• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.4
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• pp.307-317
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• 2010

The majority of financial and general business organizations have had individual damage from hacking, worms, viruses, cyber attacks, internet fraud, technology and information leaks due to criminal damage. Therefore privacy has become an important issue in the community. This paper examines various elements of the information security management system and discuss about Information Security Management System Models by using the analysis of the financial statue and its level of information security assessment. These analyses were based on the Information Security Management System (ISMS) of Korea Information Security Agency, British's ISO27001, GMITS, ISO/IEC 17799/2005, and COBIT's information security architecture. This model will allow users to manage and secure information safely. Therefore, it is recommended for companies to use the security management plan to improve the companies' financial and information security and to prevent from any risk of exposing the companies' information.

• Journal of Information Technology Application
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• v.1
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• pp.173-211
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• 1999

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.

• International Journal of Computer Science & Network Security
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• v.24 no.11
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• pp.48-54
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• 2024

Recent advancements in e-payment and e-commerce methods have resulted in rising the quantity of credit card transactions that are fraudulent, which cause significant massive financial losses and become a potential security issue. There is an urgent need for efficient methods for identifying fraudulent credit card transactions. This paper presents an effective ensemble learning technique that utilizes the grid search optimization approach for identifying credit card fraud. The suggested approach consists of two phases. First, base learners consist of multiple machine learning classifiers, including Decision Tree (DT), K-nearest neighbor (KNN), AdaBoost (ADA), Gradient Boosting (GB) and Logistic Regression (LR), are utilized to find the fraudulent transactions probabilities. Second, a meta learner that integrates the Random Forest with the Grid Search (RF-GS) is applied to categorize the probabilities of predictions produced by the base learners. RF-GS uses the Grid Search (GS) optimization technique to tune the parameters of Random Forest (RF) method, to get the maximum credit card fraud detection accuracy. A real-world dataset was utilized to evaluate the effectiveness of the suggested approach. The findings of the experiment show the effectiveness of the suggested optimized ensemble-learning strategy for identifying the fraudulent credit card transactions, which performed better than the other approaches and obtained superior accuracy of 99.01%.

• Journal of Information Processing Systems
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• v.20 no.3
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• pp.391-398
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• 2024

Given the further promotion of economic globalization, China's financial market has also expanded. However, at present, this market faces substantial risks. The main financial and economic risks in China are in the areas of policy, credit, exchange rates, accounting, and interest rates. The current status of China's financial market is as follows: insufficient attention from upper management; insufficient innovation in the development of the financial economy; and lack of a sound financial and economic risk protection system. To further understand the current situation of China's financial market, we conducted a questionnaire survey on the financial market and reached the following conclusions. A comprehensive enterprise questionnaire from the government's perspective, the enterprise's perspective and the individual's perspective showed that the following problems exist in the financial and economic risk prevention aspects of big data and Internet of Things in China. The political system at the country's grassroots level is not comprehensive enough. The legal regulatory system is not comprehensive enough, leading to serious incidents of loan fraud. The top management of enterprises does not pay enough attention to financial risk prevention. Therefore, we constructed a financial and economic risk prevention model based on big data and Internet of Things that has effective preventive capabilities for both enterprises and individuals. The concept reflected in the model is to obtain data through Internet of Things, use big data for screening, and then pass these data to the big data analysis system at the grassroots level for analysis. The data initially screened as big data are analyzed in depth, and we obtain the original data that can be used to make decisions. Finally, we put forward the corresponding opinions, and their main contents represent the following points: the key is to build a sound national financial and economic risk prevention and assessment system, the guarantee is to strengthen the supervision of national financial risks, and the purpose is to promote the marketization of financial interest rates.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.1
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• pp.133-146
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• 2015

Ever sophisticated e-finance fraud techniques have led to an increasing number of reported phishing incidents. Financial authorities, in response, have recommended that we enhance existing Fraud Detection Systems (FDS) of banks and other financial institutions. FDSs are systems designed to prevent e-finance accidents through real-time access and validity checks on client transactions. The effectiveness of an FDS depends largely on how fast it can analyze and detect abnormalities in large amounts of customer transaction data. In this study we detect fraudulent transaction patterns and establish detection rules through e-finance accident data analyses. Abnormalities are flagged by comparing individual client transaction patterns with client profiles, using the ruleset. We propose an effective flagging method that uses decision trees to normalize detection rules. In demonstration, we extracted customer usage patterns, customer profile informations and detection rules from the e-finance accident data of an actual domestic(Korean) bank. We then compared the results of our decision tree-normalized detection rules with the results of a sequential detection and confirmed the efficiency of our methods.

• Korean Security Journal
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• no.62
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• pp.185-203
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• 2020

While overall crime has been on the decline since 2009, voice phishing has rather been on the rise. The government and academia have presented various measures and conducted research to eradicate it, but it is not enough to catch up with evolving voice phishing. In the study, researchers focused on catching criminals and preventing damage from voice phishing, which is difficult to recover from. In particular, a voice phishing prediction method using the Fraud Detection System (FDS), which is being used to detect financial fraud, was studied based on the fact that the victim engaged in financial transaction activities (such as account transfers). As a result, it was conceptually derived to combine big data such as call details, messenger details, abnormal accounts, voice phishing type and 112 report related to voice phishing in machine learning-based Fraud Detection System(FDS). In this study, the research focused mainly on government measures and literature research on the use of big data. However, limitations in data collection and security concerns in FDS have not provided a specific model. However, it is meaningful that the concept of voice phishing responses that converge FDS with the types of data needed for machine learning was presented for the first time in the absence of prior research. Based on this research, it is hoped that 'Voice Phishing Damage Prediction System' will be developed to prevent damage from voice phishing.

• The Journal of the Convergence on Culture Technology
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• v.4 no.1
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• pp.193-200
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• 2018

This study is an experiential case study of cybercrime fraud that combines pharming and voice phishing in April 2017. Research on victims who have actually suffered in the study of crime or disaster is a very useful field in establishing crime prevention measures. This study is significant in that Korea is relatively poor in this kind of research. I got cyber fraud as a consequence of my loss of reasonable judgment due to mental confusion when a companion dog who was raised for 8 years was in a very dangerous situation with cystitis. Fortunately, I received all the damages in a quick report, but the period was eight months. It took too much time to get back all the damages, so I had to suffer another pain. Based on my experience, I suggest damage prevention measures. First, when a certain condition and a certain amount are transferred, the transaction is automatically stopped or a more strict confirmation procedure is added. Secondly, trafficking means to arrest the perpetrator without any harm to the victim is sought. Third, the victims of crime should be promptly reimbursed for damages or a system for lending their living funds to zero or lower interest rate.

• The Journal of Industrial Distribution & Business
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• v.12 no.12
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• pp.1-10
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• 2021

Purpose: Cyber criminals have affected various markets and the banking system has encountered various kinds of cyberattacks. The purpose of this study is to analyze cybercrime that is an emerging threat and investigate the significant contribution of financial literacy and public awareness on cybercrimes. To understand the security issues and the need for corrective steps, the techniques and strategies used by cyber fraudsters in obtaining unauthorized access and use the financial information for purpose of fraud need to be understood. Research design, data and methodology: A sample of 123 banks employees from 12 commercial banks in Malaysia was surveyed. This study differs from previous studies as it surveyed the employees' awareness, and this approach fills in the gap in existing literature. Results: The financial literacy and public awareness have positive impact on organizational performance effectiveness to combat threat of cybercrime. Some recommendations are also proposed from research findings, for banking industry and government regulations. Conclusion: The present study focuses on banking sector so its findings cannot be generalized to other sectors. Linking these topics has created a new study in combating threat of cybercrimes generally, and specifically in Malaysia. The present study enhances the understanding of customers' role to combat the impact of cybercrimes on performances of banking industry.

• Proceedings of the CALSEC Conference
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• 2002.01a
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• pp.238-241
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• 2002

·전자상거래: 서로 보지 않고 하는 거래 온라인 범죄(Online Fraud)급증 90％ of information security managers have detected breaches at their organizations within a year 74％ of companies have experienced financial losses because of cybercrime price tag on e-security breaches:>$17 billion worldwide in 2000(source: CIO Magazine, March 2001) ·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)

• 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.