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

Grupo de Investigación en Inteligencia Artificial y Reconocimiento Facial (GIIAR)

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

13 Citas (Scopus)

Resumen

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.

Idioma original	Inglés
Título de la publicación alojada	Human Interaction, Emerging Technologies and Future Applications III - Proceedings of the 3rd International Conference on Human Interaction and Emerging Technologies
Subtítulo de la publicación alojada	Future Applications, IHIET 2020
Editores	Tareq Ahram, Redha Taiar, Karine Langlois, Arnaud Choplin
Editorial	Springer
Páginas	173-179
Número de páginas	7
ISBN (versión impresa)	9783030553067
DOI	https://doi.org/10.1007/978-3-030-55307-4_27
Estado	Publicada - 2021
Evento	3rd International Conference on Human Interaction and Emerging Technologies: Future Applications, IHIET 2020 - Paris, Francia Duración: 27 ago 2020 → 29 ago 2020

Serie de la publicación

Nombre	Advances in Intelligent Systems and Computing
Volumen	1253 AISC
ISSN (versión impresa)	2194-5357
ISSN (versión digital)	2194-5365

Conferencia

Conferencia	3rd International Conference on Human Interaction and Emerging Technologies: Future Applications, IHIET 2020
País/Territorio	Francia
Ciudad	Paris
Período	27/08/20 → 29/08/20

Nota bibliográfica

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.

Areas de Conocimiento del CACES

316A Desarrollo y análisis de software y aplicaciones

Acceder al documento

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

Otros archivos y enlaces

Enlace a la publicación en Scopus

Citar esto

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. En 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 et al. / 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).

@inproceedings{9bb13fe4978f42169155201f20cd89e1,

title = "A framework for selecting classification models in the intruder detection system using topsis",

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.",

keywords = "Intrusion Detection System (IDS), Machine learning, NSL-KDD, TOPSIS",

author = "Martinez, \{Miguel Angel Quiroz\} and \{Leon Rugel\}, \{Deivid Temistocles\} and \{Espinoza Alcivar\}, \{Carlos Jose\} and Vazquez, \{Maikel Yelandi Leyva\}",

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.; 3rd International Conference on Human Interaction and Emerging Technologies: Future Applications, IHIET 2020 ; Conference date: 27-08-2020 Through 29-08-2020",

year = "2021",

doi = "10.1007/978-3-030-55307-4\_27",

language = "English",

isbn = "9783030553067",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer",

pages = "173--179",

editor = "Tareq Ahram and Redha Taiar and Karine Langlois and Arnaud Choplin",

booktitle = "Human Interaction, Emerging Technologies and Future Applications III - Proceedings of the 3rd International Conference on Human Interaction and Emerging Technologies",

}

Martinez, MAQ, Leon Rugel, DT, Espinoza Alcivar, CJ & Vazquez, MYL 2021, A framework for selecting classification models in the intruder detection system using topsis. En 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, Francia, 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 et al.
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).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

TY - GEN

T1 - A framework for selecting classification models in the intruder detection system using topsis

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.

KW - Intrusion Detection System (IDS)

KW - Machine learning

KW - NSL-KDD

KW - TOPSIS

UR - https://www.scopus.com/pages/publications/85089628131

U2 - 10.1007/978-3-030-55307-4_27

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

M3 - Conference contribution

AN - SCOPUS:85089628131

SN - 9783030553067

T3 - Advances in Intelligent Systems and Computing

SP - 173

EP - 179

BT - Human Interaction, Emerging Technologies and Future Applications III - Proceedings of the 3rd International Conference on Human Interaction and Emerging Technologies

A2 - Ahram, Tareq

A2 - Taiar, Redha

A2 - Langlois, Karine

A2 - Choplin, Arnaud

PB - Springer

T2 - 3rd International Conference on Human Interaction and Emerging Technologies: Future Applications, IHIET 2020

Y2 - 27 August 2020 through 29 August 2020

ER -

Martinez MAQ, Leon Rugel DT, Espinoza Alcivar CJ, Vazquez MYL. A framework for selecting classification models in the intruder detection system using topsis. En Ahram T, Taiar R, Langlois K, Choplin A, editores, 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). doi: 10.1007/978-3-030-55307-4_27