Citation

Hong, Brendan and Tee, Connie and Goh, Michael Kah Ong (2023) Scam Calls Detection Using Machine Learning Approaches. In: 2023 11th International Conference on Information and Communication Technology (ICoICT), 23-24 August 2023, Melaka, Malaysia.

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Abstract

In recent years, scam calls have become increasingly prevalent resulting in financial loss, identity theft, and other fraudulent activities. This research proposes a machine learning-based approach for scam call classification and detection using natural language processing (NLP) along with deep learning techniques. The model uses the dataset of scam and non-scam calls to train and understand the context of the caller and determine if the conversation is a scam or not. NLP techniques are leveraged, such as preprocessing text, converting audio samples to texts with Google API, and word embeddings, to build an accurate and reliable classifier. The highest results obtained is Long Short-Term Memory (LSTM) algorithm with an accuracy of 85.61% in detecting scam calls.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Natural Language Processing, Scam Calls Detection, Machine Learning.
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	01 Nov 2023 02:24
Last Modified:	01 Nov 2023 02:24
URII:	http://shdl.mmu.edu.my/id/eprint/11824

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