Impact of Data Balancing and Feature Selection on Machine Learning-based Network Intrusion Detection

Impact of Data Balancing and Feature Selection on Machine Learning-based Network Intrusion Detection

Unbalanced datasets are a common problem in supervised machine learning. It leads to a deeper understanding of the majority of classes in machine learning. Therefore, the machine learning model is more effective at recognizing the majority classes than the minority classes. Naturally, imbalanced dat...

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Bibliographic Details
Main Authors: Azhari Shouni Barkah, Siti Rahayu Selamat, Zaheera Zainal Abidin, Rizki Wahyudi
Format: Article
Language:English
Published: Politeknik Negeri Padang 2023-02-01
Series:JOIV: International Journal on Informatics Visualization
Subjects:
intrusion detection
feature selection
imbalance
smote
adasyn
Online Access:https://joiv.org/index.php/joiv/article/view/1041

Internet

https://joiv.org/index.php/joiv/article/view/1041

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