A multi-objective genetic algorithm for minimising network security risk and cost
Issue Date
2012Subjects
countermeasure selection problemdecision making
genetic algorithm
IT security
risk optimisation
computer security
Metadata
Show full item recordAbstract
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 2012Type
Conference papers, meetings and proceedingsLanguage
enISBN
9781467323598ae974a485f413a2113503eed53cd6c53
10.1109/HPCSim.2012.6266959
Scopus Count
The following license files are associated with this item: