Chung Jiwon, Lee Kyungho
School of Cybersecurity, Korea University, Seoul 02841, Republic of Korea.
Sensors (Basel). 2023 Sep 10;23(18):7788. doi: 10.3390/s23187788.
Efficiently and accurately identifying fraudulent credit card transactions has emerged as a significant global concern along with the growth of electronic commerce and the proliferation of Internet of Things (IoT) devices. In this regard, this paper proposes an improved algorithm for highly sensitive credit card fraud detection. Our approach leverages three machine learning models: K-nearest neighbor, linear discriminant analysis, and linear regression. Subsequently, we apply additional conditional statements, such as "IF" and "THEN", and operators, such as ">" and "<", to the results. The features extracted using this proposed strategy achieved a recall of 1.0000, 0.9701, 1.0000, and 0.9362 across the four tested fraud datasets. Consequently, this methodology outperforms other approaches employing single machine learning models in terms of recall.
随着电子商务的发展和物联网（IoT）设备的激增，高效准确地识别信用卡欺诈交易已成为全球关注的重要问题。在这方面，本文提出了一种用于高灵敏度信用卡欺诈检测的改进算法。我们的方法利用了三种机器学习模型：K近邻、线性判别分析和线性回归。随后，我们对结果应用额外的条件语句，如“IF”和“THEN”，以及运算符，如“>”和“<”。使用此提议策略提取的特征在四个测试欺诈数据集中的召回率分别达到了1.0000、0.9701、1.0000和0.9362。因此，该方法在召回率方面优于其他采用单一机器学习模型的方法。
