2.50
Hdl Handle:
http://hdl.handle.net/10547/270778
Title:
A multi-objective genetic algorithm for minimising network security risk and cost
Authors:
Viduto, Valentina; Maple, Carsten; Huang, Wei; Bochenkov, Alexey
Abstract:
Security countermeasures help ensure information security: confidentiality, integrity and availability(CIA), by mitigating possible risks associated with the security event. Due to the fact, that it is often difficult to measure such an impact quantitatively, it is also difficult to deploy appropriate security countermeasures. In this paper, we demonstrate a model of quantitative risk analysis, where an optimisation routine is developed to help a human decision maker to determine the preferred trade-off between investment cost and resulting risk. An offline optimisation routine deploys a genetic algorithm to search for the best countermeasure combination, while multiple risk factors are considered. We conduct an experimentation with real world data, taken from the PTA(Practical Threat Analysis) case study to show that our method is capable of delivering solutions for real world problem data sets. The results show that the multi-objective genetic algorithm (MOGA) approach provides high quality solutions, resulting in better knowledge for decision making.
Citation:
Viduto, V., Maple, C., Huang. W. and Bochenkov, A. (2012) "A multi-objective genetic algorithm for minimising network security risk and cost," High Performance Computing and Simulation (HPCS), 2012 International Conference on , pp.462-467, 2-6 July 2012
Publisher:
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date:
2012
URI:
http://hdl.handle.net/10547/270778
DOI:
10.1109/HPCSim.2012.6266959
Additional Links:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6266959
Type:
Conference papers, meetings and proceedings
Language:
en
ISBN:
9781467323598
Appears in Collections:
Centre for Research in Distributed Technologies (CREDIT)

Full metadata record

DC FieldValue Language
dc.contributor.authorViduto, Valentinaen_GB
dc.contributor.authorMaple, Carstenen_GB
dc.contributor.authorHuang, Weien_GB
dc.contributor.authorBochenkov, Alexeyen_GB
dc.date.accessioned2013-03-01T10:33:41Z-
dc.date.available2013-03-01T10:33:41Z-
dc.date.issued2012-
dc.identifier.citationViduto, V., Maple, C., Huang. W. and Bochenkov, A. (2012) "A multi-objective genetic algorithm for minimising network security risk and cost," High Performance Computing and Simulation (HPCS), 2012 International Conference on , pp.462-467, 2-6 July 2012en_GB
dc.identifier.isbn9781467323598-
dc.identifier.doi10.1109/HPCSim.2012.6266959-
dc.identifier.urihttp://hdl.handle.net/10547/270778-
dc.description.abstractSecurity countermeasures help ensure information security: confidentiality, integrity and availability(CIA), by mitigating possible risks associated with the security event. Due to the fact, that it is often difficult to measure such an impact quantitatively, it is also difficult to deploy appropriate security countermeasures. In this paper, we demonstrate a model of quantitative risk analysis, where an optimisation routine is developed to help a human decision maker to determine the preferred trade-off between investment cost and resulting risk. An offline optimisation routine deploys a genetic algorithm to search for the best countermeasure combination, while multiple risk factors are considered. We conduct an experimentation with real world data, taken from the PTA(Practical Threat Analysis) case study to show that our method is capable of delivering solutions for real world problem data sets. The results show that the multi-objective genetic algorithm (MOGA) approach provides high quality solutions, resulting in better knowledge for decision making.en_GB
dc.language.isoenen
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCen_GB
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6266959en_GB
dc.subjectcountermeasure selection problemen_GB
dc.subjectdecision makingen_GB
dc.subjectgenetic algorithmen_GB
dc.subjectIT securityen_GB
dc.subjectrisk optimisationen_GB
dc.subjectcomputer securityen_GB
dc.titleA multi-objective genetic algorithm for minimising network security risk and costen
dc.typeConference papers, meetings and proceedingsen
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