Detecting web attacks using random undersampling and ensemble learners

Detecting web attacks using random undersampling and ensemble learners

Abstract Class imbalance is an important consideration for cybersecurity and machine learning. We explore classification performance in detecting web attacks in the recent CSE-CIC-IDS2018 dataset. This study considers a total of eight random undersampling (RUS) ratios: no sampling, 999:1, 99:1, 95:5...

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Bibliographic Details
Main Authors: Richard Zuech, John Hancock, Taghi M. Khoshgoftaar
Format: Article
Language:English
Published: SpringerOpen 2021-05-01
Series:Journal of Big Data
Subjects:
CSE-CIC-IDS2018
Intrusion Detection
Web Attacks
Class Imbalance
Random Undersampling
Ensemble Learners
Online Access:https://doi.org/10.1186/s40537-021-00460-8

Internet

https://doi.org/10.1186/s40537-021-00460-8

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