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Automated Threat Evaluation of Automotive Diagnostic Protocols

  • Diagnostic protocols in automotive systems can offer a huge attack surface with devastating impacts if vulnerabilities are present. This paper shows the application of active automata learning techniques for reverse engineering system state machines of automotive systems. The developed black-box testing strategy is based on diagnostic protocol communication. Through this approach, it is possible to automatically investigate a highly increased attack surface. Based on a new metric, introduced in this paper, we are able to rate the possible attack surface of an entire vehicle or a single Electronic Control Unit (ECU). A novel attack surface metric allows comparisons of different ECUs from different Original Equipment Manufacturers (OEMs), even between different diagnostic protocols. Additionally, we demonstrate the analysis capabilities of our graph-based model to evaluate an ECUs possible attack surface over a lifetime.

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Metadaten
Author:Nils Weiss, Sebastian Renner, Jürgen MottokORCiDGND, Václav Matoušek
URN:urn:nbn:de:bvb:898-opus4-29882
Parent Title (English):ESCAR USA, 2021, Virtual
Document Type:conference proceeding (article)
Language:English
Year of first Publication:2021
Publishing Institution:Ostbayerische Technische Hochschule Regensburg
Release Date:2022/03/02
Tag:Automated Network Scan; Automotive Diagnostic Protocols; Security Metrics
Pagenumber:20
Institutes:Fakultät Elektro- und Informationstechnik
Fakultät Elektro- und Informationstechnik / Laboratory for Safe and Secure Systems (LAS3)
Kostenträger (Forschungsprojekt, Labor, Einrichtung etc.):IUK-1711-0018
research focus:Digitalisierung
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG