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

Sharma, Neha and Yadav, Narendra Singh and Sharma, Saurabh (2021) Classification of UNSW-NB15 dataset using Exploratory Data Analysis using Ensemble Learning. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 8 (29). e4. ISSN 2410-0218

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Abstract

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 This dataset is a modern substitute for the outdated KDD’99 dataset as it has greater uniformity of pattern distribution.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.

Item Type: Article
Uncontrolled Keywords: KDD’99, UNSW-NB15, Ensemble algorithms, XGBoost, AdaBoost, Random Forest, Extra trees
Subjects: T Technology > T Technology (General)
Depositing User: EAI Editor IV
Date Deposited: 16 Dec 2021 10:35
Last Modified: 16 Dec 2021 10:35
URI: https://eprints.eudl.eu/id/eprint/8962

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