Modelling Social-Technical Attacks with Timed Automata

Nicolas David, Alexandre David, Rene Rydhof Hansen, Kim G. Larsen, Axel Legay, Mads Christian Olesen, Christian W. Probst

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


Attacks on a system often exploit vulnerabilities that arise from human behaviour or other human activity. Attacks of this type, so-called socio-technical attacks, cover everything from social engineering to insider attacks, and they can have a devastating impact on an unprepared organisation. In this paper we develop an approach towards modelling socio-technical systems in general and socio-technical attacks in particular, using timed automata and illustrate its application by a complex case study. Thanks to automated model checking and automata theory, we can automatically generate possible attacks in our model and perform analysis and simulation of both model and attack, revealing details about the specific interaction between attacker and victim. Using timed automata also allows for intuitive modelling of systems, in which quantities like time and cost can be easily added and analysed.
Original languageEnglish
Title of host publicationProceedings of the 7th ACM CCS International Workshop on Managing Insider Security Threats (ACM MIST '15)
PublisherAssociation for Computing Machinery
Publication date2015
ISBN (Print)978-1-4503-3824-0
Publication statusPublished - 2015
Event7th ACM CCS International Workshop on Managing Insider Security Threats (MIST '15) - The Denver Marriot City Center, Denver, Colorado, United States
Duration: 12 Oct 201516 Oct 2015
Conference number: 7


Workshop7th ACM CCS International Workshop on Managing Insider Security Threats (MIST '15)
LocationThe Denver Marriot City Center
Country/TerritoryUnited States
CityDenver, Colorado
OtherIn Conjunction with the 22nd ACM Conference on Computer and Communications Security (ACM CCS 2015)
Internet address


  • Insider threats
  • Timed automata
  • Attack trees
  • Attack generation


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