Elastic gradient boosting decision trees under limited labels by sequential epistemic uncertainty quantification

Elastic CatBoost Uncertainty (eCBU)

Master thesis (2023)

Authors

E.J. Sennema Electrical Engineering, Mathematics and Computer Science

Contributors

A. Lukina Algorithmics - EEMCS (supervisor 1)
Y. Zhauniarovich Organisation & Governance - TPM (supervisor 1)
E Bárbaro (supervisor 1)
M.T.J. Spaan Algorithmics - EEMCS (supervisor 2)
D.M.J. Tax Pattern Recognition and Bioinformatics - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Erik Sennema
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Published Date

13-09-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Intrusion detection systems (IDSs) are essential for protecting computer systems and networks from malicious attacks. However, IDSs face challenges in dealing with dynamic and imbalanced data, as well as limited label availability. In this thesis, we propose a novel elastic gradient boosting decision tree algorithm, namely Elastic CatBoost Uncertainty (eCBU), that adapts to concept drifts and copes with label scarcity by using a novel sequential uncertainty estimation method. We compare our method with state-of-the-art techniques on synthetic and real-world datasets and show that it achieves comparable accuracy and higher robustness to limited label availability in intrusion detection tasks.