• Annual Conference of KIPS
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• 2021.11a
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• pp.297-299
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• 2021

Text classification is one of the popular tasks in Natural Language Processing (NLP) used to classify text or document applications such as sentiment analysis and email filtering. Nowadays, state-of-the-art (SOTA) Machine Learning (ML) and Deep Learning (DL) algorithms are the core engine used to perform these classification tasks with high accuracy, and they show satisfying results. This paper conducts a benchmarking performance's analysis of multiple SOTA algorithms on the first known labeled Korean voice phishing dataset called KorCCVi. Experimental results reveal performed on a test set of 366 samples reveal which algorithm performs the best considering the training time and metrics such as accuracy and F1 score.

• Annual Conference of KIPS
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• 2023.11a
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• pp.947-948
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• 2023

본 논문은 보이스피싱 취약 계층을 위해 통화 내용을 신속하게 처리하여 실시간으로 범죄 여부를 판별하는 VoIP 에 특화된 시스템을 제안하였다. 실제 보이스 피싱 통화 유형을 학습한 탐지 모델을 개발하여 API 로 배포하였다. 또한 보이스피싱 위험도가 일정 수준에 도달할 경우 사용자에게 보이스피싱 가능성을 경고하는 장치를 제작하였다. 본 연구는 보이스피싱을 사전에 탐지함으로써 개인정보의 유출 및 금융 피해를 예방하고 정보 보안을 실천하는 데 기여할 것으로 기대된다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.95-96
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• 2024

본 논문은 금융 범죄 중 하나인 보이스피싱을 실시간으로 예방하기 위한 탐지 기법을 제안한다. 제안된 모델은 수화기에 출력되는 음성을 녹음하고 네이버 CSR(Cloud Speech Recognition)을 통해 텍스트 파일로 변환한 후 딥러닝 기반의 KoBERT를 바탕으로 다양한 보이스피싱 패턴을 학습하여 실시간 환경에서의 신속하고 정확한 탐지를 위해 실제 통화 데이터를 적절하게 처리하여, 이를 통해 효과적인 보이스피싱 예방에 도움을 줄 것으로 예상된다.

• Journal of Service Research and Studies
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• v.5 no.1
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• pp.71-78
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• 2015

Created, but the development of mobile devices to have a margin of life have appeared in the opposite forces that are considered to be the target of financial crime and attacks them. Financial crime among crimes that target the smartphone SMS phishing, phishing, pharming, phishing, etc. voice and, in particular, a phenomenon that is growing a lot of SMS phishing is by nature a text message to your mobile. Ye Jin proactive rather than post responses in order to be safe from the SMS phishing attack individuals and businesses, and asset protection is even more important in the country. For this, the SMS phishing attack detected in advance and that can block the development program, it is necessary to deploy.

• Journal of Service Research and Studies
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• v.5 no.2
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• pp.111-118
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• 2015

Although the development of mobile devices are made us a free life, they were displayed the subject of this financial crime and attacking forces in the other side. Among finance-related crime is become a serious crime that are targeting smartphones by SMS phishing, phishing, pharming, voice phishing etc. In particular, SMS phishing is increased according to phenomenon using the nature of a text message in the mobile. SMS phishing is become new crime due to the burden to the smartphone user. Their crime is also the advanced way from the existing fraud, such as making the malicious apps. Especially it generates loopholes in the law by a method such as using a foreign server. For safe from SMS phishing attacks, proactive pre-diagnosis is even more important rather than post responses. It is necessary to deploy blocking programs for detecting SMS phishing attacks in advance to do this. In this paper we are investigating the process of block types and block apps that are currently deployed and presenting the evaluation of the application of the detection block setting app.

• ETRI Journal
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• v.36 no.5
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• pp.865-875
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• 2014

Voice phishing (vishing) uses social engineering, based on people's trust in telephone services, to trick people into divulging financial data or transferring money to a scammer. In a vishing attack, a scammer often modifies the telephone number that appears on the victim's phone to mislead the victim into believing that the phone call is coming from a trusted source, since people typically judge a caller's legitimacy by the displayed phone number. We propose a system named iVisher for detecting a concealed incoming number (that is, caller ID) in Session Initiation Protocol-based Voice-over-Internet Protocol initiated phone calls. Our results demonstrate that iVisher is capable of detecting a concealed caller ID without significantly impacting upon the overall call setup time.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.6
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• pp.1475-1487
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• 2018

To verify remitter's identity when the remitter transfers money to a recipient using an electronic financial service provided by the financial institution, the remitter inputs the information; such as the withdrawal account number, the withdrawal amount, the password pre-registered with the financial company, or the information from authenticating medium that is previously distributed by the financial institution. However, the 1-Way transaction between the financial institution and the remitter is exposed to a great risk of accidents such as an anomaly remittance or a voice phishing fraud. Therefore, in this study, we propose a 2-WAY trade verification model for electronic financial transaction that can be mutually agreed by allowing the recipient to share the transaction information with the remitter and the financial company. We have improved the traditional electronic financial transaction's method by replacing it to 2-WAY trade method, and it is used for various purposes; such as preventing an error within the remittance or voice phishing fraud, enhancing loan transaction and contract transaction, etc. Through these variety of applications, we are expecting to reduce the inconveniences while improving the convenience of financial transaction and vitalizing the P2P transaction of financial institution.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.229-239
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• 2021

For this study, we have extensively analyzed the presentation data of the government's financial-related departments and the data of each financial institution and electronic financial institution.. As a result, In Korea's electronic financial environment, real changes such as first) expansion of non-face-to-face finance, second) teleworking in the financial sector, third) abolition of accredited certification, fourth) advanced voice phishing, fifth) openness of the financial industry and diversification of forms, sixth) the'walletless society'. In addition to the above, however, global changes triggered by the Fourth Industrial Revolution spread to the financial security sector, making it difficult to respond to problems such as artificial intelligence/ deep learning/ user analysis/ deepfake technology. As the proportion of electronic finance is increasing socially, it should be studied in the fields of electronic finance and its environment, and crime and criminal investigation.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.1
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• pp.149-164
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• 2019

Digital Transformation and the Fourth Industrial Revolution, electronic financial services should be provided safely in accordance with rapidly changing technology changes in the times of change. However, telecommunication finance fraud (voice phishing) accidents are currently ongoing, and various efforts are being made to eradicate accidents such as legal amendment and improvement of policy system in order to cope with continuous increase, intelligence and advancement of accidents. In addition, financial institutions are trying to prevent fraudulent accidents by improving and upgrading the abnormal financial transaction detection system, but the results are not very clear. Despite these efforts, telecommunications and financial fraud incidents have evolved to evolve against countermeasures. In this paper, we propose an intelligent over - the - counter financial transaction system modeled through scenario - based Rule model and artificial intelligence algorithm to prevent financial transaction accidents by voice phishing. We propose an implementation model of artificial intelligence abnormal financial transaction detection system and an optimized countermeasure model that can block and respond to analysis and detection results.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.3
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• pp.439-449
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• 2024

Recent rapid advancements in voice generation technology have enabled the natural synthesis of voices using text alone. However, this progress has led to an increase in malicious activities, such as voice phishing (voishing), where generated voices are exploited for criminal purposes. Numerous models have been developed to detect the presence of synthesized voices, typically by extracting features from the voice and using these features to determine the likelihood of voice generation.This paper proposes a new model for extracting voice features to address misuse cases arising from generated voices. It utilizes a deep learning-based audio codec model and the pre-trained natural language processing model BERT to extract novel voice features. To assess the suitability of the proposed voice feature extraction model for voice detection, four generated voice detection models were created using the extracted features, and performance evaluations were conducted. For performance comparison, three voice detection models based on Deepfeature proposed in previous studies were evaluated against other models in terms of accuracy and EER. The model proposed in this paper achieved an accuracy of 88.08%and a low EER of 11.79%, outperforming the existing models. These results confirm that the voice feature extraction method introduced in this paper can be an effective tool for distinguishing between generated and real voices.