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A multi-objective genetic algorithm for minimising network security risk and cost

  • Valentina Viduto
    ,
  • Carsten Maple
    ,
  • Wei Huang
    ,
  • Alexey Bochenkov
Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Open access

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.

Publication Information

Output type

Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Original language

English

Publication milestones

  • Published - 16/08/2012

Publication status

Published - 16/08/2012

Publisher

Institute of Electrical and Electronics Engineers Inc., United States

ISBN (Print)

9781467323598

ISBN (Electronic)

9781467323598

Publication IDs

  • handle.net: 10547/270778
  • Scopus: 84867018903

Host publication title

nan

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