• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.31-40
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• 2021

The increasing number of credit card fraud cases has become a considerable problem since the past decades. This phenomenon is due to the expansion of new technologies, including the increased popularity and volume of online banking transactions and e-commerce. In order to address the problem of credit card fraud detection, a rule-based approach has been widely utilized to detect and guard against fraudulent activities. However, it requires huge computational power and high complexity in defining and building the rule base for pattern matching, in order to precisely identifying the fraud patterns. In addition, it does not come with intelligence and ability in predicting or analysing transaction data in looking for new fraud patterns and strategies. As such, Data Mining and Machine Learning algorithms are proposed to overcome the shortcomings in this paper. The aim of this paper is to highlight the important techniques and methodologies that are employed in fraud detection, while at the same time focusing on the existing literature. Methods such as Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), naïve Bayesian, k-Nearest Neighbour (k-NN), Decision Tree and Frequent Pattern Mining algorithms are reviewed and evaluated for their performance in detecting fraudulent transaction.

• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.699-704
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• 2007

In this study, we examine why there exist different legal systems in electronic commerce or online financial trading. When a fraud online transaction occurs and the online customer disputes the transaction, the online customer takes responsibility for the proof of her/his argument in many European countries while in the U.S., the burden of proof lays on the firm. This paper analyzes how these two different legal systems exist and how these can be applied to electronic commerce law. In particular, this paper intends to find the optimal level of e-commerce firms' investment on security and analyzes how security investments can be related to firm's profits and consumer's welfare depending on IT infrastructure and social trust environment. More on, this paper can be contributed to provide guidelines for regulatory framework on ecommerce online transactions and discuss social welfare implications.

• Proceedings of the CALSEC Conference
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• 2004.02a
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• pp.75-82
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• 2004

Recently introduced by the major credit card associations as replacements for the decommissioned SET and 3DSET protocols, the new payment models, 3DSecure and UCAF/SPA, have been designed to provide online merchants with a solution to an existing problem in online credit card transactions - the lack of an effective and efficient means of authenticating cardholders. The expected benefits arising from this added level of security from the merchant′s perspective are increased consumer confidence, significant reduction in the levels of fraud and charge backs and "liability shift". Using data gleaned from preliminary interviews, discussion forums and promotional material, we present a critical analysis of the potential barriers and facilitators that will impact on the widespread traction of these programs in the marketplace in the coming years.

• Korean Management Science Review
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• v.21 no.2
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• pp.273-289
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• 2004

Although the Internet is useful for transferring information, Internet auction environments make fraud more attractive to offenders, because the chance of detection and punishment is decreased. One of these frauds is the phantom transaction, which is a colluding transaction by the buyer and seller to commit the illegal discounting of a credit card. They pretend to fulfill the transaction paid by credit card, without actually selling products, and the seller receives cash from the credit card corporations. Then the seller lends it out with quite a high interest rate to the buyer, whose credit rating is so poor that he cannot borrow money from anywhere else. The purpose of this study is to empirically investigate the factors necessary to detect phantom transactions in an online auction. Based upon studies that have explored the behaviors of buyers and sellers in online auctions, the following have been suggested as independent variables: bidding numbers, bid increments, sellers' credit, auction lengths, and starting bids. In this study. we developed Internet-based data collection software and collected data on transactions of notebook computers, each of which had a winning bid of over W one million. Data analysis with a logistic regression model revealed that starting bids, sellers' credit, and auction length were significant in detecting the phantom transactions.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.34 no.4
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• pp.222-228
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• 2023

There are two categories of online crimes related to children and adolescents: those committed by adolescents and those committed against children and adolescents. While recent trends in criminal law show consensus on strengthening punishment in cases of crimes against children and adolescents, there are mixed stances in cases of juvenile delinquency. One perspective emphasizes strict punishment, whereas the other emphasizes dispositions aligned with human rights. While various forms of online crime share the commonality in that the main part of the criminal act occurs online, they can be categorized into three types: those seeking financial gain, those driven by sexual motives, and those engaged in bullying. Among these, crimes driven by sexual motives are the most serious. Second-hand trading fraud and conditional (sexual) meeting fraud fall under the category of seeking financial gain and occur frequently. Crimes driven by sexual motives include obscenity via telecommunication, filming with discrete cameras, child and adolescent sexual exploitation material, fake video distribution, and blackmail/coercion using intimate images/videos ("sextortion"). These crimes lead to various legal issues such as whether to view vulgar acronyms or body cams that teenagers frequently use as simple subcultures or crimes, what criteria should be applied to judge whether a recorded material induces sexual desire or shame, and at what stage sexual grooming becomes punishable. For example, sniping posts, KakaoTalk prisons, and chat room explosions are tricky issues, as they may or may not be punished depending on the case. Particular caution should be exercised against the indiscriminate application of a strict punishment-oriented approach to the juvenile justice system, which is being discussed in relation to online sexual offenses. In the punishment case of online crime, juvenile offenders with a high potential for future improvement and reform must be treated with special consideration.

• Journal of Korea Multimedia Society
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• v.24 no.6
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• pp.760-767
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• 2021

The identity verification is one of the most important technologies in online services. Many services in society are provided online, and the service is provided after confirming the user's identity. Users can do a lot of things online, but they also have side effects. Online digital information is easily manipulated and it is difficult to verify its authenticity, causing social confusion. Accordingly, there has been a movement for individuals to directly manage their identity information using DID. In this paper, we propose a system that can authenticate identity by directly adding own personal information and issuing an identifier using DID technology based on a private blockchain. Then, to verify the proposed system, the scenario is executed and verified.

• Proceedings of the CALSEC Conference
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• 2001.02a
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• pp.331-341
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• 2001

■ 전자상거래: 서로 보지 않고 하는 거래 온라인 범죄 (Online Fraud) 급증 ■ 전자상거래 최대의 걸림돌: 신뢰 · 62% cited trust as the top E-commerce barrier - Authentication was key to 60%; Privacy was key to 56% · (1999 ITAA and E＆Y Survey) 인터넷을 신뢰의 공간(Trust Network)으로 만들자. (OECD의 Global Theme, 1998. 10) ■ 전자상거래 신뢰 확보 → 인증기관 출현(중략)

• Convergence Security Journal
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• v.16 no.5
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• pp.33-40
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• 2016

Credit card is means of payment used like cash in terms of function and its users have increased consistently. With development of Internet and electronic commerce a role as payment method of credit card has been growing. But as the risk which results from centralized information and online increases, credit card fraud is also growing. Card theft and loss are decreasing due to countermeasure of card companies and financial supervisory authorities, while card forge and identity theft are increasing. Recently because of frequent personal information leakage and deregulation of financial security following easy-to-use payment enforcement, customer's anxiety about card fraud is growing. And the increase of card fraud lowers trust on credit system as well as causes social costs. In this paper, the security problems of card operating system are addressed in depth and the measures such as immediate switch to IC card terminals, introduction of new security technology, supervision reinforcement of the authorities are proposed.

• Smart Media Journal
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• v.11 no.5
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• pp.63-74
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• 2022

Initial public offering (IPO) has a legal framework for investor protection, and because there are various quantitative evaluation factors, objective analysis is possible, and various studies have been conducted. In addition, crowdfunding also has several devices to prevent indiscriminate funding as the legal system for investor protection. On the other hand, the blockchain-based cryptocurrency white paper (ICO), which has recently been in the spotlight, has ambiguous legal means and standards to protect investors and lacks quantitative evaluation methods to evaluate ICOs objectively. Therefore, this study collects online-published ICO white papers to detect fraud in ICOs, performs ICO fraud predictions based on BERT, a text embedding technique, and compares them with existing Random Forest machine learning techniques, and shows the possibility on fraud detection. Finally, this study is expected to contribute to the study of ICO fraud detection based on quantitative methods by presenting the possibility of using a quantitative approach using unstructured data to identify frauds in ICOs.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.173-182
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• 2022

In this paper, we propose a machine learning model that can prevent fraudulent transactions in advance and interpret them using the XAI approach. For the experiment, we collected a real data set of 12,258 mobile phone sales posts from Joonggonara, a major domestic online C2C resale trading platform. Characteristics of the text corresponding to the post body were extracted using Doc2vec, dimensionality was reduced through PCA, and various derived variables were created based on previous research. To mitigate the data imbalance problem in the preprocessing stage, a complex sampling method that combines oversampling and undersampling was applied. Then, various machine learning models were built to detect fraudulent postings. As a result of the analysis, LightGBM showed the best performance compared to other machine learning models. And as a result of SHAP, if the price is unreasonably low compared to the market price and if there is no indication of the transaction area, there was a high probability that it was a fraudulent post. Also, high price, no safe transaction, the more the courier transaction, and the higher the ratio of 0 in the price also led to fraud.