BGP Anomaly Detection with Balanced Datasets
We use machine learning techniques to build predictive models for anomaly detection in the Border Gateway Protocol (BGP). Imbalanced datasets of network anomalies pose limitations to building predictive models for anomaly detection. In order to achieve better classification performance measures, we...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2018-01-01
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| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/298271 |
| _version_ | 1797207656388100096 |
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| author | Marijana Ćosović Slobodan Obradović |
| author_facet | Marijana Ćosović Slobodan Obradović |
| author_sort | Marijana Ćosović |
| collection | DOAJ |
| description | We use machine learning techniques to build predictive models for anomaly detection in the Border Gateway Protocol (BGP). Imbalanced datasets of network anomalies pose limitations to building predictive models for anomaly detection. In order to achieve better classification performance measures, we use resampling methods to balance classes in the datasets. We use undersampling, oversampling and combination techniques to change class distributions of the datasets. In this paper we build predictive models based on preprocessed network anomaly datasets of known Internet network anomalies and observe improvement in classifier performance measures compared to those reported in our previous work. We propose to use resampling combination techniques on datasets along with Decision Tree and Naïve Bayes classifiers in order to achieve the best trade-off between (1) the F-measure and the length of model training time, and (2) avoiding overfitting and loss of information. |
| first_indexed | 2024-04-24T09:26:23Z |
| format | Article |
| id | doaj.art-10b934caa0aa47f1a0f94cc13dab9291 |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:26:23Z |
| publishDate | 2018-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-10b934caa0aa47f1a0f94cc13dab92912024-04-15T14:53:17ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392018-01-0125376677510.17559/TV-20170219114900BGP Anomaly Detection with Balanced DatasetsMarijana Ćosović0Slobodan Obradović1Faculty of Electrical Engineering, University of East Sarajevo, Vuka Karadzića 30, 71123 East Sarajevo, B&HFaculty of Electrical Engineering, University of East Sarajevo, Vuka Karadzića 30, 71123 East Sarajevo, B&HWe use machine learning techniques to build predictive models for anomaly detection in the Border Gateway Protocol (BGP). Imbalanced datasets of network anomalies pose limitations to building predictive models for anomaly detection. In order to achieve better classification performance measures, we use resampling methods to balance classes in the datasets. We use undersampling, oversampling and combination techniques to change class distributions of the datasets. In this paper we build predictive models based on preprocessed network anomaly datasets of known Internet network anomalies and observe improvement in classifier performance measures compared to those reported in our previous work. We propose to use resampling combination techniques on datasets along with Decision Tree and Naïve Bayes classifiers in order to achieve the best trade-off between (1) the F-measure and the length of model training time, and (2) avoiding overfitting and loss of information.https://hrcak.srce.hr/file/298271anomaly detectionBGPclassificationsampling techniques |
| spellingShingle | Marijana Ćosović Slobodan Obradović BGP Anomaly Detection with Balanced Datasets Tehnički Vjesnik anomaly detection BGP classification sampling techniques |
| title | BGP Anomaly Detection with Balanced Datasets |
| title_full | BGP Anomaly Detection with Balanced Datasets |
| title_fullStr | BGP Anomaly Detection with Balanced Datasets |
| title_full_unstemmed | BGP Anomaly Detection with Balanced Datasets |
| title_short | BGP Anomaly Detection with Balanced Datasets |
| title_sort | bgp anomaly detection with balanced datasets |
| topic | anomaly detection BGP classification sampling techniques |
| url | https://hrcak.srce.hr/file/298271 |
| work_keys_str_mv | AT marijanacosovic bgpanomalydetectionwithbalanceddatasets AT slobodanobradovic bgpanomalydetectionwithbalanceddatasets |