separate deep learning classifier is trained, and the output is used in the next phase. In the second phase, a meta classifier (i.e., Decision Tree is used as a meta classifier) is trained on the combined predictions from the previous phase to detect adversarial attacks. We preprocess the dataset by cleaning data, removing missing values, and adjusting the Z-score normalization. Evaluation metrics such as accuracy, recall, precision, F1-score, and confusion matrix are employed to evaluate and compare the performance of the proposed model. Results reveal that TAAD-CAV achieves the highest accuracy with a value of 70% compared with individual ML and DL classifiers. Full article
(This article belongs to the Special Issue Automotive Cyber Security)
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Other

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10 pages, 702 KiB  
Brief Report
Intrusion Detection in Intelligent Connected Vehicles Based on Weighted Self-Information
by Tianqi Yu, Jianling Hu and Jianfeng Yang
Electronics 2023, 12(11), 2510; https://doi.org/10.3390/electronics12112510 - 2 Jun 2023
Cited by 2 | Viewed by 1069
Abstract
The Internet of Vehicles (IoV) empowers intelligent and tailored services for intelligent connected vehicles (ICVs). However, with the increasing number of onboard external communication interfaces, ICVs face the challenges of malicious network intrusions. The closure of traditional vehicles had led to in-vehicle communication [...] Read more.
The Internet of Vehicles (IoV) empowers intelligent and tailored services for intelligent connected vehicles (ICVs). However, with the increasing number of onboard external communication interfaces, ICVs face the challenges of malicious network intrusions. The closure of traditional vehicles had led to in-vehicle communication protocols, including the most commonly applied controller area network (CAN), and a lack of security and privacy protection mechanisms. Therefore, to protect the connected vehicles and IoV systems from being attacked, an intrusion-detection method is proposed based on the features extracted from the arbitration identifier (ID) field of CAN messages. Specifically, a sliding window is used to continuously extract a frame of streaming CAN messages first. Afterward, the weighted self-information of the CAN message ID is defined, and both the weighted self-information and the normalized value of an ID are extracted as features. Based on the extracted features, a lightweight one-class classifier, the local outlier factor (LOF), is used to identify the outliers and detect malicious network intrusion attacks. Simulation experiments were conducted based on the public CAN dataset provided by the HCR Lab. The proposed method, using four different one-class classifiers, was analyzed, and it is also benchmarked with three information entropy-based intrusion-detection methods in the literature. The experimental results indicate that, compared to the benchmarks, the proposed method dramatically improves the detection accuracy for injection attacks, namely denial-of-service (DoS) and spoofing, especially when the number of injected messages is low. Full article
(This article belongs to the Special Issue Automotive Cyber Security)
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