Ensemble of binary SVM classifiers based on PCA and LDA feature extraction for intrusion detection

Abdulla Amin Aburomman, Md. Mamun Ibne Reaz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

Feature extraction addresses the problem of finding the most compact and informative set of features. To maximize the effectiveness of each single feature extraction algorithm and to develop an efficient intrusion detection system, an ensemble of Linear Discriminant Analysis (LDA) and Principle Component Analysis (PCA) feature extraction algorithms is implemented. This ensemble PCA-LDA method has led to good results and showed a greater proportion of precision in comparison to a single feature extraction method.

Original languageEnglish
Title of host publicationProceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages636-640
Number of pages5
ISBN (Electronic)9781467396127
DOIs
Publication statusPublished - 28 Feb 2017
Event2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016 - Xi'an, China
Duration: 3 Oct 20165 Oct 2016

Other

Other2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016
CountryChina
CityXi'an
Period3/10/165/10/16

Fingerprint

Intrusion detection
Discriminant analysis
Feature extraction
Classifiers
Classifier

Keywords

  • Ensemble
  • Feature extraction
  • Intrusion detection
  • Kdd99
  • LDA
  • PCA
  • SVM
  • Weighted Majority Voting (WMV)

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management
  • Control and Systems Engineering

Cite this

Aburomman, A. A., & Ibne Reaz, M. M. (2017). Ensemble of binary SVM classifiers based on PCA and LDA feature extraction for intrusion detection. In Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016 (pp. 636-640). [7867287] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IMCEC.2016.7867287

Ensemble of binary SVM classifiers based on PCA and LDA feature extraction for intrusion detection. / Aburomman, Abdulla Amin; Ibne Reaz, Md. Mamun.

Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 636-640 7867287.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Aburomman, AA & Ibne Reaz, MM 2017, Ensemble of binary SVM classifiers based on PCA and LDA feature extraction for intrusion detection. in Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016., 7867287, Institute of Electrical and Electronics Engineers Inc., pp. 636-640, 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016, Xi'an, China, 3/10/16. https://doi.org/10.1109/IMCEC.2016.7867287
Aburomman AA, Ibne Reaz MM. Ensemble of binary SVM classifiers based on PCA and LDA feature extraction for intrusion detection. In Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 636-640. 7867287 https://doi.org/10.1109/IMCEC.2016.7867287
Aburomman, Abdulla Amin ; Ibne Reaz, Md. Mamun. / Ensemble of binary SVM classifiers based on PCA and LDA feature extraction for intrusion detection. Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 636-640
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