A Review on Automatic Generation of Attack Trees and Its Application to Automotive Cybersecurity

Kacper Jakub Sowka, Vasile Palade, Hesam Jadidbonab, Paul Wooderson, Hoang Nga Nguyen

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


A comprehensive cybersecurity evaluation of automotive on-board networks has become a crucial antecedent to the commercial distribution of vehicles. However, the means to perform the required testing and risk assessment are limited due to the complex and increasingly obscure nature of automotive systems. To rectify this, several approaches have been put forward to systematise and automate the process of evaluating cybersecurity in vehicular systems, but these still require a significant amount of expert input. Accordingly, this work evaluates the existing state of the art in attack tree generation as a means towards automation and systematisation of automotive cybersecurity assurance in addition to considering the potential of novel machine learning methods in pursuing further automation.
Original languageEnglish
Title of host publicationArtificial Intelligence and Cyber Security in Industry 4.0
EditorsVelliangiri Sarveshwaran, Joy Iong-Zong Chen, Danilo Pelusi
PublisherSpringer, Singapore
Number of pages29
ISBN (Electronic)9789819921157
ISBN (Print)9789819921140, 9789819921171
Publication statusPublished - 14 Jun 2023

Publication series

NameAdvanced Technologies and Societal Change
ISSN (Print)2191-6853
ISSN (Electronic)2191-6861


  • Attack trees
  • Automotive cybersecurity
  • Attack tree generation
  • Threat modelling
  • Artificial Intelligence


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