A framework for selecting classification models in the intruder detection system using topsis

Miguel Angel Quiroz Martinez, Deivid Temistocles Leon Rugel, Carlos Jose Espinoza Alcivar, Maikel Yelandi Leyva Vazquez

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

As the network has expanded considerably, security mechanisms are a key issue in networks. Intrusive activities, such as unauthorized access and data manipulation, are increasing. Therefore, the role of the Network Intrusion Detection System (NIDS) in monitoring network traffic for activity and determining whether an intrusion has occurred is very important. The performance of an IDS depends on the selection of the classification model and training data, however, many classifiers generate similar results when measuring performance. The technique of order of preference for similarity to the ideal solution (TOPSIS) is used to select one or more alternatives based on the criteria. The main objective is to present some classification models used in a data set to select the best alternative according to the performance criteria using the TOPSIS method. The deductive method and selection research technique were applied to study the NSL-KDD.

Original languageEnglish
Title of host publicationHuman Interaction, Emerging Technologies and Future Applications III - Proceedings of the 3rd International Conference on Human Interaction and Emerging Technologies
Subtitle of host publicationFuture Applications, IHIET 2020
EditorsTareq Ahram, Redha Taiar, Karine Langlois, Arnaud Choplin
PublisherSpringer
Pages173-179
Number of pages7
ISBN (Print)9783030553067
DOIs
StatePublished - 2021
Event3rd International Conference on Human Interaction and Emerging Technologies: Future Applications, IHIET 2020 - Paris, France
Duration: 27 Aug 202029 Aug 2020

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1253 AISC
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

Conference3rd International Conference on Human Interaction and Emerging Technologies: Future Applications, IHIET 2020
Country/TerritoryFrance
CityParis
Period27/08/2029/08/20

Bibliographical note

Funding Information:
Acknowledgments. This work has been supported by the GIIAR research group and the Salesian Polytechnic University of Guayaquil.

Publisher Copyright:
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • Intrusion Detection System (IDS)
  • Machine learning
  • NSL-KDD
  • TOPSIS

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