Earticle

초록

영어

Despite institutional and technological efforts to eradicate voice phishing, the number of cases of voice phishing has continued to increase over the past decade. This is because it is increasingly difficult for victims to distinguish between voice phishing and normal calls due to the diversification, intelligence, and sophistication of voice phishing techniques. Although there have been studies on techniques to detect voice phishing, the effectiveness of anti-voice phishing is still insufficient. Therefore, in this study, we propose a voice phishing prevention education scheme that will enhance the general public's cognitive awareness of voice phishing and help protect potential victims. We propose a voice phishing detection model trained with real and normal calling voices using deep learning-based KoBERT, a service that evaluates voice phishing risk and provides voice phishing prevention training content and countermeasures in case of damage.1

목차

Abstract
I. INTRODUCTION
II. LITERATURE REVIEW
III. KOBERT-BASED VOICE PHISHING DETECTION MODEL
A. Dataset Collection
B. Dataset Preprocessing
C. Model Learning
D. Building an API Server
E. Development of Voice Phishing Prevention Service
IV. CONCLUSION
REFERENCES

키워드

저자

• Jihoon Yang [ Dept. of Computer Education Jeju National University ]
• Chunghun Lee [ Dept. of Computer Education Jeju National University ]
• Seong Baeg Kim [ Dept. of Computer Education Jeju National University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004