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Real-time Korean voice phishing detection based on machine learning approaches

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Abstract

Voice phishing, or vishing, is a phishing phone call in which an attacker lures receivers into providing personal their information. Damage from vishing is a serious problem worldwide and is increasing in frequency. Therefore, this study is aimed at detecting vishing in real time. Owing to the absence of research on spam detection using low-resource languages, we detect vishing in the Korean language using basic machine-learning models. We collected actual vishing damage data and converted the voice files into text to achieve spam detection using natural language processing techniques. The focus is on determining whether vishing can be rapidly detected, rather than model development. Based on the results, we suggest that vishing can be detected in real time and requires only a short training time when using machine learning models.

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Data availability Statement

The datasets collected for the current study are available at https://anonymous.4open.science/r/vishing-1AF8. Additional information used in this study can be obtained from the corresponding author upon reasonable request.

Notes

  1. https://www.fss.or.kr/fss/vstop/avoid/this_voice_l.jsp.

  2. https://corpus.korean.go.kr/.

  3. https://www.anonymous.4open.science/r/vishing-1AF8.

  4. https://cloud.google.com/speech-to-text?hl=ko.

  5. https://www.ncloud.com/product/aiService/clovaSpeech.

  6. https://konlpy.org/en/latest/.

  7. https://github.com/kakao/khaiii.

  8. https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html.

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Acknowledgements

This work was supported by an Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. IITP-2021-0-00358, AI big data-based cyber security orchestration, and automated response technology development). Moreover, this research was supported by National Research Foundation (NRF) of Korea Grant funded by the Korean Government (MSIT) (No. 2021R1A4A3022102).

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Authors and Affiliations

  1. Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul, Republic of Korea

    Minyoung Lee & Eunil Park

Authors
  1. Minyoung Lee
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  2. Eunil Park
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Contributions

ML and EP designed the study. ML collected and analyzed the data. EP presented the results. ML and EP wrote and revised the manuscript. All authors reviewed the manuscript.

Corresponding author

Correspondence to Eunil Park.

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The authors have no conflicts or competing interests to declare.

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Appendices

Appendix A. Data analysis

Table 6 shows the top-100 most widely used words in spam and nonspam cases when analyzing spam and nonspam text content, respectively. The nonspam cases mostly included everyday words, such as us, movies, people, and me, whereas the spam cases included words such as loan, investigation, bank accounts, bank, illegality, and victims (given in bold). Therefore, understanding the meaning of these words is important in vishing detection.

Table 6 Data analysis
Full size table

Appendix B. Speech-to-text tool examples

We converted the collected.mp3 files of voice phishing speech, and the results when using actual voice scripts, Google speech-to-text API, and Naver Clova Speech speech-to-text conversion tools are shown in Table 7.

Table 7 Comparison of speech-to-text tools
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Lee, M., Park, E. Real-time Korean voice phishing detection based on machine learning approaches. J Ambient Intell Human Comput 14, 8173–8184 (2023). https://doi.org/10.1007/s12652-021-03587-x

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