Sistema de Detecção de Intrusão baseado em fluxos de redes usando a métodos de aprendizado de máquina (Intrusion Detection System Based On Flows Using Machine Learning Algorithms)

Eduardo Massato Kakihata (eduardomassato@hotmail.com)1, Helton Molina Sapia (helton@unoeste.edu.br)1, Ronaldo Toshiaki Oiakawa (oikawa@unoeste.br)1, Danillo Roberto Pereira (danilopereira@unoeste.br)1, João Paulo Papa (papa@fc.unesp.br)2, Victor Hugo Costa de Albuquerque (victor.albuquerque@unifor.br)3, Francisco Assis da Silva (chico@unoeste.br)1


1Universidade do Oeste Paulista (Unoeste)
2Universidade Estadual Paulista (Unesp)
3Universidade de Fortaleza (Unifor)

This paper appears in: Revista IEEE América Latina

Publication Date: Oct. 2017
Volume: 15,   Issue: 10 
ISSN: 1548-0992


Abstract:
The use of technology information and communication by different types of devices generates a large quantity of data packets that contains of confidential and personal information. The traffic of data packet can be summarized in network flow. Due this reason, it is necessary to use computer security tools, such as Intrusion Detection Systems (IDS). This work presents an IDS that can perform the flow- based analysis (netflow). This research conducted an analysis on flows previously collected and properly detected of three different types of attacks. The flows were organized to be processed by machine learning methods. The results obtained by proposed approach were very promising. Also, this work aimed at building a public dataset to be used by researchers worldwide in order to foster IDS-related research.

Index Terms:
Intrusion Detection System, Netflow, Machine Learning, OPF, SVM, KNN, Bayes Classifier   


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