Mastersthesis,

Experimental Evaluation Tool for Exploring User-based Active Learning for ML-based Browser and URL Detection

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University of Würzburg, (September 2022)

Abstract

While Machine Learning (ML) is gaining more and more popularity, the actual incorporation into running systems is still lacking, e.g., due to missing trust of network operators. To build this trust, the goal of this work is to incorporate an active learning approach into an already existing ML-approach, that opens up the possibility of making ML-based decisions more explainable and allows to incorporate feedback of the administrator, e.g.,to retrain ML models with the adjusted decisions.

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