Review: machine learning techniques applied to cybersecurity

• Published in: International journal of machine learning and cybernetics Vol. 10; no. 10; pp. 2823 - 2836
• Main Authors: Martínez Torres, Javier, Iglesias Comesaña, Carla, García-Nieto, Paulino J.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2019; Springer Nature B.V
• 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; Estonia; Internet threats; Cybersecurity; Security; Detection systems; Machine learning
• ISSN: 1868-8071; 1868-808X
• DOI: 10.1007/s13042-018-00906-1

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.