2020

• Souli, N., C. Laoudias, P. Kolios, C. Vitale, G. Ellinas, A. Lalos, J. Casademont, P. S. Khodashenas, and P. Kapsalas. “Gnss Location Verification In Connected And Autonomous Vehicles Using In-Vehicle Multimodal Sensor Data Fusion”. In 2020 22Nd International Conference On Transparent Optical Networks (Icton), 1-4, 2020. doi:10.1109/ICTON51198.2020.9203087.

  AbstractDOI

  Connected and Autonomous Vehicles (CAVs) rely on Global Navigation Satellite Systems (GNSS), e.g., the Global Positioning System (GPS), for the provision of accurate location information for various functionalities including Vehicle-to-Vehicle/Infrastructure (V2V/V2I) communication and self-navigation. However, GNSS-based location awareness is prone to spoofing attacks, where the attacker generates counterfeit satellite signals. This in turn poses a serious threat to the CAV, e.g., car, drone, etc., as well as the surrounding entities. Thus, this threat needs to be detected reliably and mitigated timely to prevent undesired consequences (e.g., damages, casualties, etc.). To this end, this work proposes a location verification solution that leverages in-vehicle sensor readings (e.g., accelerometer, etc.) and Signals of Opportunity (SoO), as an alternative source of location information. In particular, the multimodal sensor data with SoO location measurements are fused by means of a Kalman filter and the estimated fusion-based location is used to verify the location output of the GPS receiver. In case the GPS location deviates considerably from the fusion-based location, then a location spoofing attack is ascertained. Preliminary experimental results with real GPS and sensor data collected with a drone demonstrate the effectiveness of the proposed approach.
• Casademont, J., B. Cordero, D. Camps-Mur, L. A. M. da Conceição, A. Lalos, C. Vitale, C. Laoudias, and P. S. Khodashenas. “Multi-Radio V2X Communications Interoperability Through A Multi-Access Edge Computing (Mec)”. In 2020 22Nd International Conference On Transparent Optical Networks (Icton), 1-4, 2020. doi:10.1109/ICTON51198.2020.9203495.

  AbstractDOI

  Nowadays, we are ready to have precommercial Cooperative Intelligent Transport Systems (C-ITS), nevertheless there exist challenging functional and security aspects that need to be addressed. One of them is the fact that, in every era, there will be several radio technologies which will be used by vehicles that need to be connected between them, therefore, the systems needs to provide interoperability services. The other critical issue is to reinforce security against attacks on localization receivers or in vehicles equipment. Most of these functions are based in a large amount of computation power, to this end, this paper presents the approach taken by H2020 CARAMEL project, using a Multi-access Edge Computing (MEC) that could provide the necessary performance assets.
• Piperigkos, N., A. S. Lalos, K. Berberidis, C. Laoudias, and K. Moustakas. “5G Enabled Cooperative Localization Of Connected And Semi-Autonomous Vehicles Via Sparse Laplacian Processing”. In 2020 22Nd International Conference On Transparent Optical Networks (Icton), 1-4, 2020. doi:10.1109/ICTON51198.2020.9203314.

  AbstractDOI

  Cooperative Localization has received extensive interest from several scientific communities including Robotics, Optimization, Signal Processing and Wireless Communications. It is expected to become a major aspect for a number of crucial applications in the field of Connected and (Semi-) Autonomous vehicles (CAVs), such as collision avoidance/warning, cooperative adaptive cruise control, safely navigation, etc. 5G mobile networks will be the key to providing connectivity for vehicle to everything (V2X) communications, allowing CAVs to share with other entities of the network the data they collect and measure. Typical measurement models usually deployed for this problem, are absolute position information from Global Positioning System (GPS), relative distance to neighbouring vehicles and relative angle or azimuth angle, from Light Detection and Ranging (LIDAR) or Radio Detection and Ranging (RADAR) sensors. In this paper, we provide a cooperative estimation approach that performs multi modal-fusion between interconnected vehicles. This method is based on a Graph Signal Processing tool, known as Laplacian Graph Processing, and significantly outperforms existing method both in terms of attained accuracy and computational complexity.
• Vitale, C., N. Piperigkos, C. Laoudias, G. Ellinas, J. Casademont, P. Sayyad Khodashenas, A. Kloukiniotis, et al. “The Caramel Project: A Secure Architecture For Connected And Autonomous Vehicles”. In 2020 European Conference On Networks And Communications (Eucnc), 133-138, 2020. doi:10.1109/ICTON51198.2020.9203087.

  AbstractDOI

  Connected and Autonomous Vehicles (CAVs) rely on Global Navigation Satellite Systems (GNSS), e.g., the Global Positioning System (GPS), for the provision of accurate location information for various functionalities including Vehicle-to-Vehicle/Infrastructure (V2V/V2I) communication and self-navigation. However, GNSS-based location awareness is prone to spoofing attacks, where the attacker generates counterfeit satellite signals. This in turn poses a serious threat to the CAV, e.g., car, drone, etc., as well as the surrounding entities. Thus, this threat needs to be detected reliably and mitigated timely to prevent undesired consequences (e.g., damages, casualties, etc.). To this end, this work proposes a location verification solution that leverages in-vehicle sensor readings (e.g., accelerometer, etc.) and Signals of Opportunity (SoO), as an alternative source of location information. In particular, the multimodal sensor data with SoO location measurements are fused by means of a Kalman filter and the estimated fusion-based location is used to verify the location output of the GPS receiver. In case the GPS location deviates considerably from the fusion-based location, then a location spoofing attack is ascertained. Preliminary experimental results with real GPS and sensor data collected with a drone demonstrate the effectiveness of the proposed approach.
• Kyrkou, Christos, Andreas Papachristodoulou, Andreas Kloukiniotis, Andreas Papandreou, Aris S. Lalos, Konstantinos Moustakas, and Theocharis Theocharides. “Towards Artificial-Intelligence-Based Cybersecurity For Robustifying Automated Driving Systems Against Camera Sensor Attacks”. In 2020 Ieee Computer Society Annual Symposium On Vlsi (Isvlsi), 476-481, 2020. doi:10.1109/ISVLSI49217.2020.00-11.

  AbstractDOI

  CARAMEL is a European project that aims amongst others to improve and extend cyberthreat detection and mitigation techniques for automotive driving systems. This paper highlights the important role that advanced artificial intelligence and machine learning techniques can have in proactively addressing modern autonomous vehicle cybersecurity challenges and on mitigating associated safety risks when dealing with targetted attacks on a vehicle's camera sensors. The cybersecurity solutions developed by CARAMEL are based on powerful AI tools and algorithms to combat security risks in automated driving systems and will be hosted on embedded processors and platforms. As such, it will be possible to have a specialized anti-hacking device that addresses newly introduced technological dimensions for increased robustness and cybersecurity in addition to industry needs for high speed, low latency, functional safety, light weight, low power consumption.