• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.370-372
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• 2023

신용카드 부정 사용은 고객 및 기업의 신용과 재산에 막대한 손실을 미치고 있다. 이에 따라 금융사들은 이상금융거래탐지시스템을 도입하였으나 이상 거래 발생 여부를 지속적으로 모니터링하고 있기 때문에 시스템 유지에 많은 비용이 따른다. 따라서 본 논문에서는 컴퓨팅 리소스를 절약함과 동시에 성능 개선 효과를 보인 신용카드 이상 거래 탐지 알고리즘을 제안한다. CTGAN 을 활용하여 정상 거래와 이상 거래의 비율을 일부 완화하였고 XAI 기법인 SHAP 를 활용하여 유의미한 속성값을 선택하였다. 이것을 기반으로 LSTM Autoencoder를 사용하여 이상데이터를 탐지하였다. 그 결과 전통적인 비지도 학습 기법에 비해 제안 알고리즘이 우수한 성능을 보였음을 확인하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.692-693
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• 2023

ESG 경영이 중요해짐에 따라 기업의 분식 여부도 중요해졌다. 따라서 본 논문에서는 인공신경망과 랜덤포레스트를 활용하여 기업의 분식회계 여부를 판단 성능을 비교분석하고 그 유용성에 대해 평가하였다. 실제 기업 회계정보를 수집하여 실험을 수행하였고, 실험 결과 F1-Score 기준 랜덤포레스트의 RFECV 기법이 0.81로 분식 기업을, SMOTE 기법을 사용한 모델이 정상 기업을 탐지하였고 Accuracy 기준 랜덤포레스트의 RFECV 기법과 SMOTE 기법을 사용한 모델이 0.77로 가장 효과적인 탐지 성능을 보여주었다.

• The Journal of Society for e-Business Studies
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• v.23 no.4
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• pp.153-169
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• 2018

Previous e-financial anomalies analysis and detection technology collects large amounts of electronic financial transaction logs generated from electronic financial business systems into big-data-based storage space. And it detects abnormal transactions in real time using detection rules that analyze transaction pattern profiling of existing customers and various accident transactions. However, deep analysis such as attempts to access e-finance by insiders of financial institutions with large scale of damages and social ripple effects and stealing important information from e-financial users through bypass of internal control environments is not conducted. This paper analyzes the management status of e-financial security programs of financial companies and draws the possibility that they are allies in security control of insiders who exploit vulnerability in management. In order to efficiently respond to this problem, it will present a comprehensive e-financial security management environment linked to insider threat monitoring as well as the existing e-financial transaction detection system.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.1
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• pp.25-38
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• 2022

With the diversification of payment methods and games, related financial accidents are causing serious problems for users and game companies. Recently, game companies have introduced an Fraud Detection System (FDS) for game payment systems to prevent financial incident. However, FDS is ineffective and cannot provide major evidence based on judgment results, as it requires constant change of detection patterns. In this paper, we analyze abnormal transactions among payment log data of real game companies to generate related features. One of the unsupervised learning models, Autoencoder, was used to build a model to detect abnormal transactions, which resulted in over 85% accuracy. Using X-FDS (Explainable FDS) with XAI-SHAP, we could understand that the variables with the highest explanation for anomaly detection were the amount of transaction, transaction medium, and the age of users. Based on X-FDS, we derive an improved detection model with an accuracy of 94% was finally derived by fine-tuning the importance of features that adversely affect the proposed model.

• Information Systems Review
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• v.23 no.4
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• pp.45-67
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• 2021

As the transaction volume of the C2C second-hand market is growing, the number of frauds, which intend to earn unfair gains by sending products different from specified ones or not sending them to buyers, is also increasing. This study explores the model that can identify frauds in the online C2C second-hand market by examining the postings for transactions. For this goal, this study collected 145,536 field data from actual C2C second-hand market. Then, the model is built with the characteristics from postings such as the topic and the linguistic characteristics of the product description, and the characteristics of products, postings, sellers, and transactions. The constructed model is then trained by the machine learning algorithm XGBoost. The final analysis results show that fraudulent postings have less information, which is also less specific, fewer nouns and images, a higher ratio of the number and white space, and a shorter length than genuine postings do. Also, while the genuine postings are focused on the product information for nouns, delivery information for verbs, and actions for adjectives, the fraudulent postings did not show those characteristics. This study shows that the various features can be extracted from postings written in C2C second-hand transactions and be used to construct an effective model for frauds. The proposed model can be also considered and applied for the other C2C platforms. Overall, the model proposed in this study can be expected to have positive effects on suppressing and preventing fraudulent behavior in online C2C markets.

• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.2
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• pp.11-22
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• 2015

The almost of Web page has been gathered the personal information(Korean resident registration number, name, cell-phone number, home telephone number, E-mail address, home address, etc.) using the membership and log-in. The all most user of Web page are concerned for gathering of the personal information. I-pin is the alternative means of resident registration number and has been used during the last ten-year period in the internet. The accident of I-pin mass illegal issue was happened by hacker at February, 2015. In this paper, we analysis the problems of I-pin system about I-pin mass illegal issue accident and propose a improvement method of it. First, I-pin issue must be processed by the off-line of face certification in spite of user's inconvenience. Second, I-pin use must be made up through second certification of password or OTP. The third, the notification of I-pin use must be sent to the user by the text messaging service of cell-phone or the E-mail. The forth, I-pin must be used an alternative means of Korean resident registration number in Internet. The methods can reduce the problems of I-pin system.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.10
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• pp.502-510
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• 2007

Private Matching is a problem of computing the intersection of private datasets of two parties. One could envision the usage of private matching for Insurance fraud detection system, Do-not-fly list, medical databases, and many other applications. In 2004, Freedman et at. [1] introduced a probabilistic solution for this problem, and they extended it to malicious adversary model and multi-party computation. In this paper, we propose a new deterministic protocol for private matching with perfect correctness. We apply this technique to adversary models, achieving more reliable and higher speed computation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.4
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• pp.1471-1492
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• 2015

The rapid development of smartphone technologies have resulted in the evolution of mobile botnets. The implications of botnets have inspired attention from the academia and the industry alike, which includes vendors, investors, hackers, and researcher community. Above all, the capability of botnets is uncovered through a wide range of malicious activities, such as distributed denial of service (DDoS), theft of business information, remote access, online or click fraud, phishing, malware distribution, spam emails, and building mobile devices for the illegitimate exchange of information and materials. In this study, we investigate mobile botnet attacks by exploring attack vectors and subsequently present a well-defined thematic taxonomy. By identifying the significant parameters from the taxonomy, we compared the effects of existing mobile botnets on commercial platforms as well as open source mobile operating system platforms. The parameters for review include mobile botnet architecture, platform, target audience, vulnerabilities or loopholes, operational impact, and detection approaches. In relation to our findings, research challenges are then presented in this domain.

• Food Science of Animal Resources
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• v.37 no.4
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• pp.599-605
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• 2017

Korean native honey (KNH) is much more expensive than European honey (EH) in Korea, because KNH is a favored honey which is produced less than EH. Food fraud of KNH has drawn attention of the government office concerned, which is in need of a method to differentiate between KNH and EH which are produced by the Asiatic honeybee, Apis cerana and the European honeybee, Apis mellifera, respectively. A method to discriminate KNH and EH was established by using duplex polymerase chain reaction (PCR) in this study. Immunochromatographic assay (IC) was examined to analyze the duplex PCR product. The DNA sequences of primers for the duplex PCR were determined by comparing cytochrome C oxidase genes of the two honey bee species. Chelex resin method was more efficient in extracting genomic DNA from honey than the other two procedures of commercial kits. The duplex PCR amplifying DNA of 133 bp were more sensitive than that amplifying DNA of 206 bp in detecting EH in the honey mixture of KNH and EH. Agarose gel electrophoresis and IC detected the DNA of 133 bp at the ratios of down to 1% and 5% EH in the honey mixture, respectively and also revealed that several KNH products distributed by internet shopping sites were actually EH. In conclusion, the duplex PCR with subsequent IC could also discriminate between KNH and EH and save time and labor.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.7
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• pp.475-487
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• 2020

The paper aims to facilitate a discussion around how big data technologies and data from citizens can be used to help public administration, society, and policy-making to improve community's lives. This paper discusses opportunities and challenges of big data strategies for government, society, and policy-making. It employs the presentation of numerous practical examples from different parts of the world, where public-service delivery has seen transformation and where initiatives have been taken forward that have revolutionized the way governments at different levels engage with the citizens, and how governments and civil society have adopted evidence-driven policy-making through innovative and efficient use of big data analytics. The examples include the governments of the United States, China, the United Kingdom, and India, and different levels of government agencies in the public services of fraud detection, financial market analysis, healthcare and public health, government oversight, education, crime fighting, environmental protection, energy exploration, agriculture, weather forecasting, and ecosystem management. The examples also include smart cities in Korea, China, Japan, India, Canada, Singapore, the United Kingdom, and the European Union. This paper makes some recommendations about how big data strategies transform the government and public services to become more citizen-centric, responsive, accountable and transparent.