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Addressing Challenges in Big Data Intrusion Detection System using Machine Learning Techniques

Saqr Mohammed H. Almansob1 , Santosh Shivajirao. Lomte2

  1. Department of Computer Science, Radhai Mahavidyalaya (BAM University), Aurangabad, India.
  2. School of Engineering Technology, VDF, Latur, India.

Correspondence should be addressed to: saqrmohammed2014@gmail.com.

Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-11 , Page no. 127-130, Nov-2017

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v5i11.127130

Online published on Nov 30, 2017

Copyright © Saqr Mohammed H. Almansob, Santosh Shivajirao. Lomte . 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: Saqr Mohammed H. Almansob, Santosh Shivajirao. Lomte, “Addressing Challenges in Big Data Intrusion Detection System using Machine Learning Techniques,” International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.127-130, 2017.

MLA Style Citation: Saqr Mohammed H. Almansob, Santosh Shivajirao. Lomte "Addressing Challenges in Big Data Intrusion Detection System using Machine Learning Techniques." International Journal of Computer Sciences and Engineering 5.11 (2017): 127-130.

APA Style Citation: Saqr Mohammed H. Almansob, Santosh Shivajirao. Lomte, (2017). Addressing Challenges in Big Data Intrusion Detection System using Machine Learning Techniques. International Journal of Computer Sciences and Engineering, 5(11), 127-130.

BibTex Style Citation:
@article{Almansob_2017,
author = {Saqr Mohammed H. Almansob, Santosh Shivajirao. Lomte},
title = {Addressing Challenges in Big Data Intrusion Detection System using Machine Learning Techniques},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {11 2017},
volume = {5},
Issue = {11},
month = {11},
year = {2017},
issn = {2347-2693},
pages = {127-130},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1553},
doi = {https://doi.org/10.26438/ijcse/v5i11.127130}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v5i11.127130}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1553
TI - Addressing Challenges in Big Data Intrusion Detection System using Machine Learning Techniques
T2 - International Journal of Computer Sciences and Engineering
AU - Saqr Mohammed H. Almansob, Santosh Shivajirao. Lomte
PY - 2017
DA - 2017/11/30
PB - IJCSE, Indore, INDIA
SP - 127-130
IS - 11
VL - 5
SN - 2347-2693
ER -

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Abstract

In the last few years, the number of people around the world is increasing day by day in matching the use of the internet and social media. For this reason, a large volume of data is generated by the internet and social media from gigabytes (GB) to petabytes (PB) with high speed. In this work, it is proposed Intrusion Detection System (IDS) with large amounts of data to address challenges in various types of network attacks using machine learning techniques. On another hand, it is proposed Principal Components Analysis method to reduce high dimensionality and features of data. Therefore, in order to reduce amounts of calculations and improve an accuracy of classification of data. That is, why the use of DARBAI data set in this model and it is applied to K-nearest neighbour method for classification.

Key-Words / Index Term

Big data; Intrusion Detection System (IDS), Principal Component Analysis (PCA), K-Nearest neighbour (KNN)

References

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