Automated driving requires increasing networking of vehicles, which in turn broadens their attack surface. In this paper, we describe several security design patterns that target critical steps in automotive attack chains and mitigate their con-sequences. These patterns enable the detection of anomalies in the firmware when booting, detect anomalies in the communication in the vehicle, prevent unauthorized control units from successfully transmitting messages, offer a way of transmitting security-related events within a vehicle network and reporting them to units external to the vehicle, and ensure that communication in the vehicle is secure. Using the example of a future high-level Electrical / Electronic (E / E) architecture, we also describe how these security design patterns can be used to become aware of the current attack situation and how to react to it.

SECPAT: Security Patterns for Resilient Automotive e / e Architectures

Matteucci I;Costantino G;De Vincenzi M
2022

Abstract

Automated driving requires increasing networking of vehicles, which in turn broadens their attack surface. In this paper, we describe several security design patterns that target critical steps in automotive attack chains and mitigate their con-sequences. These patterns enable the detection of anomalies in the firmware when booting, detect anomalies in the communication in the vehicle, prevent unauthorized control units from successfully transmitting messages, offer a way of transmitting security-related events within a vehicle network and reporting them to units external to the vehicle, and ensure that communication in the vehicle is secure. Using the example of a future high-level Electrical / Electronic (E / E) architecture, we also describe how these security design patterns can be used to become aware of the current attack situation and how to react to it.
2022
Istituto di informatica e telematica - IIT
978-1-6654-6959-3
automotive threat mitigation
resilience
automotive security
cybersecurity engineering
intrusion detection
connected car
AUTOSAR
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/418228
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