CICIDS-2017 dataset feature analysis with information gain for anomaly detection
Feature selection (FS) is one of the important tasks of data preprocessing in data analytics. The data with a large number of features will affect the computational complexity, increase a huge amount of resource usage and time consumption for data analytics. The objective of this study is to analyze...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers Inc.
2020
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| Online Access: | http://eprints.utm.my/93850/1/CICIDS-MohdYazidIdris2020_CICIDS2017DatasetFeatureAnalysisWithInformation.pdf |
| _version_ | 1796865705701801984 |
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| author | Kurniabudi, Kurniabudi Stiawan, Deris Darmawijoyo, Darmawijoyo Idris, Mohd. Yazid Bamhdi, Alwi M. Budiarto, Rahmat |
| author_facet | Kurniabudi, Kurniabudi Stiawan, Deris Darmawijoyo, Darmawijoyo Idris, Mohd. Yazid Bamhdi, Alwi M. Budiarto, Rahmat |
| author_sort | Kurniabudi, Kurniabudi |
| collection | ePrints |
| description | Feature selection (FS) is one of the important tasks of data preprocessing in data analytics. The data with a large number of features will affect the computational complexity, increase a huge amount of resource usage and time consumption for data analytics. The objective of this study is to analyze relevant and significant features of huge network traffic to be used to improve the accuracy of traffic anomaly detection and to decrease its execution time. Information Gain is the most feature selection technique used in Intrusion Detection System (IDS) research. This study uses Information Gain, ranking and grouping the features according to the minimum weight values to select relevant and significant features, and then implements Random Forest (RF), Bayes Net (BN), Random Tree (RT), Naive Bayes (NB) and J48 classifier algorithms in experiments on CICIDS-2017 dataset. The experiment results show that the number of relevant and significant features yielded by Information Gain affects significantly the improvement of detection accuracy and execution time. Specifically, the Random Forest algorithm has the highest accuracy of 99.86% using the relevant selected features of 22, whereas the J48 classifier algorithm provides an accuracy of 99.87% using 52 relevant selected features with longer execution time. |
| first_indexed | 2024-03-05T21:01:08Z |
| format | Article |
| id | utm.eprints-93850 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T21:01:08Z |
| publishDate | 2020 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | utm.eprints-938502022-03-07T00:09:16Z http://eprints.utm.my/93850/ CICIDS-2017 dataset feature analysis with information gain for anomaly detection Kurniabudi, Kurniabudi Stiawan, Deris Darmawijoyo, Darmawijoyo Idris, Mohd. Yazid Bamhdi, Alwi M. Budiarto, Rahmat QA75 Electronic computers. Computer science Feature selection (FS) is one of the important tasks of data preprocessing in data analytics. The data with a large number of features will affect the computational complexity, increase a huge amount of resource usage and time consumption for data analytics. The objective of this study is to analyze relevant and significant features of huge network traffic to be used to improve the accuracy of traffic anomaly detection and to decrease its execution time. Information Gain is the most feature selection technique used in Intrusion Detection System (IDS) research. This study uses Information Gain, ranking and grouping the features according to the minimum weight values to select relevant and significant features, and then implements Random Forest (RF), Bayes Net (BN), Random Tree (RT), Naive Bayes (NB) and J48 classifier algorithms in experiments on CICIDS-2017 dataset. The experiment results show that the number of relevant and significant features yielded by Information Gain affects significantly the improvement of detection accuracy and execution time. Specifically, the Random Forest algorithm has the highest accuracy of 99.86% using the relevant selected features of 22, whereas the J48 classifier algorithm provides an accuracy of 99.87% using 52 relevant selected features with longer execution time. Institute of Electrical and Electronics Engineers Inc. 2020 Article PeerReviewed application/pdf en http://eprints.utm.my/93850/1/CICIDS-MohdYazidIdris2020_CICIDS2017DatasetFeatureAnalysisWithInformation.pdf Kurniabudi, Kurniabudi and Stiawan, Deris and Darmawijoyo, Darmawijoyo and Idris, Mohd. Yazid and Bamhdi, Alwi M. and Budiarto, Rahmat (2020) CICIDS-2017 dataset feature analysis with information gain for anomaly detection. IEEE Access, 8 . pp. 132911-132921. ISSN 2169-3536 http://dx.doi.org/10.1109/ACCESS.2020.3009843 |
| spellingShingle | QA75 Electronic computers. Computer science Kurniabudi, Kurniabudi Stiawan, Deris Darmawijoyo, Darmawijoyo Idris, Mohd. Yazid Bamhdi, Alwi M. Budiarto, Rahmat CICIDS-2017 dataset feature analysis with information gain for anomaly detection |
| title | CICIDS-2017 dataset feature analysis with information gain for anomaly detection |
| title_full | CICIDS-2017 dataset feature analysis with information gain for anomaly detection |
| title_fullStr | CICIDS-2017 dataset feature analysis with information gain for anomaly detection |
| title_full_unstemmed | CICIDS-2017 dataset feature analysis with information gain for anomaly detection |
| title_short | CICIDS-2017 dataset feature analysis with information gain for anomaly detection |
| title_sort | cicids 2017 dataset feature analysis with information gain for anomaly detection |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/93850/1/CICIDS-MohdYazidIdris2020_CICIDS2017DatasetFeatureAnalysisWithInformation.pdf |
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