Classification of UNSW-NB15 dataset using Exploratory Data Analysis using Ensemble Learning

Recent advancements in machine learning have made it a tool of choice for different classification and analytical problems. This paper deals with a critical field of computer networking: network security and the possibilities of machine learning automation in this field. We will be do...

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Autores principales:	Neha Sharma, Narendra Yadav, Saurabh Sharma
Formato:	article
Lenguaje:	EN
Publicado:	European Alliance for Innovation (EAI) 2021
Materias:	kdd’99 unsw-nb15 ensemble algorithms xgboost adaboost random forest extra trees Computer engineering. Computer hardware TK7885-7895 Systems engineering TA168
Acceso en línea:	https://doaj.org/article/ef8cef05f94f49d7b5cac04900ce8bdc
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id	oai:doaj.org-article:ef8cef05f94f49d7b5cac04900ce8bdc
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spelling	oai:doaj.org-article:ef8cef05f94f49d7b5cac04900ce8bdc2021-11-30T11:07:41ZClassification of UNSW-NB15 dataset using Exploratory Data Analysis using Ensemble Learning2410-021810.4108/eai.13-10-2021.171319https://doaj.org/article/ef8cef05f94f49d7b5cac04900ce8bdc2021-11-01T00:00:00Zhttps://eudl.eu/pdf/10.4108/eai.13-10-2021.171319https://doaj.org/toc/2410-0218Recent advancements in machine learning have made it a tool of choice for different classification and analytical problems. This paper deals with a critical field of computer networking: network security and the possibilities of machine learning automation in this field. We will be doing exploratory data analysis on the benchmark UNSW-NB15 dataset. This dataset is a modern substitute for the outdated KDD’99 dataset as it has greater uniformity of pattern distribution. We will also implement several ensemble algorithms like Random Forest, Extra trees, AdaBoost, and XGBoost to derive insights from the data and make useful predictions. We calculated all the standard evaluation parameters for comparative analysis among all the classifiers used. This analysis gives knowledge, investigates difficulties, and future opportunities to propel machine learning in networking. This paper can give a basic understanding of data analytics in terms of security using Machine Learning techniques.Neha SharmaNarendra YadavSaurabh SharmaEuropean Alliance for Innovation (EAI)articlekdd’99unsw-nb15ensemble algorithmsxgboostadaboostrandom forestextra treesComputer engineering. Computer hardwareTK7885-7895Systems engineeringTA168ENEAI Endorsed Transactions on Industrial Networks and Intelligent Systems, Vol 8, Iss 29 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	kdd’99 unsw-nb15 ensemble algorithms xgboost adaboost random forest extra trees Computer engineering. Computer hardware TK7885-7895 Systems engineering TA168
spellingShingle	kdd’99 unsw-nb15 ensemble algorithms xgboost adaboost random forest extra trees Computer engineering. Computer hardware TK7885-7895 Systems engineering TA168 Neha Sharma Narendra Yadav Saurabh Sharma Classification of UNSW-NB15 dataset using Exploratory Data Analysis using Ensemble Learning
description	Recent advancements in machine learning have made it a tool of choice for different classification and analytical problems. This paper deals with a critical field of computer networking: network security and the possibilities of machine learning automation in this field. We will be doing exploratory data analysis on the benchmark UNSW-NB15 dataset. This dataset is a modern substitute for the outdated KDD’99 dataset as it has greater uniformity of pattern distribution. We will also implement several ensemble algorithms like Random Forest, Extra trees, AdaBoost, and XGBoost to derive insights from the data and make useful predictions. We calculated all the standard evaluation parameters for comparative analysis among all the classifiers used. This analysis gives knowledge, investigates difficulties, and future opportunities to propel machine learning in networking. This paper can give a basic understanding of data analytics in terms of security using Machine Learning techniques.
format	article
author	Neha Sharma Narendra Yadav Saurabh Sharma
author_facet	Neha Sharma Narendra Yadav Saurabh Sharma
author_sort	Neha Sharma
title	Classification of UNSW-NB15 dataset using Exploratory Data Analysis using Ensemble Learning
title_short	Classification of UNSW-NB15 dataset using Exploratory Data Analysis using Ensemble Learning
title_full	Classification of UNSW-NB15 dataset using Exploratory Data Analysis using Ensemble Learning
title_fullStr	Classification of UNSW-NB15 dataset using Exploratory Data Analysis using Ensemble Learning
title_full_unstemmed	Classification of UNSW-NB15 dataset using Exploratory Data Analysis using Ensemble Learning
title_sort	classification of unsw-nb15 dataset using exploratory data analysis using ensemble learning
publisher	European Alliance for Innovation (EAI)
publishDate	2021
url	https://doaj.org/article/ef8cef05f94f49d7b5cac04900ce8bdc
work_keys_str_mv	AT nehasharma classificationofunswnb15datasetusingexploratorydataanalysisusingensemblelearning AT narendrayadav classificationofunswnb15datasetusingexploratorydataanalysisusingensemblelearning AT saurabhsharma classificationofunswnb15datasetusingexploratorydataanalysisusingensemblelearning
_version_	1718406719063719936

Classification of UNSW-NB15 dataset using Exploratory Data Analysis using Ensemble Learning

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