Department of Computer Applications, Presidency College, Bangalore, India.
Department of Computer Science and Engineering, Excel Engineering College, Namakkal, India.
Comput Intell Neurosci. 2023 Jan 13;2023:2693022. doi: 10.1155/2023/2693022. eCollection 2023.
Credit card fraud has drastically increased in recent times due to the advancements in e-commerce systems and communication technology. Falsified credit card transactions affect the financial status of the companies as well as clients regularly and fraudsters incessantly try to develop new approaches to commit frauds. The recognition of credit card fraud is essential to sustain the trustworthiness of e-payments. Therefore, it is highly needed to design effective and accurate credit card fraud detection (CCFD) techniques. The recently developed machine learning (ML) and deep learning (DL) can be employed for CCFD because of the characteristics of building an effective model to identify fraudulent transactions. In this view, this study presents a novel oppositional cat swarm optimization-based feature selection model with a deep learning model for CCFD, called the OCSODL-CCFD technique. The major intention of the OCSODL-CCFD technique is to detect and classify fraudulent transactions using credit cards. The OCSODL-CCFD technique derives a new OCSO-based feature selection algorithm to choose an optimal subset of features. Besides, the chaotic krill herd algorithm (CKHA) with the bidirectional gated recurrent unit (BiGRU) model is applied for the classification of credit card frauds, in which the hyperparameter tuning of the BiGRU model is performed using the CKHA. To demonstrate the supreme outcomes of the OCSODL-CCFD model, a wide range of simulation analyses were carried out. The extensive comparative analysis highlighted the better outcomes of the OCSODL-CCFD model over the compared ones based on several evaluation metrics.
近年来，由于电子商务系统和通信技术的进步，信用卡欺诈急剧增加。伪造的信用卡交易经常影响公司和客户的财务状况，欺诈者不断试图开发新的方法来实施欺诈。识别信用卡欺诈对于维持电子支付的可信度至关重要。因此，设计有效的、准确的信用卡欺诈检测（CCFD）技术是非常必要的。最近开发的机器学习（ML）和深度学习（DL）可以应用于 CCFD，因为它们具有构建有效模型来识别欺诈性交易的特点。有鉴于此，本研究提出了一种新颖的基于反对猫群优化的特征选择模型与深度学习模型相结合的信用卡欺诈检测方法，称为 OCSODL-CCFD 技术。OCSODL-CCFD 技术的主要目的是使用信用卡检测和分类欺诈性交易。OCSODL-CCFD 技术衍生出一种新的基于 OCSO 的特征选择算法，以选择最佳的特征子集。此外，混沌磷虾群算法（CKHA）与双向门控循环单元（BiGRU）模型一起应用于信用卡欺诈的分类，其中使用 CKHA 对 BiGRU 模型的超参数进行调整。为了证明 OCSODL-CCFD 模型的卓越结果，进行了广泛的模拟分析。广泛的比较分析强调了 OCSODL-CCFD 模型在基于多个评估指标的比较模型中的更好结果。
