Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles

The damaging effects of cyberattacks to an industry like the Cooperative Connected and Automated Mobility (CCAM) can be tremendous. From the least important to the worst ones, one can mention for example the damage in the reputation of vehicle manufacturers, the increased denial of customers to adopt CCAM, the loss of working hours (having direct impact on the European GDP), material damages, increased environmental pollution due e.g., to traffic jams or malicious modifications in sensors’ firmware, and ultimately, the great danger for human lives, either they are drivers, passengers or pedestrians. Connected vehicles will soon become a reality on our roads, bringing along new services and capabilities, but also technical challenges and security threats. To overcome these risks, the CARAMEL project has developed several anti-hacking solutions for the new generation of vehicles. CARAMEL (Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles), a research project co-funded by the European Union under the Horizon 2020 framework programme, is a project consortium with 15 organizations from 8 European countries together with 3 Korean partners. The project applies a proactive approach based on Artificial Intelligence and Machine Learning techniques to detect and prevent potential cybersecurity threats to autonomous and connected vehicles. This approach has been addressed based on four fundamental pillars, namely: Autonomous Mobility, Connected Mobility, Electromobility, and Remote Control Vehicle. This book presents theory and results from each of these technical directions

Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles

The damaging effects of cyberattacks to an industry like the Cooperative Connected and Automated Mobility (CCAM) can be tremendous. From the least important to the worst ones, one can mention for example the damage in the reputation of vehicle manufacturers, the increased denial of customers to adopt CCAM, the loss of working hours (having direct impact on the European GDP), material damages, increased environmental pollution due e.g., to traffic jams or malicious modifications in sensors’ firmware, and ultimately, the great danger for human lives, either they are drivers, passengers or pedestrians. Connected vehicles will soon become a reality on our roads, bringing along new services and capabilities, but also technical challenges and security threats. To overcome these risks, the CARAMEL project has developed several anti-hacking solutions for the new generation of vehicles. CARAMEL (Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles), a research project co-funded by the European Union under the Horizon 2020 framework programme, is a project consortium with 15 organizations from 8 European countries together with 3 Korean partners. The project applies a proactive approach based on Artificial Intelligence and Machine Learning techniques to detect and prevent potential cybersecurity threats to autonomous and connected vehicles. This approach has been addressed based on four fundamental pillars, namely: Autonomous Mobility, Connected Mobility, Electromobility, and Remote Control Vehicle. This book presents theory and results from each of these technical directions

Algoritmos para a optimización da asignación de puntos de acceso en redes de acceso sen fíos controladas por SDN

Wireless networks are migrating from 4G toward 5G. 5G comprises the next major mobile telecommunication standards that will be able to support thousands of users with up to 100.000 simultaneous communications with more coverage. It is expected that there would be problems to increase data traffic as multiple devices will be simultaneously accessing the network expecting the best user experience. Currently, mobile terminals feature multiple interfaces to adapt to the steadily increasing number of available wireless access networks. This provides a suitable ground for offloading data from cellular to different WIFI access points using the integration of WIFI and LTE offered by LTE v.12 and v.13. There is a parallel trend towards network programming relying on centralized controllers, of which the Software-Defined Network (SDN) architecture with the OpenFlow protocol is a clear exponent. We intend to design and implement a global network optimization algorithm that will use flow steering techniques to deal with the increasing data traffic. This algorithm will be applied on an SDN architecture where the end-terminals will be integrated with the core network.Erasmus Mundus EGOVTN: Open Government data in Tunisia for Service Innovation and Transparency. Grant Agreement nº 2013-2434/001-001-EMA

Enhancing Healthcare Ecosystem Cybersecurity: The Role of BAE in the SECANT Project

a white paper on: “Enhancing Healthcare Ecosystem Cybersecurity: The Role of BAE in the SECANT Project