Improving Model Performance for Predicting Exfiltration Attacks Through Resampling Strategies

Improving Model Performance for Predicting Exfiltration Attacks Through Resampling Strategies

Addressing class imbalance is critical in cybersecurity applications, particularly in scenarios like exfiltration detection, where skewed datasets lead to biased predictions and poor generalization for minority classes. This study investigates five Synthetic Minority Oversampling Technique (SMOTE)...

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Bibliographic Details
Main Authors: Arif Rahman Hakim, Kalamullah Ramli, Muhammad Salman, Esti Rahmawati Agustina
Format: Article
Language:English
Published: IIUM Press, International Islamic University Malaysia 2025-01-01
Series:International Islamic University Malaysia Engineering Journal
Subjects:
machine learning
imbalance data
SMOTE
exfiltration
Online Access:https://journals.iium.edu.my/ejournal/index.php/iiumej/article/view/3547
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https://journals.iium.edu.my/ejournal/index.php/iiumej/article/view/3547

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