Ensemble SVM classifiers based on PCA and LDA for IDS

Abdulla Amin Aburomman, Md. Mamun Ibne Reaz

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

2 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 and Principle Component Analysis feature extraction algorithms is implemented. The experiments demonstrate that the ensemble method outperforms single feature extraction method with 92% in overall accuracy.

Original languageEnglish
Title of host publication2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages95-99
Number of pages5
ISBN (Electronic)9781509028894
DOIs
Publication statusPublished - 27 Mar 2017
Event2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016 - Putrajaya, Malaysia
Duration: 14 Nov 201616 Nov 2016

Other

Other2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016
CountryMalaysia
CityPutrajaya
Period14/11/1616/11/16

Fingerprint

classifiers
pattern recognition
Feature extraction
Classifiers
Intrusion detection
Discriminant analysis
Experiments

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Biomedical Engineering
  • Control and Systems Engineering
  • Hardware and Architecture
  • Computer Networks and Communications
  • Instrumentation

Cite this

Aburomman, A. A., & Ibne Reaz, M. M. (2017). Ensemble SVM classifiers based on PCA and LDA for IDS. In 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016 (pp. 95-99). [7888016] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICAEES.2016.7888016

Ensemble SVM classifiers based on PCA and LDA for IDS. / Aburomman, Abdulla Amin; Ibne Reaz, Md. Mamun.

2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 95-99 7888016.

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

Aburomman, AA & Ibne Reaz, MM 2017, Ensemble SVM classifiers based on PCA and LDA for IDS. in 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016., 7888016, Institute of Electrical and Electronics Engineers Inc., pp. 95-99, 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016, Putrajaya, Malaysia, 14/11/16. https://doi.org/10.1109/ICAEES.2016.7888016
Aburomman AA, Ibne Reaz MM. Ensemble SVM classifiers based on PCA and LDA for IDS. In 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 95-99. 7888016 https://doi.org/10.1109/ICAEES.2016.7888016
Aburomman, Abdulla Amin ; Ibne Reaz, Md. Mamun. / Ensemble SVM classifiers based on PCA and LDA for IDS. 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 95-99
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