Ethernet - a survey on its fields of application

During the last decades, Ethernet progressively became the most widely used local area networking (LAN) technology. Apart from LAN installations, Ethernet became also attractive for many other fields of application, ranging from industry to avionics, telecommunication, and multimedia. The expanded application of this technology is mainly due to its significant assets like reduced cost, backward-compatibility, flexibility, and expandability. However, this new trend raises some problems concerning the services of the protocol and the requirements for each application. Therefore, specific adaptations prove essential to integrate this communication technology in each field of application. Our primary objective is to show how Ethernet has been enhanced to comply with the specific requirements of several application fields, particularly in transport, embedded and multimedia contexts. The paper first describes the common Ethernet LAN technology and highlights its main features. It reviews the most important specific Ethernet versions with respect to each application field’s requirements. Finally, we compare these different fields of application and we particularly focus on the fundamental concepts and the quality of service capabilities of each proposal

In-vehicle communication networks : a literature survey

The increasing use of electronic systems in automobiles instead of mechanical and hydraulic parts brings about advantages by decreasing their weight and cost and providing more safety and comfort. There are many electronic systems in modern automobiles like antilock braking system (ABS) and electronic brakeforce distribution (EBD), electronic stability program (ESP) and adaptive cruise control (ACC). Such systems assist the driver by providing better control, more comfort and safety. In addition, future x-by-wire applications aim to replace existing braking, steering and driving systems. The developments in automotive electronics reveal the need for dependable, efficient, high-speed and low cost in-vehicle communication. This report presents the summary of a literature survey on in-vehicle communication networks. Different in-vehicle system domains and their requirements are described and main invehicle communication networks that have been used in automobiles or are likely to be used in the near future are discussed and compared with key references

Smart Procurement of Naturally Generated Energy (SPONGE) for Plug-in Hybrid Electric Buses

We discuss a recently introduced ECO-driving concept known as SPONGE in the context of Plug-in Hybrid Electric Buses (PHEB)'s.Examples are given to illustrate the benefits of this approach to ECO-driving. Finally, distributed algorithms to realise SPONGE are discussed, paying attention to the privacy implications of the underlying optimisation problems.Comment: This paper is recently submitted to the IEEE Transactions on Automation Science and Engineerin

A robust, reliable and deployable framework for In-vehicle security

Cyber attacks on financial and government institutions, critical infrastructure, voting systems, businesses, modern vehicles, etc., are on the rise. Fully connected autonomous vehicles are more vulnerable than ever to hacking and data theft. This is due to the fact that the protocols used for in-vehicle communication i.e. controller area network (CAN), FlexRay, local interconnect network (LIN), etc., lack basic security features such as message authentication, which makes it vulnerable to a wide range of attacks including spoofing attacks. This research presents methods to protect the vehicle against spoofing attacks. The proposed methods exploit uniqueness in the electronic control unit electronic control unit (ECU) and the physical channel between transmitting and destination nodes for linking the received packet to the source. Impurities in the digital device, physical channel, imperfections in design, material, and length of the channel contribute to the uniqueness of artifacts. I propose novel techniques for electronic control unit (ECU) identification in this research to address security vulnerabilities of the in-vehicle communication. The reliable ECU identification has the potential to prevent spoofing attacks launched over the CAN due to the inconsideration of the message authentication. In this regard, my techniques models the ECU-specific random distortion caused by the imperfections in digital-to-analog converter digital to analog converter (DAC), and semiconductor impurities in the transmitting ECU for fingerprinting. I also model the channel-specific random distortion, impurities in the physical channel, imperfections in design, material, and length of the channel are contributing factors behind physically unclonable artifacts. The lumped element model is used to characterize channel-specific distortions. This research exploits the distortion of the device (ECU) and distortion due to the channel to identify the transmitter and hence authenticate the transmitter.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/154568/1/Azeem Hafeez Final Disseration.pdfDescription of Azeem Hafeez Final Disseration.pdf : Dissertatio

AI-based intrusion detection systems for in-vehicle networks: a survey.

The Controller Area Network (CAN) is the most widely used in-vehicle communication protocol, which still lacks the implementation of suitable security mechanisms such as message authentication and encryption. This makes the CAN bus vulnerable to numerous cyber attacks. Various Intrusion Detection Systems (IDSs) have been developed to detect these attacks. However, the high generalization capabilities of Artificial Intelligence (AI) make AI-based IDS an excellent countermeasure against automotive cyber attacks. This article surveys AI-based in-vehicle IDS from 2016 to 2022 (August) with a novel taxonomy. It reviews the detection techniques, attack types, features, and benchmark datasets. Furthermore, the article discusses the security of AI models, necessary steps to develop AI-based IDSs in the CAN bus, identifies the limitations of existing proposals, and gives recommendations for future research directions

A novel framework for vehicle functions identification by exploiting machine learning techniques

openNowadays vehicles architectures exploit various automotive network protocols that bring information between the implemented Electronic Central Units (ECUs). Exchanged data are encoded and only Original Equipment Manufacturers (OEMs) and T1 (Tier One) producers know their meaning and how decode them. A software model will be developed in order to detect vehicles functions without having database files associated to network signals. Furthermore, the model will behave like an ECU by producing output signals related to input ones. Machine Learning techniques will be exploited, in particular Clustering task will be exploited to understand not a priori known vehicle functions and a Neural Network will be implemented to emulate an ECU behavior. Signals will be grouped in five different types of vehicle functions and the model will predict the ECU’s output data with high accuracy. Applications concerning the developed project are, in primis, to fix up possible vehicles electronics faults. In addiction, vehicle predictive maintenance could be done. Another application, could be to check by OEMs if T1 manufacturers comply the required specification.Nowadays vehicles architectures exploit various automotive network protocols that bring information between the implemented Electronic Central Units (ECUs). Exchanged data are encoded and only Original Equipment Manufacturers (OEMs) and T1 (Tier One) producers know their meaning and how decode them. A software model will be developed in order to detect vehicles functions without having database files associated to network signals. Furthermore, the model will behave like an ECU by producing output signals related to input ones. Machine Learning techniques will be exploited, in particular Clustering task will be exploited to understand not a priori known vehicle functions and a Neural Network will be implemented to emulate an ECU behavior. Signals will be grouped in five different types of vehicle functions and the model will predict the ECU’s output data with high accuracy. Applications concerning the developed project are, in primis, to fix up possible vehicles electronics faults. In addiction, vehicle predictive maintenance could be done. Another application, could be to check by OEMs if T1 manufacturers comply the required specification