Attack tree analysis for insider threats on the IoT using Isabelle

Kammueller, Florian ORCID logoORCID:, Nurse, Jason R. C. and Probst, Christian (2016) Attack tree analysis for insider threats on the IoT using Isabelle. Human Aspects of Information Security, Privacy, and Trust: 4th International Conference, HAS 2016, Held as Part of HCI International 2016, Toronto, ON, Canada, July 17-22, 2016, Proceedings. In: 4th International Conference on Human Aspects of Security, Privacy and Trust, HCII-HAS 2016, 17-24 Jul 2016, Toronto, ON, Canada. ISBN 9783319393803. ISSN 0302-9743 [Conference or Workshop Item] (doi:10.1007/978-3-319-39381-0_21)

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The Internet-of-Things (IoT) aims at integrating small devices around humans. The threat from human insiders in “regular” organisations is real; in a fully-connected world of the IoT, organisations face a substantially more severe security challenge due to unexpected access possibilities and information flow. In this paper, we seek to illustrate and classify insider threats in relation to the IoT (by ‘smart insiders’), exhibiting attack vectors for their characterisation. To model the attacks we apply a method of formal modelling of Insider Threats in the interactive theorem prover Isabelle. On the classified IoT attack examples, we show how this logical approach can be used to make the models more precise and to analyse the previously identified Insider IoT attacks using Isabelle attack trees

Item Type: Conference or Workshop Item (Paper)
Additional Information: Paper published as:
Kammüller F., Nurse J.R.C., Probst C.W. (2016) Attack Tree Analysis for Insider Threats on the IoT Using Isabelle. In: Tryfonas T. (eds) Human Aspects of Information Security, Privacy, and Trust. HAS 2016. Lecture Notes in Computer Science, vol 9750. Springer, Cham
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 19714
Notes on copyright: The final publication is available at Springer via
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Depositing User: Florian Kammueller
Date Deposited: 04 May 2016 14:12
Last Modified: 29 Nov 2022 21:48

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