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

Elastic gradient boosting decision trees under limited labels by sequential epistemic uncertainty quantification: Elastic CatBoost Uncertainty (eCBU)

Sennema, Erik (TU Delft Electrical Engineering, Mathematics and Computer Science)

Lukina, A. (mentor)
Zhauniarovich, Y. (mentor)
Bárbaro, E. (mentor)
Spaan, M.T.J. (graduation committee)
Tax, D.M.J. (graduation committee)

Delft University of Technology

Computer Science | Artificial Intelligence

2023-09-13

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.

concept drift
uncertainty quantification
Intrusion Detection

http://resolver.tudelft.nl/uuid:6eab157f-ee89-4d0b-b1df-a849ae3099d0

Student theses

master thesis

© 2023 Erik Sennema