<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>2988</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>20</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-05-01</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Automated Threat Evaluation of Automotive Diagnostic Protocols</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">ESCAR USA, 2021, Virtual</parentTitle>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-29882</identifier>
    <enrichment key="Kostentraeger">IUK-1711-0018</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Nils Weiss</author>
    <author>Sebastian Renner</author>
    <author>Jürgen Mottok</author>
    <author>Václav Matoušek</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Automotive Diagnostic Protocols</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Security Metrics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Automated Network Scan</value>
    </subject>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/2988/ESCARPaper.pdf</file>
  </doc>
</export-example>
