Towards Effective Algorithms for Intelligent Defense Systems
Faculty of Computing, Health and Science
ECU Security Research Institute
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.
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