The introduction of Information and Communications Technologies (ICT) systems into vehicles make them more prone to cyber-security attacks. Such attacks may impact on vehicles capability and, consequently, on the safety of drivers, passengers. Indeed, the strong integration between dedicated ICT devices, the physical environment, and the networking infrastructure, leads to consider modern vehicles as Cyber-Physical Systems.
This workshop aims at providing a forum for researchers and engineers in academia and industry to foster an exchange of research results, experiences, and products in the automotive domain from both a theoretical and practical perspective.
Its ultimate goal is to envision new trends and ideas about aspects of designing, implementing, and evaluating innovative solutions for the Cyber-Physical Systems with a particular focus on the new generation of vehicles.
Indeed, the automotive domain presents several challenges in the fields of vehicular network, Internet of Things, Privacy, as well as, Safety and Security methods and approaches.
The workshop aims at presenting the advancement on the state of art in these fields and spreading their adoption in several scenarios involving main stockholders of the automotive domain.
The list of topics includes (but it is not limited to):
- Architecture, design, and implementation of safe and secure Cyber-Physical Systems
- Automated Vehicular Technologies
- Vehicular Communications and Networks
- In-Vehicle communication protocols
- User-friendly authoring tools to edit privacy preferences
- Technical infrastructures for privacy and security policies management
- User-to-Vehicle interactions and communications
- Software Process Development in Automotive systems
- Security threats and vulnerabilities of Cyber-Physical Systems
- Offensive and Defensive Cybersecurity Strategies
- Safety and Security Trade-off and Convergences
- Cooperative/collaborative vehicular systems
- Cyber-security solutions for connected and autonomous vehicles
- Privacy of vehicular data
- Driver behaviour characterization
- Standardization and Interoperability
Welcome domains of application are (but may not limited to):
- Vehicular Network
- Embedded Systems
- Cyber-Physical Systems
- Smart cities and Smart environment
|Submission deadline for paper: |10/05/2021 |28/05/2021
|Notification of authors: ||30/06/2021
|Camera-ready copy due: ||16/07/2021
- Gianpiero Costantino, IIT-CNR, Italy
- Ilaria Matteucci, IIT-CNR, Italy
- Giampaolo Bella, Univesity of Catania, Italy
- Pietro Biondi, Univesity of Catania, Italy
- Jeremy Bryans, Coventry University, UK
- Gianpiero Costantino, IIT-CNR, Italy (Co-Chair)
- Bogdan Groza, Politehnica University of Timisoara, RO
- Sandeep Gupta, University of Trento, Italy
- Mathias Johanson, Alkit, Sweden
- Erich Leitgeb, University of Graz, Austria
- John Mace, University of Newcastle, UK
- Eda Marchetti, ISTI-CNR, Italy
- Ilaria Matteucci, IIT-CNR, Italy (Co-Chair)
- Francesco Mercaldo, IIT-CNR, Italy
- Davide Micale, Univesity of Catania, Italy
- Paolo Santi, MIT, US
- Francesco Santini, University of Perugia, Italy
- Stefano Sebastio, Raytheon Technologies, Ireland
- Daniele Sgandurra, Royal Holloway - University of London, UK
- Renaud Sirdey, CEA, France
The keynote Speaker is Roland Rieke, from Fraunhofer-Gesellschaft, Germany.
Title. Machine-learning methods for in-vehicle intrusion detection
Abstract. The networking of vehicles is essential if the traffic is to be controlled intelligently and the vehicles themselves are to make decisions for automated driving, depending on the situation in the immediate vicinity, but also in the more distant context that cannot be directly perceived by the vehicle. However, connectivity opens up new possibilities for attackers to remotely access safety-critical vehicle subsystems.
In this talk, various methods are presented with which the activities of an attacker who already has access to a subsystem in a vehicle can be detected. An overview of weak points is given and the current state of the art with regard to various detection methods is presented. In particular, various model-based methods of machine learning are analyzed with regard to their applicability for the detection of malicious messages in vehicles. Available data sets, test setup and results from experiments with different simulated attacks are presented.
In particular, we used process mining, support vector machines, artificial neural networks, genetic programming, and rule-based systems and combinations thereof.
Finally, model-based detection of anomalies is considered in a holistic framework for the management of security strategies.
Submitted papers must be written in English and must contain results that have not previously published nor concurrently submitted to a journal or conference with published proceedings. Submissions, as pdf files, must not exceed 6 pages of text (excluding references). Reviewers are explicitly not expected to read the appendices while deciding whether to accept or reject the paper. Papers must be typeset in LaTeX in A4 format (not “US Letter”) using the IEEE EuroS&P conference
proceeding template and must follow the guidelines of the main conference.
Papers must be submitted electronically through EasyChair.
Accepted papers will be published through IEEE Xplore in a volume accompanying the main IEEE EuroS&P 2021 proceedings.
It is required that each accepted paper be presented at the workshop by one of its authors.
For any question, please contact the firstname.lastname@example.org.