Study of a real time voice phishing detection system with machine learning technology
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Date
2024-03-14
Authors
Σκαρπέτης, Ιωάννης
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Abstract
This study investigated the development and performance of a real-time vishing (voice
phishing) detection system employing machine learning algorithms.
The research methodology encompassed the creation of a custom dataset utilizing advanced
generative pretrained transformers (ChatGPT), preprocessing techniques, and the evaluation
and hyper parameter tuning of various machine learning models, including Logistic
Regression, Random Forest, Gradient Boosted Trees (GBT) and deep neural network
architectures. Furthermore, to support the real-time detection of such attacks a Speech-To Text system based on the Google API was implemented. Results from the real-time testing of
these models indicate a promising capability for distinguishing between legitimate and
fraudulent telecommunications in real-time.
The hyperband tuned neural network model alongside the gradient boosted trees model
displayed the most promising performance, since these models displayed the best accuracy
in classifications during the final testing process, suggesting their potential to be suitable for
deployment in real-world applications.
This study contributes to the ongoing efforts to safeguard individuals against vishing scams
and highlights the need for further research on the subject. Future enhancements of the work
presented in this study include improvements on the dataset diversity and the research of
alternative prediction algorithms and preprocessing methods like transformers. The findings
underscore the potential of machine learning in effectively bolstering cybersecurity measures
against the evolving threat of voice phishing.
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Keywords
Artificial intelligence and machine learning, Speech to text transcription in real time, Voice phishing detection