Resampling imbalanced data for network intrusion detection datasets
Abstract Machine learning plays an increasingly significant role in the building of Network Intrusion Detection Systems. However, machine learning models trained with imbalanced cybersecurity data cannot recognize minority data, hence attacks, effectively. One way to address this issue is to use res...
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| Autori principali: | , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
SpringerOpen
2021-01-01
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| Serie: | Journal of Big Data |
| Soggetti: | |
| Accesso online: | https://doi.org/10.1186/s40537-020-00390-x |
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