Open AccessCOMPARATIVE ANALYSIS AND EVALUATION OF THE APPLICATION OF DEEP LEARNING TECHNIQUES TO CYBERSECURITY DATASETS
Author(s) -
Xavier Larriva-Novo,
Mario Vega-Barbas,
Víctor A. Villagrá,
Julio Berrocal
Publication year - 2021
Publication title -
dyna
Language(s) - English
Resource type - Journals
eISSN - 1989-1490
pISSN - 0012-7361
DOI - 10.6036/10007
Subject(s) - computer science , intrusion detection system , perceptron , artificial intelligence , machine learning , false positive paradox , deep learning , decision tree , data mining , artificial neural network , computer security
Cybersecurity has stood out in recent years with the aim of protecting information systems. Different methods, techniques and tools have been used to make the most of the existing vulnerabilities in these systems. Therefore, it is essential to develop and improve new technologies, as well as intrusion detection systems that allow detecting possible threats. However, the use of these technologies requires highly qualified cybersecurity personnel to analyze the results and reduce the large number of false positives that these technologies presents in their results. Therefore, this generates the need to research and develop new high-performance cybersecurity systems that allow efficient analysis and resolution of these results. This research presents the application of machine learning techniques to classify real traffic, in order 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 document presents a comparison between the results obtained by applying the aforementioned algorithms and algorithms that are not deep learning, such as: random forest and decision tree. Finally, the results obtained are presented, showing that the long-short-term-memory algorithm is the one that provides the best results in relation to precision and logarithmic loss.