• 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.

• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.908-915
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• 2023

Purpose: The purpose of this study is to examine the types of fraudulent crimes among various crimes taking place during NFT transactions, and to approach institutional problems caused by crime types analysis and crackdown methods and legal limitations. Method: IIn order to classify the types of fraudulent crimes that appear in NFT transactions, the crime types were analyzed through the results of previous studies and cases of current incidents. Result: Most of the crimes that are taking place through NFTs are various types of fraudulent crimes such as rug pools, thefts, personal information theft fraud, and pig murder. Therefore, these types were classified and various damage cases were also analyzed. It is a matter of copyright. Conclusion: Currently, the financial problems caused by the occurrence of fraudulent crimes in NFTs worldwide are the most worrisome, and the scale will be even greater as the market grows in the future. Therefore, in Korea, various institutional supplements and policies should be prepared through analysis of crime types that can affect crime prevention and investigation and arrest activities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3723-3737
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• 2022

Google Play is one of the largest Android phone app markets and it contains both free and paid apps. It provides a variety of categories for every target user who has different needs and purposes. The customer's rate every product based on their experience of apps and based on the average rating the position of an app in these arch varies. Fraudulent behaviors emerge in those apps which incorporate search rank maltreatment and malware proliferation. To distinguish the fraudulent behavior, a novel framework is structured that finds and uses follows left behind by fraudsters, to identify both malware and applications exposed to the search rank fraud method. This strategy correlates survey exercises and remarkably joins identified review relations with semantic and behavioral signals produced from Google Play application information, to distinguish dubious applications. The proposed model accomplishes 90% precision in grouping gathered informational indexes of malware, fakes, and authentic apps. It finds many fraudulent applications that right now avoid Google Bouncers recognition technology. It also helped the discovery of fake reviews using the reviewer relationship amount of reviews which are forced as positive reviews for each reviewed Google play the android app.

• Journal of Digital Convergence
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• v.11 no.10
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• pp.217-224
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• 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
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• v.25 no.3
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• pp.615-625
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• 2015

This paper proposes new methods and examples for improving fraud detection rules based on banking customer's transaction behaviors focused on anomaly detection method. This study investigates real example that FDS(Fraud Detection System) regards fraudulent transaction as legitimate transaction and figures out fraudulent types and transaction patterns. To understanding the cases that FDS regard legitimate transaction as fraudulent transaction, it investigates all transactions that requied additional authentications or outbound call. We infered additional facts to refine detection rules in progress of outbound calling and applied to existing detection rules to improve. The main results of this study is the following: (a) Type I error is decreased (b) Type II errors are also decreased. The major contribution of this paper is the improvement of effectiveness in detecting fraudulent transaction using transaction behaviors and providing a continuous method that elevate fraud detection rules.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.29-36
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• 2013

To accommodate the rapid growth of e-commerce transactions, non-face-to-face transactions, businesses use a wide variety of payment methods. However, many of these payment mediums are not secure as shown by increases in fraudulent transactions. In this paper, we analyze a particular e-commerce transaction medium, the Safety Transaction Service (STS). This system protects consumers through a wide variety of safeguards: safety settlement systems (escrow), consumer damage compensation insurance, payment guarantee, and secure bank settlement. In contrast to the safeguards, we identify the limitations and concerns with the STS and potential legal and political improvements. The plethora of payment methods limits the consumers ability to distinguish between the secured and unsecured transaction services. Regulation and consumer based verification of transaction services are essential to root out dangerously fraudulent systems. We propose the development of specific standards to these systems, in particular the need for consumer confirmation and clear settlement documentation. Only through the active promotion of scrutiny and improvement to STS will consumers be protected in e-commerce.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.5
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• pp.113-130
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• 2011

Due to the development of information and communication technology, the global influence on politics, economics, society, and culture has grown. A major example of this impact on the economic sector is the growth of e-commerce, which increases both the speed and efficiency of businesses. In light of these new developments, businesses need to shift away from the misconception that information overwhelms to embrace the enhanced competitiveness that e-commerce provides. However, concern about fraudulent transactions through e-commerce is pertinent because of the loss in both critical revenue and consumer confidence in open markets. Current solutions for fraudulent transactions include real-time monitoring and processing, payment pending, and confirmation through SMS, E-mail, and other wired means. Our research focuses on the management of Fraud Detection Systems (FDS) to safeguard online electronic payment systems. With effective implementation of our research we hope to foster an honorable online trading culture and protect consumers. Future comparative research in domestic and abroad markets would provide further insight into preventing fraudulent transactions.

• 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 the Korea Society of Computer and Information
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• v.27 no.3
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• pp.173-182
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• 2022

In this paper, we propose a machine learning model that can prevent fraudulent transactions in advance and interpret them using the XAI approach. For the experiment, we collected a real data set of 12,258 mobile phone sales posts from Joonggonara, a major domestic online C2C resale trading platform. Characteristics of the text corresponding to the post body were extracted using Doc2vec, dimensionality was reduced through PCA, and various derived variables were created based on previous research. To mitigate the data imbalance problem in the preprocessing stage, a complex sampling method that combines oversampling and undersampling was applied. Then, various machine learning models were built to detect fraudulent postings. As a result of the analysis, LightGBM showed the best performance compared to other machine learning models. And as a result of SHAP, if the price is unreasonably low compared to the market price and if there is no indication of the transaction area, there was a high probability that it was a fraudulent post. Also, high price, no safe transaction, the more the courier transaction, and the higher the ratio of 0 in the price also led to fraud.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.31-40
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• 2021

The increasing number of credit card fraud cases has become a considerable problem since the past decades. This phenomenon is due to the expansion of new technologies, including the increased popularity and volume of online banking transactions and e-commerce. In order to address the problem of credit card fraud detection, a rule-based approach has been widely utilized to detect and guard against fraudulent activities. However, it requires huge computational power and high complexity in defining and building the rule base for pattern matching, in order to precisely identifying the fraud patterns. In addition, it does not come with intelligence and ability in predicting or analysing transaction data in looking for new fraud patterns and strategies. As such, Data Mining and Machine Learning algorithms are proposed to overcome the shortcomings in this paper. The aim of this paper is to highlight the important techniques and methodologies that are employed in fraud detection, while at the same time focusing on the existing literature. Methods such as Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), naïve Bayesian, k-Nearest Neighbour (k-NN), Decision Tree and Frequent Pattern Mining algorithms are reviewed and evaluated for their performance in detecting fraudulent transaction.