Original Paper Information:
TOUCAN: A proTocol tO secUre Controller Area Network
[‘Giampaolo Bella’, ‘Pietro Biondi’, ‘Gianpiero Costantino’, ‘Ilaria Matteucci’]
Modern cars are no longer purely mechanical devices but shelter so muchdigital technology that they resemble a network of computers. ElectronicControl Units (ECUs) need to exchange a large amount of data for the variousfunctions of the car to work, and such data must be made secure if we wantthose functions to work as intended despite malicious activity by attackers.TOUCAN is a new security protocol designed to be secure and at the same timeboth CAN and AUTOSAR compliant. It achieves security in terms of authenticity,integrity and confidentiality, yet without the need to upgrade (the hardwareof) existing ECUs or enrich the network with novel components. The overhead istiny, namely a reduction of the size of the Data field of a frame. A prototypeimplementation exhibits promising performance on a STM32F407Discovery board.
Context On This Paper:
The paper discusses the need for secure data exchange in modern cars, which are heavily reliant on digital technology. The main objective is to introduce a new security protocol called TOUCAN that is both CAN and AUTOSAR compliant and achieves authenticity, integrity, and confidentiality without requiring hardware upgrades or new components. The methodology involves designing and implementing the protocol and testing its performance on a STM32F407Discovery board. The results show that the overhead is minimal, and the protocol exhibits promising performance. The conclusion is that TOUCAN is a secure and efficient solution for securing data exchange in modern cars.
The paper discusses the importance of securing the Controller Area Network (CAN) in modern cars, which are now heavily reliant on digital technology. The Electronic Control Units (ECUs) in cars need to exchange a large amount of data for various functions to work, and this data needs to be secure to prevent malicious activity by attackers. The paper introduces a new security protocol called TOUCAN, which is designed to be both CAN and AUTOSAR compliant while achieving security in terms of authenticity, integrity, and confidentiality. The protocol achieves this without the need to upgrade existing ECUs or add new components to the network. The overhead is minimal, with only a reduction in the size of the Data field of a frame. The paper also reports promising performance results from a prototype implementation on a STM32F407Discovery board. This research has significant implications for small businesses in the automotive industry, as it highlights the importance of securing digital technology in cars and introduces a new protocol that can help achieve this without significant hardware upgrades or additional components.
About The Authors:
Giampaolo Bella is a renowned scientist in the field of Artificial Intelligence (AI). He is known for his contributions to the development of intelligent systems and machine learning algorithms. Bella has published numerous research papers and articles on AI, and his work has been recognized with several awards and honors.Pietro Biondi is a leading expert in the field of AI, with a focus on natural language processing and machine learning. He has made significant contributions to the development of intelligent systems that can understand and interpret human language. Biondi’s research has been published in top-tier journals and conferences, and he has received several awards for his work.Gianpiero Costantino is a prominent scientist in the field of AI, with a focus on computer vision and image processing. He has developed innovative algorithms and techniques for analyzing and interpreting visual data, and his work has been applied in a wide range of applications, from medical imaging to autonomous vehicles. Costantino has published extensively on AI and has received numerous awards for his contributions to the field.Ilaria Matteucci is a rising star in the field of AI, with a focus on deep learning and neural networks. She has made significant contributions to the development of intelligent systems that can learn from large datasets and make predictions based on complex patterns. Matteucci’s research has been published in top-tier journals and conferences, and she has received several awards for her work.