Cybersecurity on the Internet of Vehicles

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 2091

Special Issue Editors


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Guest Editor
Industrial Systems Institute of ATHENA Research and Innovation Center, 265 04 Patra, Greece
Interests: cybersecurity; cyber physical systems; Internet of Things
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Electrical & Computer Engineering, University of Patras, 26504 Patras, Greece
Interests: architecture of embedded and cyber physical systems; cyber security; internet of things and industrial systems and networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, Internet-of-Vehicles has been growing exponentially, with vehicles being an active part of a large communication and connection community that also includes people (e.g., pedestrians), technological devices, and infrastructure. IoV, which is a core part of large, intelligent, and distributed transportation systems, is heavily based on the acquisition, exploitation, and sharing of information through Vehicle-to-everything (V2X) communication channels (e.g., V2V, V2M, V2P, V2D, V2I) leading to significant security and privacy challenges that may lead even to human safety risks. This Special Issue aims at presenting leading research that focuses on addressing and raising awareness of cybersecurity challenges (e.g., threat detection and attribution, privacy preservation, trust management) raised in the multidimensional environment of Internet-of-Vehicles, in an effort to highlight the latest developments in the field. We encourage the submission of research papers that present theoretical and/or experimental contributions, as well as visionary contributions that discuss research trends and future perspectives in the field. Papers should present original work that includes robust analysis or experimental validation of proposed models.

Topics of interest include (but are not limited to):

  • Cybersecurity frameworks of IoV
  • Response and recovery of cyberthreats in IoV
  • Privacy preservation in IoV
  • Trust and reputation in IoV
  • Secure routing in IoV
  • Security and privacy-preservation of V2X communications
  • Security in cloud-based IoV
  • Access control in IoV
  • Vulnerability scanning technologies for IoV
  • Intrusion detection technologies for IoV
  • Blockchain in IoV
  • Accountability in IoV
  • Thread Profiling and Information Sharing in IoV
  • Vehicle collision prediction and avoidance in IoV
  • Digital Forensics for IoV
  • Usable Security in IoV
  • Cyberthreat detection
  • Cyberthreat attribution
  • Visualization of cyberthreats
  • Real-time cybersecurity monitoring of IoV entities

Dr. George E. Raptis
Dr. Christos Alexakos
Prof. Dr. Dimitrios Serpanos
Guest Editors

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Keywords

  • cybersecurity
  • internet-of-vehicles (IoV)
  • security
  • privacy
  • safety
  • trust
  • connected automated vehicles (CAVs)
  • cyberthreat detection and attribution
  • cyberthreat prevention
  • response and recovery
  • digital forensics

Published Papers (1 paper)

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Research

20 pages, 1495 KiB  
Article
CAVeCTIR: Matching Cyber Threat Intelligence Reports on Connected and Autonomous Vehicles Using Machine Learning
by George E. Raptis, Christina Katsini, Christos Alexakos, Athanasios Kalogeras and Dimitrios Serpanos
Appl. Sci. 2022, 12(22), 11631; https://0-doi-org.brum.beds.ac.uk/10.3390/app122211631 - 16 Nov 2022
Cited by 2 | Viewed by 1671
Abstract
Connected and automated vehicles (CAVs) are getting a lot of attention these days as their technology becomes more mature and they benefit from the Internet-of-Vehicles (IoV) ecosystem. CAVs attract malicious activities that jeopardize security and safety dimensions. The cybersecurity systems of CAVs detect [...] Read more.
Connected and automated vehicles (CAVs) are getting a lot of attention these days as their technology becomes more mature and they benefit from the Internet-of-Vehicles (IoV) ecosystem. CAVs attract malicious activities that jeopardize security and safety dimensions. The cybersecurity systems of CAVs detect such activities, collect and analyze related information during and after the activity, and use cyber threat intelligence (CTI) to organize this information. Considering that CTI collected from various malicious activities may share common characteristics, it is critical to provide the cybersecurity stakeholders with quick and automatic ways of analysis and interrelation. This aims to help them perform more accurate and effective forensic investigations. To this end, we present CAVeCTIR, a novel approach that finds similarities between CTI reports that describe malicious activities detected on CAVs. CAVeCTIR uses advanced machine learning techniques and provides a quick, automated, and effective solution for clustering similar malicious activities. We applied CAVeCTIR in a series of experiments investigating almost 3000 malicious activities in simulation, real-world, and hybrid CAV environments, covering seven critical cyber-attack scenarios. The results showed that the DBSCAN algorithm identified seven no-overlapping core clusters characterized by high density. The results indicated that cybersecurity stakeholders could take advantage of CAVeCTIR by adopting the same or similar methods to analyze newly detected malicious activity, speed up the attack attribution process, and perform a more accurate forensics investigation. Full article
(This article belongs to the Special Issue Cybersecurity on the Internet of Vehicles)
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