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Review: machine learning techniques applied to cybersecurity

  • Javier Martínez Torres
  • Carla Iglesias Comesaña
  • Paulino J. García-Nieto
Original Article
  • 75 Downloads

Abstract

Machine learning techniques are a set of mathematical models to solve high non-linearity problems of different topics: prediction, classification, data association, data conceptualization. In this work, the authors review the applications of machine learning techniques in the field of cybersecurity describing before the different classifications of the models based on (1) their structure, network-based or not, (2) their learning process, supervised or unsupervised and (3) their complexity. All the capabilities of machine learning techniques are to be regarded, but authors focus on prediction and classification, highlighting the possibilities of improving the models in order to minimize the error rates in the applications developed and available in the literature. This work presents the importance of different error criteria as the confusion matrix or mean absolute error in classification problems, and relative error in regression problems. Furthermore, special attention is paid to the application of the models in this review work. There are a wide variety of possibilities, applying these models to intrusion detection, or to detection and classification of attacks, to name a few. However, other important and innovative applications in the field of cybersecurity are presented. This work should serve as a guide for new researchers and those who want to immerse themselves in the field of machine learning techniques within cybersecurity.

Keywords

Cybersecurity Detection systems Internet threats Machine learning Security 

Notes

Acknowledgements

C. Iglesias acknowledges the support of the Spanish Ministry of Education, Culture and Sport for FPU Grant number 12/02283. J. Martinez acknowledges the support of the Spanish Ministry of Education for Grant project ID TIN2016-76770-R.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Universidad Internacional de la RiojaLogroñoSpain
  2. 2.University of VigoVigoSpain
  3. 3.University of OviedoOviedoSpain

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