Proceedings Article10.23919/DATE54114.2022.9774594
A Comprehensive Solution for Securing Connected and Autonomous Vehicles
Mohsin Kamal,Christos Kyrkou,Nikos Piperigkos,Andreas Papandreou,Andreas Kloukiniotis,Jordi Casademont,Natlia Porras Mateu,Daniel Baos Castillo,Rodrigo Diaz Rodriguez,Nicola Gregorio Durante,Peter Hofmann,P. Kapsalas,Aris S. Lalos,Konstantinos Moustakas,Christos Laoudias,Theocharis Theocharides,Georgios Ellinas +16 more
- 14 Mar 2022
pp 790-795
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TL;DR: A technical overview of the H2020 CARAMEL project is given in which Artificial Intelligent (AI)-based cybersecurity for CAVs is the main goal, and the counter-measures to these attacks and vulnerabilities are presented via the current results in the CARAMel project achieved by implementing the designed security algorithms.
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Abstract: With the advent of Connected and Autonomous Vehicles (CAVs) comes the very real risk that these vehicles will be exposed to cyber-attacks by exploiting various vulnerabilities. This paper gives a technical overview of the H2020 CARAMEL project (currently in the intermediate stage) in which Artificial Intelligent (AI)-based cybersecurity for CAVs is the main goal. Most of the possible scenarios are considered, by which an adversary can generate attacks on CAVs, such as attacks on camera sensors, GPS location, Vehicle to Everything (V2X) message transmission, the vehicle's On-Board Unit (OBU), etc. The counter-measures to these attacks and vulnerabilities are presented via the current results in the CARAMEL project achieved by implementing the designed security algorithms.
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Citations
Automotive Cybersecurity: A Survey on Frameworks, Standards, and Testing and Monitoring Technologies
TL;DR: This study surveys automotive cybersecurity frameworks, standards, and testing/monitoring technologies to address vulnerabilities and weaknesses in modern vehicle systems, highlighting key findings, research gaps, and future research directions for software engineers and practitioners.
1
Securing Autonomous Vehicles Against GPS Spoofing Attacks: A Deep Learning Approach
Maliha Shabbir,Mohsin Kamal,Zahid Ullah,Maqsood Muhammad Khan +3 more
TL;DR: This research proposes a novel methodology that uses deep learning algorithms, such as Convolutional Neural Networks (CNN), and machine learning (ML) algorithms,such as Support Vector Machine (SVM), to protect CAVs from GPS location spoofing attacks.
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