UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 6 Issue 5
May-2019
eISSN: 2349-5162

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

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Published Paper ID:
JETIRCW06030


Registration ID:
217031

Page Number

148-150

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Title

EVALUATION OF VARIOUS CLASSIFIERS FOR NSL-KDD DATASET USING MACHINE LEARNING APPROACH

Abstract

As the traffic on the network is increasing day by day due to intense use of internet, security is considered as the main issue. The network security procedures need an extreme attention to analyze the network traffic in an efficient manner. The various new evolving intrusions affect the network security adversely. There are number of security tools developed to prevent the network from the various types of intrusions but the rapid rise of intrusion activities is a concerned issue. An Intrusion detection system is used to analyze the network traffic for intrusions and classifies the network traffic as normal or an attack. Classification methods assist to design “Intrusion Detection Models” which can distinguish the normal network traffic and intrusive traffic. In this paper we are going to analyze the various classifiers that can be used to design the Intelligent Intrusion Detection Model using machine learning methodology. These classifiers have been evaluated using the popular and most effective Data Mining Tool that is called as WEKA (Waikato Environment for Knowledge Analysis) using all the attributes of the NSL-KDD Dataset. The experiments have been performed by taking the instances of Training and Testing Datasets. The statistical techniques have been used to identify the best classifier among all classifiers. Accuracy, True Positive Rate (TPR), Precision performance metrics have been considered for evaluating the best classification algorithms.

Key Words

Classifiers, Data Mining, NSL-KDD Dataset, WEKA, Machine Learning, statistical techniques, Intrusion Detection System, Performance Metrics.

Cite This Article

"EVALUATION OF VARIOUS CLASSIFIERS FOR NSL-KDD DATASET USING MACHINE LEARNING APPROACH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.148-150, May 2019, Available :http://www.jetir.org/papers/JETIRCW06030.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

"EVALUATION OF VARIOUS CLASSIFIERS FOR NSL-KDD DATASET USING MACHINE LEARNING APPROACH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp148-150, May 2019, Available at : http://www.jetir.org/papers/JETIRCW06030.pdf

Publication Details

Published Paper ID: JETIRCW06030
Registration ID: 217031
Published In: Volume 6 | Issue 5 | Year May-2019
DOI (Digital Object Identifier):
Page No: 148-150
Country: -, -, - .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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