Milandu Keith Moussavou Boussougou, Dong-Joo Park

KIPS Transactions on Software and Data Engineering, Vol. 11, No. 10, pp. 437-446, Oct. 2022
https://doi.org/10.3745/KTSDE.2022.11.10.437,   PDF Download:
Keywords: KoBERT, NLP, Text Classification, Machine Learning, Deep Learning
Abstract

Text classification task from Natural Language Processing (NLP) combined with state-of-the-art (SOTA) Machine Learning (ML) and Deep Learning (DL) algorithms as the core engine is widely used to detect and classify voice phishing call transcripts. While numerous studies on the classification of voice phishing call transcripts are being conducted and demonstrated good performances, with the increase of non-face-to-face financial transactions, there is still the need for improvement using the latest NLP technologies. This paper conducts a benchmarking of Korean voice phishing detection performances of the pre-trained Korean language model KoBERT, against multiple other SOTA algorithms based on the classification of related transcripts from the labeled Korean voice phishing dataset called KorCCVi. The results of the experiments reveal that the classification accuracy on a test set of the KoBERT model outperforms the performances of all other models with an accuracy score of 99.60%.

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