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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| Format: | Article |
| Language: | English |
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European Alliance for Innovation (EAI)
2021-11-01
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| Series: | EAI Endorsed Transactions on Industrial Networks and Intelligent Systems |
| Subjects: | |
| Online Access: | https://eudl.eu/pdf/10.4108/eai.13-10-2021.171319 |
| _version_ | 1818741129216000000 |
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| author | Neha Sharma Narendra Yadav Saurabh Sharma |
| author_facet | Neha Sharma Narendra Yadav Saurabh Sharma |
| author_sort | Neha Sharma |
| collection | DOAJ |
| 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 This dataset is a modern substitute for the outdated KDD’99 dataset as it has greater uniformity of pattern distribution.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. |
| first_indexed | 2024-12-18T01:51:43Z |
| format | Article |
| id | doaj.art-ef8cef05f94f49d7b5cac04900ce8bdc |
| institution | Directory Open Access Journal |
| issn | 2410-0218 |
| language | English |
| last_indexed | 2024-12-18T01:51:43Z |
| publishDate | 2021-11-01 |
| publisher | European Alliance for Innovation (EAI) |
| record_format | Article |
| series | EAI Endorsed Transactions on Industrial Networks and Intelligent Systems |
| spelling | doaj.art-ef8cef05f94f49d7b5cac04900ce8bdc2022-12-21T21:25:01ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on Industrial Networks and Intelligent Systems2410-02182021-11-0182910.4108/eai.13-10-2021.171319Classification of UNSW-NB15 dataset using Exploratory Data Analysis using Ensemble LearningNeha Sharma0Narendra Yadav1Saurabh Sharma2Manipal University Jaipur, Rajasthan- 303007, IndiaManipal University Jaipur, Rajasthan- 303007, IndiaAmity University Rajasthan, IndiaRecent 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.https://eudl.eu/pdf/10.4108/eai.13-10-2021.171319kdd’99unsw-nb15ensemble algorithmsxgboostadaboostrandom forestextra trees |
| spellingShingle | Neha Sharma Narendra Yadav Saurabh Sharma Classification of UNSW-NB15 dataset using Exploratory Data Analysis using Ensemble Learning EAI Endorsed Transactions on Industrial Networks and Intelligent Systems kdd’99 unsw-nb15 ensemble algorithms xgboost adaboost random forest extra trees |
| title | 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_short | 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 |
| topic | kdd’99 unsw-nb15 ensemble algorithms xgboost adaboost random forest extra trees |
| url | https://eudl.eu/pdf/10.4108/eai.13-10-2021.171319 |
| work_keys_str_mv | AT nehasharma classificationofunswnb15datasetusingexploratorydataanalysisusingensemblelearning AT narendrayadav classificationofunswnb15datasetusingexploratorydataanalysisusingensemblelearning AT saurabhsharma classificationofunswnb15datasetusingexploratorydataanalysisusingensemblelearning |