Transport Layer Scanning for Attack Surface Detection in Vehicular Networks

  • In the beginning of every security analysis or penetration test of a system, information about the target has to be gathered. On IT-Systems a port scan is usually performed as a first step of an investigation. Since the communication protocols differ in automotive systems, generic port scanning tools can’t be used for a security analysis of CANs. More complex protocols have a higher likelihood ofIn the beginning of every security analysis or penetration test of a system, information about the target has to be gathered. On IT-Systems a port scan is usually performed as a first step of an investigation. Since the communication protocols differ in automotive systems, generic port scanning tools can’t be used for a security analysis of CANs. More complex protocols have a higher likelihood of implementation errors and bugs. On CAN networks, such payloads are transferred through International Standard Transport Protocol (ISO-TP) communication. We designed a new methodology to identify ISO-TP endpoints in automotive networks. Every of these endpoints can provide exploitable application layer protocols and therefor has to be considered during penetration testing and security analysis. We contribute a new scan approach for the automated evaluation of possible attack surfaces in automotive CAN networks which has a higher coverage and multiple advantages than state of the art approaches.show moreshow less

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Metadaten
Author:Nils Weiss, Sebastian Renner, Jürgen MottokOTHORCiDGND, Václav Matoušek
DOI:https://doi.org/10.1145/3385958.3430476
Parent Title (English):CSCS '20: Computer Science in Cars Symposium
Place of publication:Feldkirchen
Document Type:conference proceeding (article)
Language:English
Year of first Publication:2020
Release Date:2021/02/10
Tag:attack surface detection; automation; automotive networks; network scan
Issue:7
First Page:1
Last Page:8
Institutes:Fakultät Elektro- und Informationstechnik
Research Center for Artificial Intelligence - RCAI
Fakultät Elektro- und Informationstechnik / Laboratory for Safe and Secure Systems (LAS3)
research focus:Digitale Transformation
Frontdoor-URL:https://opus4.kobv.de/opus4-oth-regensburg/1001
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