Review: machine learning techniques applied to cybersecurity

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...

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Bibliographic Details
Published in:International journal of machine learning and cybernetics 2019-10, Vol.10 (10), p.2823-2836
Main Authors: Martínez Torres, Javier, Iglesias Comesaña, Carla, García-Nieto, Paulino J.
Format: Article
Language:English
Subjects:
Access control
Artificial Intelligence
Bibliographic literature
Classification
Complex Systems
Computational Intelligence
Confidentiality
Control
Cybercrime
Cybersecurity
Data encryption
Decision trees
Engineering
Errors
Identity theft
Internet
Machine learning
Malware
Mathematical analysis
Mathematical models
Mechatronics
Neural networks
Original Article
Pattern Recognition
Personal information
Robotics
Software
Support vector machines
Systems Biology
User training
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Summary: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.
ISSN:1868-8071
1868-808X
DOI:10.1007/s13042-018-00906-1