Abstract
This study focuses on the preprocessing of information for the selection of the most significant characteristics of a network traffic database, recovered from an Ecuadorian institution, using a method of classifying optimal entities and attributes, with the In order to achieve a complete understanding of its real composition to be able to generate patterns and identification of trends of behavior in the network, both of patterns that deviate from normal traffic behavior (intrusive), as well as normal, to detect with high precision possible attacks. Network management tools were used as a multifunctional security server software, as well as pre-processing of data tools for the selection of attributes, as well as the elimination of noise from the instances of the database, It allowed to identify which ins- tances and attributes are correct and contribute with effective information in the study. Among them we have: Greedy Stepwise Algorithm (Algoritmo Voráz), K-Means Algorithm, Discrete Chi-square Attributes and the use of computational models as Evolutionary Neural Networks and Gene Algorithms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Yin, C., Zhu, Y., Fei, J., He, X.: A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access 5, 21954–21961 (2017)
Reddy, R.R., Ramadevi, Y., Sunitha, K.V.N.: Effective discriminant function for intrusion detection using SVM. In: 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1148–1153 (2016)
Modi, C.N., Acha, K.: Virtualization layer security challenges and intrusion detection/prevention systems in cloud computing: a comprehensive review. J. Supercomput. 73(3), 1192–1234 (2017)
Farnaaz, N., Jabbar, M.: Random Forest Modeling for Network Intrusion Detection System (2016)
Zarkami, R., Moradi, M., Pasvisheh, R.S., Bani, A., Abbasi, K.: Input variable selection with greedy stepwise search algorithm for analysing the probability of fish occurrence: a case study for Alburnoides mossulensis in the Gamasiab River, Iran. Ecol. Eng. 118, 104–110 (2018)
Martínez-Estudillo, F.J., Hervás-Martínez, C., Gutiérrez, P.A., Martínez-Estudillo, A.C.: Evolutionary product-unit neural networks classifiers. Neurocomputing 72(1–3), 548–561 (2008)
Yao, X.: Evolving artificial neural networks (1999)
Noguera, J., Portillo, N., Hernandez, L.: Redes Neuronales, Bioinspiración para el Desarrollo de la Ingeniería. Ingeniare 17, 117 (2014)
García Vallejo, C.A.: Selección de Instancias y Atributos en Conjuntos de Datos mediante Algoritmos sobre Grafos (2012)
Morring, B.D., Martinez, T.R.: A nearest neighbor data reduction algorithm (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Hidalgo-Guijarro, J. et al. (2020). Preprocessing Information from a Data Network for the Detection of User Behavior Patterns. In: Ahram, T., Karwowski, W., Pickl, S., Taiar, R. (eds) Human Systems Engineering and Design II. IHSED 2019. Advances in Intelligent Systems and Computing, vol 1026. Springer, Cham. https://doi.org/10.1007/978-3-030-27928-8_101
Download citation
DOI: https://doi.org/10.1007/978-3-030-27928-8_101
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27927-1
Online ISBN: 978-3-030-27928-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)