With the emergence of connected and autonomous vehicles along with their growing integration of available services, in-vehicle electronic architecture is no longer able to work up to its full potential. Each new service demands processing and computation traditionally handled by a new Electronic Control Unit (ECU). Modern vehicle design tends to be architected around ECU gathered either by feature or by physical location to minimize wiring. Both architectures cannot exploit capabilities of Processing Units (PU) in term of resources such as MIPS and memories.In this paper, we propose a novel approach for the management of these resources by introducing multi-agent systems. This concept consists of integrating IoT-A architectures (implementing agents as close as possible to the hardware), and Avatar architectures (virtualizing the representation of the hardware in high level: Cloud and Edge computing). The result of this work is the emergence of a new EdgeAgent architectural proposal to extend vehicle management and functionalities while considering the environment for vehicles of the future.

The Internet connection is becoming ubiquitous in embedded systems, making them potential victims of intrusion. While in the age of deep learning, these algorithms tend to produce worse results than traditional machine learning. In this paper, we propose a methodology based on feed-forward neural network for intrusion detection. Better performances than traditional machine learning techniques can be achieved when all steps of the methodology are applied. Performance is evaluated on CICIDS2017, showing accuracy better than 99% and a false positive rate lower than 0.5%. After analysis of previous studies, their results are compared to the performance of the proposed approach. Finally, the neural network trained on a PC has been implemented on an automotive processor to characterize performance aspects.