Análisis comparativo y evaluación de la aplicación de técnicas deep learning a datasets de ciberseguridad

Abstract

Cybersecurity has been highlighted in recent years to protect information systems. Various methods, techniques, and tools have been used to make the most of the existing vulnerabilities of these systems. Therefore, it is essential to develop and improve new technologies, as well as intrusion detection systems to detect possible threats. However, the use of these technologies requires highly qualified cybersecurity personnel to analyze the results and improve the accuracy of the results. Therefore, this generates the need to research and develop new cybersecurity systems with high performance to efficiently analyze and resolve such results. This research presents the application of machine learning techniques for real traffic classification, intending to identify possible attacks. The study has been carried out using machine learning tools applying deep learning algorithms such as multi-layer perceptron and long short term memory. Additionally, this paper presents a comparison between the results obtained by the application of the algorithms and non-deep learning algorithms such as random forest and decision tree. Finally, the results obtained show that the long short-term memory algorithm is the one that provides the best results in terms of accuracy and logarithmic loss.