• Journal of Industrial Convergence
• /
• v.17 no.2
• /
• pp.1-7
• /
• 2019

The Kakao bank can be conveniently used if there are only smartphones, identity cards, and bank accounts. However, a few days before the inauguration of Kakao Bank, the company opened an account for receiving loans from other people. In order to avoid such cases, the financial transactions will be detected if the SDS is withdrawn at a short interval of time. The detection system of FDS has four functions which are monitoring and auditing, collection, analysis, and response. There are security problems of the cocoa banks in various directions. The Kakao bank has a way to respond to the problem using FDS.: Keywords : Cocoa bank, security issues, information protection, FDS

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.05a
• /
• pp.303-304
• /
• 2021

In the 21st century, as we enter the Fourth Industrial Revolution, research in various fields utilizing big data is being conducted, and innovative and useful technologies are constantly emerging in the world. Among several technologies recently in the big data era, among various fields utilizing some algorithms of artificial intelligence, it shines in the field of finance and is used for pin tech, financial fraud detection and risk management, etc., and recently Even in the booming stock market, it is used for investment prediction and investment factor analysis using artificial intelligence algorithm models. In this paper, we plan to investigate various research cases and investigate trends in how they are used in the stock market through artificial intelligence that utilizes big data.

• Korean Journal of Medicinal Crop Science
• /
• v.26 no.1
• /
• pp.26-31
• /
• 2018

Background: Angelica gigas, A. sinensis, and A. acutiloba are commercially important in the herbal medicine market, and among them, A. gigas has the highest economic value and price. However, their similar morphological traits are often used for fraud. Despite their importance in herbal medicine, recognition of the differences between Angelica species is currently inadequate. Methods and Results: A multiplex polymerase chain reaction (PCR) method was developed for direct detection and identification of A. gigas, A. sinensis, and A. acutiloba. The gene for the distinction of species was targeted at ITS in the nucleus and trnC-petN gene in chloroplasts. The optimized multiplex PCR in the present study utilized each Angelica species-specific primer pairs. Each primer pair yielded products of 229 base pairs (bp) for A. gigas, 53 bp for A. sinensis, 170 bp for A. acutiloba. Additionally non-specific PCR products were not detected in similar species by species-specific primers. Conclusions: In the present study, a multiplex-PCR assay, successfully assessed the authenticity of Angelica species (A. gigas, A. sinensis, and A. acutiloba). and whole genome amplification (WGA) was performed after DNA extraction to identify, the species in the product. The detection method of raw materials developed in the present study could be applied to herbal medicine and health functional food management.

• Journal of Advanced Navigation Technology
• /
• v.18 no.6
• /
• pp.569-575
• /
• 2014

Since 2009 the number of users of smart phones and tablet PC is growing exponentially. In particular Apple's iOS and Google's Android OS are the heart of this remarkable growth, most of smart phone and tablet PC are designed to operate based on these two OS. Such increasing use of smart devices has led to changes in the social environment that allows, without the constraints of time and place. However, such development does not supply only ease to do something, even compared to past, financial fraud and information leakage are easier than before by variety of new types of attack for example phishing, pharming, smishing and qshing. So according to this paper, analyzes for smishing attack, propose a countermeasure system of the technical way and proved its higher performance compare to the existing method.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.18 no.6
• /
• pp.876-880
• /
• 2008

Outliers are the observations which are very larger or smaller than most observations in the given data set. These are shown by some sources. The result of the analysis with outliers may be depended on them. In general, we do data analysis after removing outliers. But, in data mining applications such as fraud detection and intrusion detection, outliers are included in training data because they have crucial information. In regression models, simple and multiple regression models need to eliminate outliers from given training data by standadized and studentized residuals to construct good model. In this paper, we use support vector regression(SVR) based on statistical teaming theory to analyze data with outliers in regression. We verify the improved performance of our work by the experiment using synthetic data sets.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2015.10a
• /
• pp.763-765
• /
• 2015

The study is being conducted to ensure the transparency of research and development have identified the problems of the current system and improve the way out. Such a study about the subject that do not follow either outside the institutional system has a disadvantage compared to an unfulfilled. R & D in order to prevent the misuse and fraud enforcement shall detect abnormal transactions that occur from transactions between research institutions and credit card issuers in real time. In this paper, we propose a detection method for real-time transaction over. It is able to detect and respond fraudulent transactions that may occur in a variety of environments by adding the data obtained by the business rules to derive stopped making detection system.

• The Journal of Bigdata
• /
• v.5 no.1
• /
• pp.209-225
• /
• 2020

This study tried to establish a base level for the form of ginseng in order to prevent fraud in which novice consumers, who have no information on ginseng and mountain ginseng, regard ginseng as mountain ginseng. To that end, researchers designed a service design in which when a consumer takes a picture of ginseng with an APP dedicated to a smartphone, the photo is sent remotely and the determined results are sent to the consumer based on machine learning data. In order to minimize the difference between the data set in the research process and the background color, location, size, illumination, and color temperature of the mountain ginseng when consumers took pictures through their smartphones, the filming box exclusively for consumers was designed. Accordingly, the collection of mountain ginseng samples was made under the same controlled environment and setting as the designed box. This resulted in a 100% predicted probability from the CNN(VGG16) model using a sample that was about one-tenth less than widley required in machine learning.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.30 no.6
• /
• pp.1053-1065
• /
• 2020

With the recent development of hardware performance and artificial intelligence technology, sophisticated fake videos that are difficult to distinguish with the human's eye are increasing. Face synthesis technology using artificial intelligence is called Deepfake, and anyone with a little programming skill and deep learning knowledge can produce sophisticated fake videos using Deepfake. A number of indiscriminate fake videos has been increased significantly, which may lead to problems such as privacy violations, fake news and fraud. Therefore, it is necessary to detect fake video clips that cannot be discriminated by a human eyes. Thus, in this paper, we propose a deep-fake detection model applied with Bidirectional Convolution LSTM and Attention Module. Unlike LSTM, which considers only the forward sequential procedure, the model proposed in this paper uses the reverse order procedure. The Attention Module is used with a Convolutional neural network model to use the characteristics of each frame for extraction. Experiments have shown that the model proposed has 93.5% accuracy and AUC is up to 50% higher than the results of pre-existing studies.

• Journal of Digital Convergence
• /
• v.11 no.10
• /
• pp.217-224
• /
• 2013

Due to the development of mobile technologies, the carrier (telecommunications) billing service market is rapidly growing. carrier (telecommunications) billing service allows users to make on-line purchases through mobile-billing. Users find this particularly convenient because the payment acts as a credit transaction. Furthermore, the system is commonly believed to be secure through its use of SMS (Short Message Service) authentication and a real-time transaction history to confirm the transaction. Unfortunately, there is a growing number of fraudulent transactions threaten the future of this system. The more well documented types of security breaches involves hackers intercepting the authentication process. By contaminating the device with security breaching applications, hackers can secretly make transactions without notifying users until the end of month phone bill. This study sheds light on the importance of this societal threat and suggests solutions. In particular, "secure" systems need to be more proactive in addressing the methods hackers use to make fraudulent transactions. Our research partially covers specific methods to prevent fraudulent transactions on carrier billing service providers' systems. We discuss about the proposed improvements such as complement of electronic payment systems, active promotion for fraudulent transactions enhanced monitoring, fraud detection and introduce a new authentication service. This research supports a future of secure communications billing services, which is essential to expanding new markets.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.22 no.4
• /
• pp.851-868
• /
• 2012

After the financial crisis in 2008, the financial market still seems to be unstable with expanding the insolvency of the financial companies' real estate project financing loan in the aftermath of the lasted real estate recession. Especially after the illegal actions of people's financial institutions disclosed, while increased the anxiety of economic subjects about financial markets and weighted in the confusion of financial markets, the potential risk for the overall national economy is increasing. Thus as economic recession prolongs, the people's financial institutions having a weak profit structure and financing ability commit illegal acts in a variety of ways in order to conceal insolvent assets. Especially it is hard to find the loans of shareholder and the same borrower sharing credit risk in advance because most of them usually use a third-party's name bank account. Therefore, in order to effectively detect the fraud under other's name, it is necessary to analyze by clustering the borrowers high-related to a particular borrower through an analysis of association between the whole borrowers. In this paper, we introduce Analysis Techniques for detecting financial loan frauds in advance through an analysis of association between the whole borrowers by extending SNA(social network analysis) which is being studied by focused on sociology recently to the forensic accounting field of the financial frauds. Also this technique introduced in this pager will be very useful to regulatory authorities or law enforcement agencies at the field inspection or investigation.