Decision tree algorithms.

<div><p>Imbalanced intrusion classification is a complex and challenging task as there are few number of instances/intrusions generally considered as minority instances/intrusions in the imbalanced intrusion datasets. Data sampling methods such as over-sampling and under-sampling methods...

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Bibliographic Details
Main Author:	Mahbub E. Sobhani (22278967) (author)
Other Authors:	Anika Tasnim Rodela (22278970) (author), Dewan Md. Farid (22278973) (author)
Published:	2025
Subjects:	Genetics Immunology Science Policy Environmental Sciences not elsewhere classified Astronomical and Space Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified used decision tree reduced computational power na &# 239 exhibit superior performance execution time requirements 96 %, 85 supervised machine learning imbalanced intrusion data handling imbalanced data data balancing methods imbalanced intrusion datasets 83 %, 99 65 %, 99 sampling methods remove superior performer compared intrusions generally considered data sampling methods sampling technique ignores used random forest bagging techniques address synthetic minority instances minority informative instances sampling methods sampling techniques imbalanced ratio 77 %, synthetic minority informative instances sampling technique random forest cicddos2019 datasets minority instances common technique class instances overfitting problem overall dataset large portion high dimensionality generated e experimental results ensemble learning challenging task bayes classifier base classifier bagging model adaptive treehive accuracy rates
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Decision tree algorithms.

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