Multi-layer security assurance of the 5G automotive system based on multi-criteria decision making

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Security assurance is the capacity of any teleinformatic system to demonstrate that the system is secure. It is provided by testing the system by an independent laboratory. Such an evaluation gives to the customer certainty that the system or product is secure enough for the intended use. In this paper we discuss the security assurance that the automotive sector requires to the 5G network for the secure communication of the Intelligent Transport System applications. In this case, the automotive sector takes the role of the customer of the network operator that provides connectivity of cars, trucks, bicycles, pedestrians and traffic infrastructure for creating vehicle to everything platform. Concretely, in this paper we (1) show new methodologies for evaluating network security, (2) provide a new strategy for the customer (automotive company) to select the underlying system based on network assurance levels, (3) survey security functionalities of a network devoted to automotive applications and demonstrate how automotive applications, 5G network and physical infrastructure, cooperate for enhancing security of the end-to-end system, and (4) provide example security evaluation results at different assurance levels. The results show the necessity of providing different levels of network assurance during the certification process in order that the automotive customer will be able to select the best products and sub-systems that may demonstrate (assure) enough security to the complete system.
This work was supported in part by the National Center for Research and Development in Poland under Grant 381282/II/NCBR/2018, in part by the National Science Foundation under Grant 2150213 and Grant 1956193, and in part by the Project titled “Smart and Health Ageing through People Engaging in Supporting Systems” (SHAPES) under Grant 857159. The work of Luis J. de la Cruz Llopis was supported by the Spanish Government under the Research Project “Enhancing Communication Protocols with Machine Learning While Protecting Sensitive Data (COMPROMISE)” funded by MCIN/AEI/10.13039/501100011033 under Grant PID2020-113795RBC3X.
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