• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.161-177
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• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.

• Proceedings of the CALSEC Conference
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• 2005.11a
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• pp.85-96
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• 2005

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.469-471
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• 2023

본 논문에서는 기존 중고거래 플랫폼들의 불편한 점들을 해소하고 사용자들이 효율적이고 편리한 중고거래를 할 수 있도록 도와주는 플랫폼을 개발했다. 조사를 통해 기존 중고거래 플랫폼은 허위 매물, 시세 파악의 어려움, 사기 피해 등의 문제점이 존재한다는 사실을 인식했다. 문제 해결을 위해 파이썬을 활용하여 주요 중고거래 플랫폼의 상품 데이터를 수집했다. 이에 IQR을 적용하여 가격의 이상치를 판별했다. 가격 비교와 허위 매물 판별이 용이하게 되는 장점이 있다. 또한 이상치를 제거한 상품들의 시세를 계산하여 데이터를 차트로 시각화했다. 플랫폼과 지역마다 상이한 중고 상품의 신뢰성 있는 시세를 파악할 수 있고 중고거래 사기 피해를 방지할 수 있도록 사용자에게 주요 사기 수법, 뉴스 등의 정보를 제공한다.

• Journal of Intelligence and Information Systems
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• v.15 no.1
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• pp.65-85
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• 2009

The main purpose of this paper is to propose a sophistication framework for a global manufacturing network (GMN) driven by a mother company to autonomously propagate and coordinate transaction data that are exchanged among manufacturing partners. The framework is based on conceptual fundamentals of previous research that provide a step toward ultimate successful collaboration in the supply chain and employs mobile agents for the coordination and propagation of transaction data. Maintaining the integrity of transaction data linked to a huge information web is difficult. With the sophistication functionalities of this framework, it becomes easy to effectively control the overall GMN operations and to accomplish the intended goals. The current level of sophistication focuses on the transaction data propagation. The sophistication level may be expanded up to business intelligence in the future.

• Journal of Energy Engineering
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• v.27 no.4
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• pp.86-91
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• 2018

This paper is a review of accounts balancing time in small distributed power trading platform using blockchain technology. First, the national VPP energy management system using the AMI applied to this study is introduced and then the accounts balancing time and process of the cryptocurrency coin payment which based on the power generation of pro-consumer certified by power big data analysis in a test bed environment is discussed. Futhermore the configuration of a power Big Data analysis system with GPU Fast Big Data that applies MapD to current lambda architecture is also introduced.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.609-615
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• 2022

In this paper, we propose a monitoring system for illegal overseas direct purchase based on C2C transaction between individuals. The Customs Act stipulates that direct purchases from overseas are exempted from taxation only if they are less than a certain amount (US$150, but US$200 in the US) or are recognized as self-used goods. The act of reselling overseas direct purchase items purchased with exemption from taxation online, etc., is a crime of smuggling without a report. Nevertheless, the number of re-sells on online second-hand websites is increasing, and it is becoming a controversial social issue of continuous violation of the Customs Act. Therefore, this study collects unspecified transaction details related to overseas direct purchase, refines the data in a big data method, and designs it as a monitoring system through natural language processing, etc. analyzed. It will be possible to use it to crack down on illegal transactions of overseas direct purchase goods.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.65-77
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• 2022

Due to the recent development in electronic financial services, transactions of electronic prepayment are rapidly growing, leading to growing fraud attempts. This paper proposes a methodology that can effectively detect fraud transactions in electronic prepayment by machine learning algorithms, including support vector machines, decision trees, and artificial neural networks. Actual transaction data of electronic prepayment services were collected and preprocessed to extract the most relevant variables from raw data. Two different approaches were explored in the paper. One is a transaction-based approach, and the other is a user ID-based approach. For the transaction-based approach, the first model is primarily based on raw data features, while the second model uses extra features in addition to the first model. The user ID-based approach also used feature engineering to extract and transform the most relevant features. Overall, the user ID-based approach showed a better performance than the transaction-based approach, where the artificial neural networks showed the best performance. The proposed method could be used to reduce the damage caused by financial accidents by detecting and blocking fraud attempts.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 1998.12a
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• pp.123-135
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• 1998

현재 많은 관심을 받고 있는 전자상거래는 앞으로 더욱 활성화되어질 전망이다. 전자상거래는 그 특성상 디지털 데이터와 같은 소액 거래가 쉽게 이루어질 수 있다. 소액 거래는 비록 거래 비용은 적지만 그 응용 분야가 넓기 때문에 중요성을 가진다. 이러한 소액 거래를 위해서는 처리비용이 적어야만 한다. 소액 거래를 위한 많은 프로토콜이 연구되고 있는데, 본 고에서는 지금까지 개발된 대표적인 소액지분 프로토콜을 살펴보고 기존의 소액지분 프로토콜에서 대부분 지원하지 않았던 익명성을 부여한 소액지불 프로토콜을 제안한다.

• Proceedings of the CALSEC Conference
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• 2002.01a
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• pp.362-366
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• 2002

일반적 정의 :"온라인 네트워크를 통해 이뤄지는 모든 형태의 거래" OECD(1997):"전자상거래는 일반적으로 개인과 조직 모두를 포함해 텍스트, 음성 화상을 포함한 디지털데이터의 처리와 전송에 기초한 상업활동과 관련된 모든 종류의 거래" 경제주체에 따른 EC의 분류 : 기업 대 기업(Business to Business), 기업 대 소비자(Business to Consumer), 소비자 대 소비자(Consumer to Consumer), 정부 대 기업(Government to Business), 정부 대 소비자(Government to Consumer), 기업 대 딜러간(Business to Dealer), 인터네 비즈니스 사이트 대 사이트(Site to Site) Mobile Commerce의 정의 일반적 정의 : 휴대폰, PDA, 노트북 등의 개인 휴대 단말기와 무선 통신네트웍을 기반으로 한 재화(Goods), 용역(Service), 정보(Information) 및 디지털 컨텐츠 등의 모든 전자적 거래(중략)

• Proceedings of the Korea Database Society Conference
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• 2000.11a
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• pp.53-60
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• 2000

본 연구에서는 인터넷 비즈니스를 통해 획득할 수 있는 수익을 규명하고, 사례조사를 통하여 거래유형별로 나타나는 수익구조의 차이를 제시한다. 거래의 유형에 따라 사례연구를 통해 수익구조를 살펴본 결과는 거래유형에 따라 수익구조가 서로 다르게 나타나고 있음을 보이고 있다. 이는 거래유형별로 수익원이 각기 다름을 의미하며 비즈니스 모델별로 적합한 수익구조를 구성할 수 있음을 의미한다. 그리고 수익구조를 구성하고 있는 수익들간의 관계를 분석함으로써 획득하고자 하는 수익과 그에 선행하여 획득하여야 하는 수익을 연결시켰다. 이와 같은 결과를 통해 인터넷 비즈니스를 전개하고 있는 기업에서는 획득하고자 하는 수익과 그에 필요한 적절한 수익구조를 구성하는데 가이드라인으로 사용될 수 있을 것이다.