Synthetic Minority Oversampling Technique for Optimizing Classification Tasks in Botnet and Intrusion-Detection-System Datasets

Synthetic Minority Oversampling Technique for Optimizing Classification Tasks in Botnet and Intrusion-Detection-System Datasets

Presently, security is a hot research topic due to the impact in daily information infrastructure. Machine-learning solutions have been improving classical detection practices, but detection tasks employ irregular amounts of data since the number of instances that represent one or several malicious...

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Bibliographic Details
Main Authors: David Gonzalez-Cuautle, Aldo Hernandez-Suarez, Gabriel Sanchez-Perez, Linda Karina Toscano-Medina, Jose Portillo-Portillo, Jesus Olivares-Mercado, Hector Manuel Perez-Meana, Ana Lucila Sandoval-Orozco
Format: Article
Language:English
Published: MDPI AG 2020-01-01
Series:Applied Sciences
Subjects:
imbalanced data
datasets
botnet detection
synthetic minority oversampling technique
machine learning
predictive models.
Online Access:https://www.mdpi.com/2076-3417/10/3/794

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https://www.mdpi.com/2076-3417/10/3/794

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