• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.5
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• pp.1179-1195
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• 2018

Recently, a variety of attacks on web services have been occurring through a multiple access environment such as PC, tablet, and smartphone. These attacks are causing various subsequent damages such as online fraud transactions, takeovers and theft of accounts, fraudulent logins, and information leakage through web service vulnerabilities. Creating a new fake account for Fraud attacks, hijacking accounts, and bypassing IP while using other usernames or email addresses is a relatively easy attack method, but it is not easy to detect and block these attacks. In this paper, we have studied a method to detect online fraud transaction and obsession by identifying and managing devices accessing web service using web-based device fingerprinting. In particular, it has been proposed to identify devices and to manage them by scoring process. In order to secure the validity of the proposed scheme, we analyzed the application cases and proved that they can effectively defend against various attacks because they actively cope with online fraud and obtain visibility of user accounts.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.163-171
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• 2022

Online services are widely spread, and their use increases day by day. As COVID-19 spread and people spent much time online, fraud scams have risen unexpectedly. Manipulation techniques have become more effective at swindling those lacking basic technological knowledge. Unfortunately, a user needs a quorum. The interest in preventing scammers from obtaining effective quality service has become the most significant obstacle, increasing the variety of daily Internet platforms. This paper is concerned with analyzing purchase data and extracting provided results. In addition, after examining relevant documents presenting research discussing them, the recommendation was made that future work avoids them; this would save a lot of effort, money, and time. This research highlights many problems a person may face in dealing with online institutions and possible solutions to the epidemic through theft operations on the Internet.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.10a
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• pp.68-98
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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 are decreased. One of fraud is phantom transaction which is a colluding transaction by the buyer and seller to commit illegal discounting of credit card. They pretend to fulfill the transaction paid by credit card, without actual selling products, and the seller receives cash from credit card corporations. Then seller lends it out buyer with quite high interest rate whose credit score is so bad that he cannot borrow money from anywhere. The purpose of this study is to empirically investigate the factors to detect of the phantom transaction in online auction. Based up on the studies that explored behaviors of buyers and sellers in online auction, bidding numbers, bid increments, sellers' credit, auction length, and starting bids were suggested as independent variables. We developed an Internet-based data collection software agent and collect data on transactions of notebook computers each of which winning bid was over 1,000,000 won. Data analysis with logistic regression model revealed that starting bids, sellers' credit, and auction length were significant in detecting the phantom transaction.

• The Journal of Society for e-Business Studies
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• v.20 no.2
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• pp.141-153
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• 2015

In recent days, internet appears to be a daily necessity, which leads to the increase of internet fraud. This study particularly focuses upon C2C internet fraud, while other researches on internet fraud seldom touch such a topic. The aim of this study is to grasp the stark reality of the soaring C2C online fraud, to categorize the type of the frauds, to identify the root causes of the fraud, and to suggest security measures to prevent the C2C online fraud.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.35-40
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• 2022

With the advent of the internet age and the outbreak of COVID-19, many companies have embraced online trade. However, due to the way the cyber economy works, the number of companies engaged in financial fraud by falsifying their transaction amounts and customer numbers has been gradually increasing. The purpose of this study is to analyze financial fraud of companies in the Internet era and to present solutions. Therefore, this study analyzed the financial fraud behavior of Luckin Coffee in China as an example and studied the causes and countermeasures of financial fraud. As a result, it was found that the cause of financial fraud lies in the opacity of cash flows from online transactions. The recommendations proposed by this study is to improve internal control systems in companies, develop risk management system, and establish comprehensive external supervision system

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.200-206
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• 2021

Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.1006-1027
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• 2022

In the crowdfunding market, various crowdfunding platforms can offer founders the possibilities to collect funding and launch someone's next campaign, project or events. Especially, healthcare crowdfunding is a field that is growing rapidly on health-related problems based on online platforms. One of the largest platforms, GoFundMe, has raised US$ 5 billion since 2010. Unfortunately, while providing crucial help to care for many people, it is also increasing risk of fraud. Using the largest platform of crowdfunding market, GoFundMe, we conduct an exhaustive search of detection on fraud from October 2016 to September 2019. Data sets are based on 6 main types of medical focused crowdfunding campaigns or events, such as cancer, in vitro fertilization (IVF), leukemia, health insurance, lymphoma and, surgery type. This study evaluated a detect of fraud process to identify fraud from non-fraud healthcare crowdfunding campaigns using various machine learning technics.

• 정보보호뉴스
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• s.136
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• pp.40-43
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• 2009

• Smart Media Journal
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• v.12 no.2
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• pp.36-41
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• 2023

With the advent of applicant tracking systems, online recruitment has become more popular, and recruitment fraud has become a serious problem. This research aims to develop a reliable model to detect recruitment fraud in online recruitment environments to reduce cost losses and enhance privacy. The main contribution of this paper is to provide an automated methodology that leverages insights gained from exploratory analysis of data to distinguish which job postings are fraudulent and which are legitimate. Using EMSCAD, a recruitment fraud dataset provided by Kaggle, we trained and evaluated various single-classifier and ensemble-classifier-based machine learning models, and found that the ensemble classifier, the random forest classifier, performed best with an accuracy of 98.67% and an F1 score of 0.81.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.1-10
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• 2024

In the era of big data, the growth of e-commerce transactions brings forth both opportunities and risks, including the threat of data theft and fraud. To address these challenges, an automated real-time fraud detection system leveraging machine learning was developed. Four algorithms (Decision Tree, Naïve Bayes, XGBoost, and Neural Network) underwent comparison using a dataset from a clothing website that encompassed both legitimate and fraudulent transactions. The dataset exhibited an imbalance, with 9.3% representing fraud and 90.07% legitimate transactions. Performance evaluation metrics, including Recall, Precision, F1 Score, and AUC ROC, were employed to assess the effectiveness of each algorithm. XGBoost emerged as the top-performing model, achieving an impressive accuracy score of 95.85%. The proposed system proves to be a robust defense mechanism against fraudulent activities in e-commerce, thereby enhancing security and instilling trust in online transactions.