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Discovering “unknown known” security requirements

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conference contribution
posted on 18.02.2016, 09:27 by Awais Rashid, Syed Asad Ali Naqvi, Rajiv Ramdhany, Matthew Edwards, Ruzanna Chitchyan, M. Ali Babar
Security is one of the biggest challenges facing organisations in the modern hyper-connected world. A number of theoretical security models are available that provide best practice security guidelines and are widely utilised as a basis to identify and operationalise security requirements. Such models often capture high-level security concepts (e.g., whitelisting, secure configurations, wireless access control, data recovery, etc.), strategies for operationalising such concepts through specific security controls, and relationships between the various concepts and controls. The threat landscape, however, evolves leading to new tacit knowledge that is embedded in or across a variety of security incidents. These unknown knowns alter, or at least demand reconsideration of the theoretical security models underpinning security requirements. In this paper, we present an approach to discover such unknown knowns through multi-incident analysis. The approach is based on a novel combination of grounded theory and incident fault trees. We demonstrate the effectiveness of the approach through its application to identify revisions to a theoretical security model widely used in industry.

History

Citation

ICSE '16 Proceedings of the 38th International Conference on Software Engineering, pp. 866-876

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Computer Science

Source

2016 IEEE/ACM 38th IEEE International Conference on Software Engineering (ICSE) , 14-22 May 2016, Austin, TX, USA

Version

AM (Accepted Manuscript)

Published in

ICSE '16 Proceedings of the 38th International Conference on Software Engineering

Publisher

Institute of Electrical and Electronics Engineers (IEEE), United States

isbn

978-1-4503-3900-1

Acceptance date

12/12/2015

Copyright date

2016

Available date

08/12/2016

Publisher version

http://dl.acm.org/citation.cfm?doid=2884781.2884785

Language

en

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