5th Innovation & Entrepreneurship Forum (IEF2020) 2nd Presentation

5th Innovation & Entrepreneurship Forum (IEF2020) 2nd Presentation

IEF 2020 is organized by the Centre for Entrepreneurship of the University of Cyprus in collaboration with PwC Cyprus. The 2020 Forum will bring together AI experts, researchers and professionals, decision-makers, entrepreneurs and game-changers to discuss the Challenge of Artificial Intelligence and address the most defying questions around the future of AI and its impact on society, economy and politics.

Recently our collaborators from University of Cyprus presented their AI research results “DriveGuard: A deep learning technique for countering cyber-attacks against autonomous vehicle camera sub-systems in the context of the H2020 CARAMEL project” as part of the activities of the 5th innovation & Entrepreneurship Forum (IEF2020): “The Challenge of AI”


In the context of CARAMEL, we investigate the effect of camera sensor attacks for visual AI tasks in autonomous vehicles and present research results for a deep learning technique called DriveGuard, that act as a defense and mitigation mechanism for autonomous vehicle perception systems. This approach can provide protection against scenarios where an attacker can gain access to vital vehicle components by taking advantage of over-the-air updates in order to instantiate attacks that are not immediately detectable or perceivable, in contrast to just switching components off. DriveGuard utilizes the convolutional autoencoder family of deep learning networks to build an approach for efficient image reconstruction and anomaly detection. The approach will be integrated into an embedded anti-hacking device that will be capable for passive detection of attacks on an autonomous vehicle’s visual perception modules.

Approach to guard the segmentation module. The algorithm identifies different objects on the scene.
Left) Original image, Center) Attacked image, Right) Reconstructed image

You can find more information about this topic at the following video:

5th Innovation & Entrepreneurship Forum (IEF2020)

5th Innovation & Entrepreneurship Forum (IEF2020)

IEF 2020 is organized by the Centre for Entrepreneurship of the University of Cyprus in collaboration with PwC Cyprus. The 2020 Forum will bring together AI experts, researchers and professionals, decision-makers, entrepreneurs and game-changers to discuss the Challenge of Artificial Intelligence and address the most defying questions around the future of AI and its impact on society, economy and politics.

Recently our collaborators from University of Cyprus presented “A modular approach to detect GPS location spoofing attack in autonomous vehicles” as part of the activities of the 5th innovation & Entrepreneurship Forum (IEF2020): “The Challenge of AI”

Autonomous vehicles can interact with their environment without human interaction through built-in sensors such as radar, rgb, lidar, GPS, ultrasonic that interact with the ECU to make automatic decisions. Therefore, enabling improvements in fuel efficiency, reduction of travel time, reduction of accidents, among others.

However, this dependence on sensor readings makes autonomous vehicles susceptible to attacks. An attacker could send false GPS signals to deceive the autonomous vehicles.

In this context, it’s being presented a cost-effective and modular solution for the AVs to timely and reliably detect and mitigate the GPS spoofing attack. Specifically, we develop an in-vehicle detection scheme that leverages multi-sensor fusion coupled with Bayesian filtering to produce in real-time an alternative GPS-free location stream of the AV that is used to check the integrity of the GPS location stream.

We invite you to watch the video presentation.

IEF2020 – Home (

Learn more about our new partners

Learn more about our new partners

During the last months the coordinator of the project Pouria Sayaad Kodashenas has been in constant contact with Korean entities with the purpose of concreting a new mutual agreement of joint work between 3 new Korean entities and the CARAMEL Project. It is a pleasure to announce that this agreement has finally been consolidated.
With this announcement we are keen to welcome the group of companies that from now on have become official members of the CARAMEL consortium.
  • ETRI
KATECH Korea Automotive Technology Research Institute is a research institute in Korea specializing in automotive parts. They were stablished in 1990 with the aim of developing domestic automobile technology research. A lustrum after they expanded their business as an Authorized Testing Institution. improving reliability. During the first decade of the 20th century they have been continuing its development of autoparts with a focus on endeavor quality improvements. After a successful start they have been focusing also on RnD activities.

MOBIGEN is a South Korean company that started its business in 2000, after it opened its R&D Center. Since its establishment, they have been awarded many distinctions due to its outstanding performance. They have been focusing on Big Data & OSS(Operations Support System) Solution Provider for Operators in Major Infra Structure – Telecom, Electricity & Utility, mostly with a focus on Research and Development. The core concept of Mobigen solutions focuses on the Real-time Processing of Massive Data.

ETRI The Electronics and Telecommunications Research Institute is a Korean government-funded research institution. ETRI makes contribution to the nation’s economic and social development through research, development, and distribution of industrial core technologies in the field of Information, Communications, Electronics, Broadcasting, and Convergence technologies.

Its Role in CARAMEL

Providing trial site and trial execution

Remote driving use case

Vehicle Gateway

Automotive cyber security AI edge HW platform

Cyber attack detection and estimation algorithm based on ML

7th General Assembly meeting

7th General Assembly meeting

The CARAMEL project held its seventh plenary meeting in an online format with representatives of each of the consortium’s member companies, and on this occasion we had the participation of the new official partners.

In such meting each of the consortium entity gave a brief introduction to its former company, and a brief explanation about its role in the project.

it was an outstanding introductory meeting among all participants.

Exploring adversarial attacks and defenses for fake twitter account detection

Exploring adversarial attacks and defenses for fake twitter account detection

Did you know that some of the most common used social media channels are being affected by the creation of false accounts that aims to bias the opinion of specific large groups of people by publishing fake news.

Have you ever thought how might be possible to detect such accounts?

One of CARAMEL’s partners  has just released a very interesting article about Exploring Adversarial Attacks and Defences for Fake Twitter Account Detection.

We encourage to read this interesting just released paper.

  • Papandreou, Andreas, Andreas Kloukiniotis, Aris Lalos, and Konstantinos Moustakas. “Deep Multi-Modal Data Analysis and Fusion for Robust Scene Understanding in CAVs”. Edited by IEEE MMSP 2021 (October 2021).
  • Kantartopoulos, Panagiotis, Nikolaos Pitropakis, Alexios Mylonas, and Nicolas Kylilis. “Exploring Adversarial Attacks and Defences for Fake Twitter Account Detection”. Technologies 8, no. 4 (2020): 64. doi:
Online General Assembly Meeting

Online General Assembly Meeting

The CARAMEL project held its sixth plenary meeting in an online format with representatives of each of the consortium’s member companies.

This special session was attended by the external advisory board EAB, composed of international experts in the key areas for CARAMEL, who listened to the progress made in each of the work packages and provided strategic guidance and support, so that the project would achieve the proposed objectives.

On this occasion, the topics addressed were

  • WP2 Use cases, risk assessment, requirements and architecture
  • WP3 Countermeasures and mitigation techniques for advanced cybersecurity
  • WP4 Cross-cutting detection and cybersecurity prevention
  • WP5 Development of antihacking device and in-depth defense
  • WP7 Dissemination, communication and exploitation of results


CARAMEL was pleased to present a cybersecurity workshop in the scope of ITSC 2020.

As part of the activities of the IEEE ITSC 2020, a workshop on cyber security was offered on September 20th as a joint effort between two major H2020 EU-funded projects: the nIoVe (A Novel Adaptive Cybersecurity Framework for the Internet-of-Vehicles) and CARAMEL (Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles)

The CyberSec workshop organized by ATHENA Research & Innovation Center, CENTRE FOR RESEARCH AND TECHNOLOGY-HELLAS (CERTH), KIOS, and University Cyprus, presented current innovative activities and R&D activities in public and private sectors in the area of cybersecurity.

The slides of the showcased presentations can be found at the following link.

  • A novel Adaptive Cybersecurity Framework for the Internet-of-Vehicles: The nIoVe approach
  • Protecting the new generation of cars against cybercriminals
  • Privacy in Cooperative Intelligent Transport Systems (C-ITS): threats, impact and assessment
  • Risk Analysis and Security Assurance in Connected Vehicles: The SAFERtec and 2CeVau approach
  • Security and safety for the Internet-of-Things

Protecting the new generation of cars against cybercriminals


CARAMEL was pleased to contribute to the EWGT2020, with three papers for the session Cybersecurity of Connected and Automated Vehicles.

As part of this year’s activities of the EWGT conference carried in Papus, Cyprus from September 16-18. CARAMEL presented the following 3 papers, product of the ongoing research activities:

  • Addressing Cybersecurity in the Next Generation Mobility Ecosystem with CARAMEL
  • A benchmarking framework for cyber-attacks on autonomous vehicles
  • Impact of False Data Injection attacks on Decentralized Electric Vehicle Charging Protocols

Addressing Cybersecurity in the Next Generation Mobility Ecosystem with CARAMEL

The proliferation of next generation mobility, promotes the use of autonomous cars, connected vehicles and electromobility. It creates novel attack surfaces for high impact cyberattacks affecting the society. Addressing the cybersecurity challenges introduced by modern vehicles requires a proactive and multi-faceted approach combining techniques originating from various domains of ICT. Emerging technologies such as 5G, LiDAR, novel in-vehicle and roadside sensors and smart charging, used in modern cars, introduce new challenges and potential security gaps in the next generation mobility ecosystem. Thus, it is critical that the domain’s cybersecurity must be approached in a structured manner from a multi-domain and multi-technology perspective. The CARAMEL H2020 project aims to address the cybersecurity challenges on the pillars upon which the next generation mobility is constructed (i.e., autonomous mobility, connected mobility, electromobility). To achieve that, advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques will be utilized for the identification of anomalies and the classification of incoming signals indicating a cyber-attack or a cybersecurity risk. Apart from risk detection, methods for the mitigation of the identified risks will also be continuously incorporated to the CARAMEL solution. The final goal of CARAMEL is to create an anti-hacking platform for the European automotive cybersecurity and to demonstrate its value through extensive attack and penetration scenarios. In this paper we will expand on the unique cybersecurity-relevant characteristics of the pillars upon which the CARAMEL solution is built. Next, a number of use cases emerging from such analysis will be extracted in order to form the basis upon which the CARAMEL platform will be evaluated. Finally, we will conclude with an overview of the platform’s architectural composition.

A benchmarking framework for cyber-attacks on autonomous vehicles

In this paper, a novel framework for a benchmark system for autonomous vehicles focusing on their security and reliability is proposed. Computer vision and networking technologies are improving offering solutions towards automation in connected autonomous vehicles. These systems are using sensor technologies, including vision and communication, providing information and measurements for the environment and other connected vehicles. As a result, unlike conventional vehicles, autonomous vehicles have to communicate with other vehicles as well as other external network infrastructure. However, such requirements make autonomous vulnerable to the attack. This may also motivate various types of cyber threats and attacks like traffic signs modification, GPS spoofing, and Vehicular Adhoc network distributed denial of service. Hence, this paper explores various aspects of security issues, vulnerabilities, exploitation methods and the adverse effect of them on connected autonomous vehicles and proposes a novel benchmark framework focusing on physical and communication-based attack to evaluate and assets the state- of-the-art technologies that are currently used during cyber-attack.

Impact of False Data Injection attacks on Decentralized Electric Vehicle Charging Protocols

Electric vehicles (EVs) gain great attention nowadays since the electrification of private and public transport has a great potential to reduce greenhouse gas emissions and mitigate oil dependency. However, the influx of a large number of electrical loads without any coordination could have adverse aects to the electrical grid. More importantly, the complexity in the coordination of a large number of EVs, pose critical challenges in ensuring overall system integrity. A typical attack found in the controllers of connected EVs is false data injection (FDI), which can be utilized to distort real energy demand and supply figures. Energy distribution requests may therefore be erroneous, which results in additional costs or more devastating hazards. The lack of a proper coordination scheme, robust to such cyber attacks could cause voltage magnitude drops and unacceptable load peaks. In this work, we study the impact of FDI attacks, on various decentralised charging protocol with reduced computational requirements. The proposed decentralised EV charging algorithms only require from each EV to solve a local problem, hence the proposed implementation require low computational resources. An extensive evaluation study highlights the strengths and weaknesses of the presented solutions which are based on iterative convex optimization solvers.

Special thanks to the organizers of this event: Research and Innovation Center of Excellence (KIOS CoE), University of Cyprus which are also members of CARAMEL’s consortium.


CARAMEL was present yesterday at the International Conference on Transparent Optical Networks with the presentation of the following papers:

ICTON-OSCto5G GNSS Location Verification in Connected and Autonomous Vehicles using In-Vehicle Multimodal Sensor Data Fusion

ICTON-OSCto5G 5G enabled cooperative localization of connected and semi-autonomous vehicles via sparse Laplacian processing


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 Systems (GPSs), 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.

ICTON-CTSII Multi-radio V2X communications interoperability through a multi-access edge computing (MEC)


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.

All presentations can be accessed from the ICTON Conference online platform.

Until 30 September 2020


The CARAMEL project participated yesterday in the ISLVSI conference, presenting the paper ” Towards artificial-intelligence-based cybersecurity for robustifying automated driving systems against camera sensor attacks ” [1]

The conference presentation can be accessed by following the steps mentioned by the ISLVSI organizers:


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.

C. Kyrkou et al., “Towards Artificial-Intelligence-Based Cybersecurity for Robustifying Automated Driving Systems Against Camera Sensor Attacks,” 2020 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), Limassol, Cyprus, 2020, pp. 476-481, doi: 10.1109/ISVLSI49217.2020.00-11.

Theme: Elation by Kaira.