Towards Effective Algorithms for Intelligent Defense Systems

  • Michael N. Johnstone
  • Andrew Woodward
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7672)


With the volume of data required to be analysed and interpreted by security analysts, the possibility of human error looms large and the consequences possibly harmful for some systems in the event of an adverse event not being detected. In this paper we suggest machine learning algorithms that can assist in supporting the security function effectively and present a framework that can be used to choose the best algorithm for a specific domain. A qualitative framework was produced, and it is suggested that a naive Bayesian classifier and artificial neural network based algorithms are most likely the best candidates for the proposed application. A testing framework is proposed to conduct a quantitative evaluation of the algorithms as the next step in the determination of best fit for purpose algorithm. Future research will look to repeat this process for cyber security specific applications, and also examine GPGPU optimisations.


Machine Learning Security Optimisation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Michael N. Johnstone
    • 1
  • Andrew Woodward
    • 1
  1. 1.School of Computer and Security ScienceSecurity Research Centre Edith Cowan University Perth Western AustraliaAustralia

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