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

doi:10.1007/978-3-030-55307-4_27

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

Artificial Intelligence and Facial Recognition Research Group (GIIAR)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	Human Interaction, Emerging Technologies and Future Applications III - Proceedings of the 3rd International Conference on Human Interaction and Emerging Technologies
Subtitle of host publication	Future Applications, IHIET 2020
Editors	Tareq Ahram, Redha Taiar, Karine Langlois, Arnaud Choplin
Publisher	Springer
Pages	173-179
Number of pages	7
ISBN (Print)	9783030553067
DOIs	https://doi.org/10.1007/978-3-030-55307-4_27
State	Published - 2021
Event	3rd International Conference on Human Interaction and Emerging Technologies: Future Applications, IHIET 2020 - Paris, France Duration: 27 Aug 2020 → 29 Aug 2020

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1253 AISC
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	3rd International Conference on Human Interaction and Emerging Technologies: Future Applications, IHIET 2020
Country/Territory	France
City	Paris
Period	27/08/20 → 29/08/20

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

Access to Document

10.1007/978-3-030-55307-4_27

Cite this

Martinez, M. A. Q., Leon Rugel, D. T., Espinoza Alcivar, C. J., & Vazquez, M. Y. L. (2021). A framework for selecting classification models in the intruder detection system using topsis. In T. Ahram, R. Taiar, K. Langlois, & A. Choplin (Eds.), Human Interaction, Emerging Technologies and Future Applications III - Proceedings of the 3rd International Conference on Human Interaction and Emerging Technologies: Future Applications, IHIET 2020 (pp. 173-179). (Advances in Intelligent Systems and Computing; Vol. 1253 AISC). Springer. https://doi.org/10.1007/978-3-030-55307-4_27

Martinez, Miguel Angel Quiroz ; Leon Rugel, Deivid Temistocles ; Espinoza Alcivar, Carlos Jose ; Vazquez, Maikel Yelandi Leyva. / A framework for selecting classification models in the intruder detection system using topsis. Human Interaction, Emerging Technologies and Future Applications III - Proceedings of the 3rd International Conference on Human Interaction and Emerging Technologies: Future Applications, IHIET 2020. editor / Tareq Ahram ; Redha Taiar ; Karine Langlois ; Arnaud Choplin. Springer, 2021. pp. 173-179 (Advances in Intelligent Systems and Computing).

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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.",

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note = "Funding Information: Acknowledgments. This work has been supported by the GIIAR research group and the Salesian Polytechnic University of Guayaquil. Publisher Copyright: {\textcopyright} 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.; null ; Conference date: 27-08-2020 Through 29-08-2020",

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Martinez, MAQ, Leon Rugel, DT, Espinoza Alcivar, CJ & Vazquez, MYL 2021, A framework for selecting classification models in the intruder detection system using topsis. in T Ahram, R Taiar, K Langlois & A Choplin (eds), Human Interaction, Emerging Technologies and Future Applications III - Proceedings of the 3rd International Conference on Human Interaction and Emerging Technologies: Future Applications, IHIET 2020. Advances in Intelligent Systems and Computing, vol. 1253 AISC, Springer, pp. 173-179, 3rd International Conference on Human Interaction and Emerging Technologies: Future Applications, IHIET 2020, Paris, France, 27/08/20. https://doi.org/10.1007/978-3-030-55307-4_27

A framework for selecting classification models in the intruder detection system using topsis. / Martinez, Miguel Angel Quiroz; Leon Rugel, Deivid Temistocles ; Espinoza Alcivar, Carlos Jose ; Vazquez, Maikel Yelandi Leyva.

Human Interaction, Emerging Technologies and Future Applications III - Proceedings of the 3rd International Conference on Human Interaction and Emerging Technologies: Future Applications, IHIET 2020. ed. / Tareq Ahram; Redha Taiar; Karine Langlois; Arnaud Choplin. Springer, 2021. p. 173-179 (Advances in Intelligent Systems and Computing; Vol. 1253 AISC).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Martinez, Miguel Angel Quiroz

AU - Leon Rugel, Deivid Temistocles

AU - Espinoza Alcivar, Carlos Jose

AU - Vazquez, Maikel Yelandi Leyva

N1 - 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.

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

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Martinez MAQ, Leon Rugel DT, Espinoza Alcivar CJ, Vazquez MYL. A framework for selecting classification models in the intruder detection system using topsis. In Ahram T, Taiar R, Langlois K, Choplin A, editors, Human Interaction, Emerging Technologies and Future Applications III - Proceedings of the 3rd International Conference on Human Interaction and Emerging Technologies: Future Applications, IHIET 2020. Springer. 2021. p. 173-179. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-030-55307-4_27

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

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Keywords

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