• KIPS Transactions on Computer and Communication Systems
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• v.12 no.5
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• pp.165-170
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• 2023

With the recent rise of blockchain technology, cryptocurrency platforms using it are increasing, and currency transactions are being actively conducted. However, crimes that abuse the characteristics of cryptocurrency are also increasing, which is a problem. In particular, phishing scams account for more than a majority of Ethereum cybercrime and are considered a major security threat. Therefore, effective phishing scams detection methods are urgently needed. However, it is difficult to provide sufficient data for supervised learning due to the problem of data imbalance caused by the lack of phishing addresses labeled in the Ethereum participating account address. To address this, this paper proposes a phishing scams detection method that uses both Trans2vec, an effective graph embedding techique considering Ethereum transaction networks, and semi-supervised learning model Tri-training to make the most of not only labeled data but also unlabeled data.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.791-805
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• 2022

In recent years, the number of scams related to voice phishing has been on the decline, but the number of messenger phishing attacks, a new type of crime, is increasing. In this study, by analyzing SNS posts containing messenger phishing cases, criminal trends of the main methods, imposture of trusted relative and fake payment were identified. Through the analysis, main words and patterns composing the message and the similarity and continuity of the phone numbers used were derived as criminal attributes, and criminal organizations were grouped. As the results of the analysis, we propose a cooperative system to prevent damage from messenger phishing by disseminating the criminal information collected by investigative agencies to private operators, and a plan to respond to messenger phishing predicted through grouping of criminal organizations.

• Convergence Security Journal
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• v.14 no.5
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• pp.65-74
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• 2014

Phishing scans through wired telephones have been evolving into smissing and pharming. While we use wire or wireless telephones, text messages, e-mails, and online-banking conveniently, the ways of hacking and phishing attacks are getting developed and various. This paper investigates the various aspects of attacks depending on the kinds of phishing and suggests general prevention measures. In addition, the user-oriented practical preventive measures and government-driven long term measures are proposed in this paper. Technological developments, short or long term preventive measures proposed by the government, and continuous public relations could be solutions since in a short time, it could be difficult to eradicate phishing scams evolving continuously. Besides, the internet media as well as SNS are great helps in promoting the preventives against phishing and smissing. Finally this paper asserts that the newly developed service technology should be made carefully without security problems.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.272-282
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• 2022

Between 2014 and 2019, the US lost more than 2.1 billion USD to phishing attacks, according to the FBI's Internet Crime Complaint Center, and COVID-19 scam complaints totaled more than 1,200. Phishing attacks reflect these awful effects. Phishing websites (PWs) detection appear in the literature. Previous methods included maintaining a centralized blacklist that is manually updated, but newly created pseudonyms cannot be detected. Several recent studies utilized supervised machine learning (SML) algorithms and schemes to manipulate the PWs detection problem. URL extraction-based algorithms and schemes. These studies demonstrate that some classification algorithms are more effective on different data sets. However, for the phishing site detection problem, no widely known classifier has been developed. This study is aimed at identifying the features and schemes of SML that work best in the face of PWs across all publicly available phishing data sets. The Scikit Learn library has eight widely used classification algorithms configured for assessment on the public phishing datasets. Eight was tested. Later, classification algorithms were used to measure accuracy on three different datasets for statistically significant differences, along with the Welch t-test. Assemblies and neural networks outclass classical algorithms in this study. On three publicly accessible phishing datasets, eight traditional SML algorithms were evaluated, and the results were calculated in terms of classification accuracy and classifier ranking as shown in tables 4 and 8. Eventually, on severely unbalanced datasets, classifiers that obtained higher than 99.0 percent classification accuracy. Finally, the results show that this could also be adapted and outperforms conventional techniques with good precision.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.3
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• pp.107-117
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• 2014

The personal information extrusion accident(hacking, improper maintenance) by the internet has been increased rapidly during the last ten-year period. The personal information includes Korean resident registration number, name, cellphone number, home telephone number, E-mail address, home address, etc. Korean resident registration number is unchangeable from the cradle to the grave. Illegally accessed personal information can be wrongfully resoled or used at phishing scam. In this paper, we propose a alternative means of Korean resident registration number using the authorized certificate. The authorized certificate is in use safely in Internet. The authorized certificate(included Chinese name) is in used in offline service, the authorized certificate(included English name) is used in online(for government service) and the authorized certificate(not included name) is used in online(for private service). The authorized certificates are possible for discard, republication. The method can reduce the possibility of the personal information extrusion.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.266-268
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• 2022

최근 블록체인 기술이 부상하면서 이를 이용한 암호화폐가 범죄의 대상이 되고 있다. 특히 피싱 스캠은 이더리움 사이버 범죄의 과반수 이상을 차지하며 주요 보안 위협원으로 여겨지고 있다. 따라서 효과적인 피싱 스캠 탐지 방법이 시급하다. 그러나 전체 노드에서 라벨링된 피싱 주소의 부족으로 인한 데이터 불균형으로 인하여 지도학습에 충분한 데이터 제공이 어려운 상황이다. 이를 해결하기 위해 본 논문에서는 이더리움 트랜잭션 네트워크를 고려한 효율적인 네트워크 임베딩 기법인 trans2vec 과 준지도 학습 모델 tri-training 을 함께 사용하여 라벨링된 데이터뿐만 아니라 라벨링되지 않은 데이터도 최대한 활용하는 피싱 스캠 탐지 방법을 제안한다.

• The Journal of the Korea Contents Association
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• v.13 no.3
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• pp.141-151
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• 2013

With the spread of Personal Computers(PC) in the 1980's, many people started to deal businesses with PC. From late 1990's, the Internet age with PC have started and many people have showed keen interest in cyber-space and now they are utilizing it. Since 2000's the use of cyber-space have skyrocketed and it caused significant changes to humans' life. There was a huge prosperity to us but the new kind of crime, cyber-crime, was raised. Unlike past physical type of crimes, those cyber-crimes take place in the cyber-space and they have special features of non-facing, anonymity, specialty, technologic, repetition, continuation. Those cyber-crimes are continually growing since 2003 and in 2010 it almost doubled compared to 2003. General cyber-crimes like phishing-scam pornography circulation was most of them and notably perpetrators of them are younger generation. Recently cyber-crimes are showing the trend of advancing more and more and cyber-bullying, fraud like phishing scam are on the rise. The police are responding by making 'Cyber Terror Response Center', but it does not work effectively with the problems of breakup of prevention and investigation unit, procedure of investigation and the system itself. So, I suggest practical use of private security to remedy our police's weakness and to prevent cyber-crimes. Preventing solutions of cyber-crime with private security are physical defense of large-scale servers and vital computers, building of Back-up system to prevent vital data loss, and building of cyber-crime preventing system combining software and hardware.