Journal article
Artificial intelligence and machine learning in dynamic cyber risk analytics at the edge
- Abstract:
- We explore the potential and practical challenges in the use of artificial intelligence (AI) in cyber risk analytics, for improving organisational resilience and understanding cyber risk. The research is focused on identifying the role of AI in connected devices such as Internet of Things (IoT) devices. Through literature review, we identify wide ranging and creative methodologies for cyber analytics and explore the risks of deliberately influencing or disrupting behaviours to socio-technical systems. This resulted in the modelling of the connections and interdependencies between a system's edge components to both external and internal services and systems. We focus on proposals for models, infrastructures and frameworks of IoT systems found in both business reports and technical papers. We analyse this juxtaposition of related systems and technologies, in academic and industry papers published in the past 10 years. Then, we report the results of a qualitative empirical study that correlates the academic literature with key technological advances in connected devices. The work is based on grouping future and present techniques and presenting the results through a new conceptual framework. With the application of social science's grounded theory, the framework details a new process for a prototype of AI-enabled dynamic cyber risk analytics at the edge.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, 950.3KB, Terms of use)
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- Publisher copy:
- 10.1007/s42452-020-03559-4
Authors
- Publisher:
- Springer
- Journal:
- SN Applied Sciences More from this journal
- Volume:
- 2
- Issue:
- 11
- Article number:
- 1773
- Publication date:
- 2020-10-06
- Acceptance date:
- 2020-09-21
- DOI:
- EISSN:
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2523-3971
- ISSN:
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2523-3963
- Language:
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English
- Keywords:
- Pubs id:
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1136596
- Local pid:
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pubs:1136596
- Deposit date:
-
2020-10-12
Terms of use
- Copyright holder:
- Radanliev et al.
- Copyright date:
- 2020
- Rights statement:
- ©2020 The Author(s).
- Notes:
- Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
- Licence:
- CC Attribution (CC BY)
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