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1. DOS-GNN: Dual-Feature Aggregations with Over-Sampling for Class-Imbalanced Fraud Detection On Graphs

   https://doi.org/10.1109/IJCNN60899.2024.10650494  

   Jing, Shixiong; Chen, Lingwei; Li, Quan; Wu, Dinghao (June 2024, International Joint Conference on Neural Networks)

   As fraudulent activities have shot up manifolds, fraud detection has emerged as a pivotal process in different fields (e.g., e-commerce, online reviews, and social networks). Since interactions among entities provide valuable insights into fraudulent activities, such behaviors can be naturally represented as graph structures, where graph neural networks (GNNs) have been developed as prominent models to boost the efficacy of fraud detection. In graph-based fraud detection, handling imbalanced datasets poses a significant challenge, as the minority class often gets overshadowed, diminishing the performance of conventional GNNs. While oversampling has recently been adapted for imbalanced graphs, it contends with issues such as graph heterophily and noisy edge synthesis. To address these limitations, this paper introduces DOS-GNN, incorporating Dual-feature aggregation with Over-Sampling to advance GNNs for class-imbalanced fraud detection on graphs. This model exploits feature separation and dual-feature aggregation to mitigate the impact of heterophily and acquire refined node embeddings that facilitate fraud oversampling to balance class distribution without the need for edge synthesis. Extensive experiments on four large and real-world fraud datasets demonstrate that DOS-GNN can significantly improve fraud detection performance on graphs with different imbalance ratios and homophily ratios, outperforming state-of-the-art GNN models. 

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2. An Ensemble-Based Machine Learning for Predicting Fraud of Credit Card Transactions

   https://doi.org/10.1007/978-3-031-10464-0_14  

   Baabdullah, T.; Rawat, D.; Liu, C.; Alzahrani, A. (January 2022, Proceedings of 2022 Computing Conference, Lecture Notes in Networks and Systems)

   Recently, using credit cards has been considered one of the essential things of our life due to its pros of being easy to use and flexible to pay. The critical impact of the increment of using credit cards is the occurrence of fraudulent transactions, which allow the illegal user to get money and free goods via unauthorized usage. Artificial Intelligence (AI) and Machine Learning (ML) have become effective techniques used in different applications to ensure cybersecurity. This paper proposes our fraud detection system called Man-Ensemble CCFD using an ensemble-learning model with two stages of classification and detection. Stage one, called ML-CCFD, utilizes ten machine learning (ML) algorithms to classify credit card transactions to class 1 as a fraudulent transaction or class 0 as a legitimate transaction. As a result, we compared their classification reports together, precisely precision, recall (sensitivity), and f1-score. Then, we selected the most accurate ML algorithms based on their classification performance and prediction accuracy. The second stage, known Ensemble-learning CCFD, is an ensemble model that applies the Man-Ensemble method on the most effective ML algorithms from stage one. The output of the second stage is to get the final prediction instead of using common types of ensemble learning, such as voting, stacking, boosting, and others. Our framework’s results showed the effectiveness and efficiency of our fraud detection system compared to using ML algorithms individually due to their weakness issues, such as errors, overfitting, bias, prediction accuracy, and even their robustness level. 

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3. Beyond Bot Detection: Combating Fraudulent Online Survey Takers

   https://doi.org/10.1145/3485447.3512230  

   Zhang, Ziyi; Zhu, Shuofei; Mink, Jaron; Xiong, Aiping; Song, Linhai; Wang, Gang (April 2022, Proceedings of the Web Conference 2022)

   Different techniques have been recommended to detect fraudulent responses in online surveys, but little research has been taken to systematically test the extent to which they actually work in practice. In this paper, we conduct an empirical evaluation of 22 antifraud tests in two complementary online surveys. The first survey recruits Rust programmers on public online forums and social media networks. We find that fraudulent respondents involve both bot and human characteristics. Among different anti-fraud tests, those designed based on domain knowledge are the most effective. By combining individual tests, we can achieve a detection performance as good as commercial techniques while making the results more explainable. To explore these tests under a broader context, we ran a different survey on Amazon Mechanical Turk (MTurk). The results show that for a generic survey without requiring users to have any domain knowledge, it is more difficult to distinguish fraudulent responses. However, a subset of tests still remain effective. 

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4. Using Disposable Domain Names to Detect Online Card Transaction Fraud

   https://doi.org/10.1109/ICC.2019.8761144  

   Laurens, Roy; Rezaeighaleh, Hossein; Zou, Cliff C.; Jusak, Jusak (May 2019, ICC 2019 - 2019 IEEE International Conference on Communications (ICC))

   Online card transaction fraud is one of the major threats to the bottom line of E-commerce merchants. In this paper, we propose a novel method for online merchants to utilize disposable (“one-time use”) domain names to detect client IP spoofing by collecting client's DNS information during an E-commerce transaction, which in turn can help with transaction fraud detection. By inserting a dynamically generated unique hostname on the E-commerce transaction webpage, a client will issue an identifiable DNS query to the customized authoritative DNS server maintained by the online Merchant. In this way, the online Merchant is able to collect DNS configuration of the client and match it with the client's corresponding transaction in order to verify the consistency of the client's IP address. Any discrepancy can reveal proxy usage, which fraudsters commonly use to spoof their true origins. We have deployed our preliminary prototype system on a real online merchant and successfully collected clients DNS queries correlated with their web transactions; then we show some real instances of successful fraud detection using this method. We also address some concerns regarding the use of disposable domains. 

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5. Deep Structure Learning for Fraud Detection

   https://doi.org/10.1109/ICDM.2018.00072  

   Wang, Haibo; Zhou, Chuan; Wu, Jia; Dang, Weizhen; Zhu, Xingquan; Wang, Jilong (November 2018, Proc. of the 18th IEEE International Conference on Data Mining (ICDM- 2018))

   Fraud detection is of great importance because fraudulent behaviors may mislead consumers or bring huge losses to enterprises. Due to the lockstep feature of fraudulent behaviors, fraud detection problem can be viewed as finding suspicious dense blocks in the attributed bipartite graph. In reality, existing attribute-based methods are not adversarially robust, because fraudsters can take some camouflage actions to cover their behavior attributes as normal. More importantly, existing structural information based methods only consider shallow topology structure, making their effectiveness sensitive to the density of suspicious blocks. In this paper, we propose a novel deep structure learning model named DeepFD to differentiate normal users and suspicious users. DeepFD can preserve the non-linear graph structure and user behavior information simultaneously. Experimental results on different types of datasets demonstrate that DeepFD outperforms the state-of-the-art baselines. 

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