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Intruder Detection Using Fuzzy Min-Max Neural Network and A Principal Component Analysis (PCA) in Network Data

A. F. Aldubai1 , V. H. Humbe2 , S.S. Chowhan3 , Y.F. Aldubai4

  1. Department of Computational Sciences and Technology, S.R.T.M University, Nanded, India.
  2. School of Technology, Sub-Center Latur S.R.T.M University, latur, India.
  3. Department of Computational Sciences and Technology, S.R.T.M University, Nanded , India.
  4. Faculty of Administrative Sciences, Albaydaa University, Radaa , Yemen.

Correspondence should be addressed to: Ahmed_Aldubai86@yahoo.com.

Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-12 , Page no. 109-117, Dec-2017

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v5i12.109117

Online published on Dec 31, 2017

Copyright © A. F. Aldubai, V. H. Humbe, S.S. Chowhan, Y.F. Aldubai . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: A. F. Aldubai, V. H. Humbe, S.S. Chowhan, Y.F. Aldubai, “Intruder Detection Using Fuzzy Min-Max Neural Network and A Principal Component Analysis (PCA) in Network Data,” International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.109-117, 2017.

MLA Style Citation: A. F. Aldubai, V. H. Humbe, S.S. Chowhan, Y.F. Aldubai "Intruder Detection Using Fuzzy Min-Max Neural Network and A Principal Component Analysis (PCA) in Network Data." International Journal of Computer Sciences and Engineering 5.12 (2017): 109-117.

APA Style Citation: A. F. Aldubai, V. H. Humbe, S.S. Chowhan, Y.F. Aldubai, (2017). Intruder Detection Using Fuzzy Min-Max Neural Network and A Principal Component Analysis (PCA) in Network Data. International Journal of Computer Sciences and Engineering, 5(12), 109-117.

BibTex Style Citation:
@article{Aldubai_2017,
author = {A. F. Aldubai, V. H. Humbe, S.S. Chowhan, Y.F. Aldubai},
title = {Intruder Detection Using Fuzzy Min-Max Neural Network and A Principal Component Analysis (PCA) in Network Data},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {12 2017},
volume = {5},
Issue = {12},
month = {12},
year = {2017},
issn = {2347-2693},
pages = {109-117},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1588},
doi = {https://doi.org/10.26438/ijcse/v5i12.109117}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v5i12.109117}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1588
TI - Intruder Detection Using Fuzzy Min-Max Neural Network and A Principal Component Analysis (PCA) in Network Data
T2 - International Journal of Computer Sciences and Engineering
AU - A. F. Aldubai, V. H. Humbe, S.S. Chowhan, Y.F. Aldubai
PY - 2017
DA - 2017/12/31
PB - IJCSE, Indore, INDIA
SP - 109-117
IS - 12
VL - 5
SN - 2347-2693
ER -

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Abstract

To improve classification performance and prediction time of (IDS) system when a few number of large hyperboxes are formed in the network. We have proposed FMM NN and PCA features extraction algorithms. The process of a system consisting of the pre-processing dataset based on interquartile ranges filter and features extraction. Followed by the Fuzzy min-max neural network (FMM NN), which define as supervised learning classifier, that utilizes a fuzzy set hyberbox as pattern classes for learning and classification. Each hyberbox consists of min and mix points of opposite corners of hyberbox with corresponding to membership function. Two real-time and faster streaming datasets (KDD99 and NSL-KDD) are used to empirically evaluate the effectiveness of the proposed FMM NN system. The results are analyzed and compared with others existing systems and published results.

Key-Words / Index Term

Intrusion Detection, Fuzzy min-max neural network, Principal Component Analysis (PCA), Machine Learning

References

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