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Electronics
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Security & Privacy in Intelligent Transportation Systems
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Security & Privacy in Intelligent Transportation Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 January 2022) | Viewed by 8376

Special Issue Editors

Dr. Thabet Kacem

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Guest Editor
Department of Computer Science and Information Technology, University of the District of Columbia, Washington, DC, USA
Interests: cyber security; intelligent transportation systems; critical infrastructure security; smart cities
Special Issues, Collections and Topics in MDPI journals
Dr. Cagatay Catal

E-Mail Website1 Website2
Guest Editor
Department of Computer Engineering, Bahcesehir University, 34349 Beşiktaş/Istanbul, Turkey
Interests: smart systems; cyber security; machine learning; deep learning; software architecture; software testing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mehmet Aksit

E-Mail Website
Guest Editor
Department of Computer Science, University of Twente, 7522 NB Enschede, The Netherlands
Interests: software engineering; software architecture; smart cities; aspect-oriented programming; digital ecosystems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Bedir Tekinerdogan

E-Mail Website
Guest Editor
Information Technology Group, Wageningen University, 6708 PB Wageningen, The Netherlands
Interests: software engineering; software architecture; systems engineering; smart systems; critical infrastructures; software ecosystems; system of systems
Special Issues, Collections and Topics in MDPI journals
Dr. Qingzhi Liu

E-Mail Website
Guest Editor
Information Technology Group, Wageningen University & Research, Wageningen, The Netherlands
Interests: internet of things; artificial intelligence IoT; intelligent edge computing

Special Issue Information

Dear Colleagues,

In recent years, intelligent transportation systems (ITS) have become very popular and are in high demand to advance transportation safety, mobility, and solve other transportation problems. By relying on novel technologies, such as cloud computing, 5G, IoT, and artificial intelligence, ITS has opened the door to endless applications and is considered one of the backbones of future smart cities.

Despite these attractive benefits and widespread applications of ITS, the security and privacy of such systems have not been studied in-depth and still suffer from several flaws that, if exploited, can result in severe attacks. This Special Issue will publish high-quality research, from both academic and industrial stakeholders, and serves as an outlet to disseminate solutions focusing on the security and privacy of intelligent transportation systems.

The proposed topics include but are not limited to the following:

  • Security and privacy in advanced traffic management systems
  • Security and privacy in advanced vehicle control systems
  • Security and privacy in connected vehicles
  • Security and privacy in V2V, V2I, and V2X
  • Security architectures for intelligent transportation systems
  • Security and privacy of data collection, transmission, and analysis in intelligent transportation systems
  • Security and privacy of 5G-based intelligent transportation systems
  • Lightweight cryptographic algorithms and protocols for intelligent transportation systems
  • AI and machine learning approaches to detect and classify cyber-attacks targeting intelligent transportation systems
  • Blockchain for secure intelligent transportation systems
  • Authentication and access management in intelligent transportation systems
  • Key management protocols for intelligent transportation systems
  • Threat models and attack strategies of intelligent transportation systems
  • Intrusion and malware detection for intelligent transportation systems

Dr. Thabet Kacem
Prof. Dr. Cagatay Catal
Prof. Dr. Mehmet Aksit
Prof. Dr. Bedir Tekinerdogan
Dr. Qingzhi Liu
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

  • Intelligent transportation systems
  • Security
  • Privacy
  • V2V, VEX, V2I
  • Intrusion and malware detection
  • Attack detection and classification
  • Authentication and access management
  • Blockchain

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Published Papers (2 papers)

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Research

22 pages, 9297 KiB  
Article
Privacy-Preserving Tampering Detection in Automotive Systems
by Adrian-Silviu Roman, Béla Genge, Adrian-Vasile Duka and Piroska Haller
Electronics 2021, 10(24), 3161; https://doi.org/10.3390/electronics10243161 - 18 Dec 2021
Cited by 10 | Viewed by 3196
Abstract
Modern auto-vehicles are built upon a vast collection of sensors that provide large amounts of data processed by dozens of Electronic Control Units (ECUs). These, in turn, monitor and control advanced technological systems providing a large palette of features to the vehicle’s end-users [...] Read more.
Modern auto-vehicles are built upon a vast collection of sensors that provide large amounts of data processed by dozens of Electronic Control Units (ECUs). These, in turn, monitor and control advanced technological systems providing a large palette of features to the vehicle’s end-users (e.g., automated parking, autonomous vehicles). As modern cars become more and more interconnected with external systems (e.g., cloud-based services), enforcing privacy on data originating from vehicle sensors is becoming a challenging research topic. In contrast, deliberate manipulations of vehicle components, known as tampering, require careful (and remote) monitoring of the vehicle via data transmissions and processing. In this context, this paper documents an efficient methodology for data privacy protection, which can be integrated into modern vehicles. The approach leverages the Fast Fourier Transform (FFT) as a core data transformation algorithm, accompanied by filters and additional transformations. The methodology is seconded by a Random Forest-based regression technique enriched with further statistical analysis for tampering detection in the case of anonymized data. Experimental results, conducted on a data set collected from the On-Board Diagnostics (OBD II) port of a 2015 EUR6 Skoda Rapid 1.2 L TSI passenger vehicle, demonstrate that the restored time-domain data preserves the characteristics required by additional processing algorithms (e.g., tampering detection), showing at the same time an adjustable level of privacy. Moreover, tampering detection is shown to be 100% effective in certain scenarios, even in the context of anonymized data. Full article
(This article belongs to the Special Issue Security & Privacy in Intelligent Transportation Systems)
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14 pages, 345 KiB  
Article
Malware Detection Based on Graph Attention Networks for Intelligent Transportation Systems
by Cagatay Catal, Hakan Gunduz and Alper Ozcan
Electronics 2021, 10(20), 2534; https://doi.org/10.3390/electronics10202534 - 18 Oct 2021
Cited by 15 | Viewed by 4032
Abstract
Intelligent Transportation Systems (ITS) aim to make transportation smarter, safer, reliable, and environmentally friendly without detrimentally affecting the service quality. ITS can face security issues due to their complex, dynamic, and non-linear properties. One of the most critical security problems is attacks that [...] Read more.
Intelligent Transportation Systems (ITS) aim to make transportation smarter, safer, reliable, and environmentally friendly without detrimentally affecting the service quality. ITS can face security issues due to their complex, dynamic, and non-linear properties. One of the most critical security problems is attacks that damage the infrastructure of the entire ITS. Attackers can inject malware code that triggers dangerous actions such as information theft and unwanted system moves. The main objective of this study is to improve the performance of malware detection models using Graph Attention Networks. To detect malware attacks addressing ITS, a Graph Attention Network (GAN)-based framework is proposed in this study. The inputs to this framework are the Application Programming Interface (API)-call graphs obtained from malware and benign Android apk files. During the graph creation, network metrics and the Node2Vec model are utilized to generate the node features. A GAN-based model is combined with different types of node features during the experiments and the performance is compared against Graph Convolutional Network (GCN). Experimental results demonstrated that the integration of the GAN and Node2Vec models provides the best performance in terms of F-measure and accuracy parameters and, also, the use of an attention mechanism in GAN improves the performance. Furthermore, node features generated with Node2Vec resulted in a 3% increase in classification accuracy compared to the features generated with network metrics. Full article
(This article belongs to the Special Issue Security & Privacy in Intelligent Transportation Systems)
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