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Published in:

Volume 8 Issue 1
January-2021
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2101276


Registration ID:
305471

Page Number

521-527

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Title

A Model to Detect Network Intrusion Using Machine Learning

Abstract

The attacks on computer security are becoming global and it is a very important security threats issues to the cyberspace, that if an organization is not mindful of, important data will be accessed, modified, or deleted. Computer security attackers utilize the compulsions and security weaknesses in the computer network and data system to carry out an attack, which ideals the divulgence of system information and the capture of the privacy of users and threaten data availability or integrity. The proposed system aim in developing a model to detect network intrusion using machine learning algorithm. The dataset consists of different categories of intrusions stimulated in a military environment. The dataset consists of a raw TCP/IP data for a network by stimulating a typical US Air Force Lan. The dataset is made up of 41 Columns. The class column consists of two types, which are Normal Network Packets and Anomalous Network Packets. The dataset was preprocessed by converting some features that was characters as values to 0s and 1s by using the pandas.get_dummies function. After that, we applied feature extraction by dropping and adding some features. The number of columns increased to 112 columns after performing feature extraction. The dataset was split into a train and a testing set using the train_test_split function. We use two machine learning algorithms in building/training our model. These machine learning algorithms are Random Forest Classifier and Support Vector Machine. After training/building our models, Random forest Classifier had the highest accuracy results which is about 99.78% while Support Vector Classifier had about 96.87%.

Key Words

Network Intrusion, Machine Learning, Support Vector Classifier, Random Forest Classifier.

Cite This Article

"A Model to Detect Network Intrusion Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 1, page no.521-527, January-2021, Available :http://www.jetir.org/papers/JETIR2101276.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"A Model to Detect Network Intrusion Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 1, page no. pp521-527, January-2021, Available at : http://www.jetir.org/papers/JETIR2101276.pdf

Publication Details

Published Paper ID: JETIR2101276
Registration ID: 305471
Published In: Volume 8 | Issue 1 | Year January-2021
DOI (Digital Object Identifier):
Page No: 521-527
Country: Bori, River State, Nigeria .
Area: Science & Technology
ISSN Number: 2349-5162
Publisher: IJ Publication


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