The credit/debit card deceit detection is an enormously difficult task. However, it is a well known problem of our cloud based mobile internet society and it must be solved by technocrats in the welfare of societal mental harassments. The main problem in executing credit/debit card fraud detection technique is the availability of limited amount of fraud related data like transaction amount, transaction date, transaction time, address, and vendor category code related to the frauds. It is the truth of mobile internet world that there are billions of potential places and e-commerce websites where a credit/debit card can be used by fraudulent people for online transactions and payments which make it exceedingly thorny to trace the pattern of frauds. Moreover, the problem of fraud detection in cloud— Internet of Things (IoT) based smart societies has numerous constraints like continuous change in the behavior of normal and fraudulent persons, the fraudulent people try to develop and use new method for executing frauds, and very little availability of frauds related bench mark data sets. In this research article, the authors have presented logistic regression based k-fold machine learning technique (MLT) for fraud detection and prevention in cloud-IoT based smart societal environment. The k-fold method creates multiple folds of bank transactions related data before implementing logistic regression and MLT. The logistic regression performs logic based regression analysis and the intelligent machine learning approach performs registration, classification, clustering, dimensionality reduction, deep learning, training, and reinforcement learning steps on the received bank transactions data. The implementation of proposed methodology and its further analysis using intelligent machine learning tools like ROC (Receiver Operating Characteristic) curve, confusion matrix, mean-recall score value, and precision recall curves for European banks day-to-day transactions related bench mark data set reveal that the proposed methodology is efficient, accurate, and reliable for detecting frauds in cloud-IoT based smart societal environment.

信用卡/借记卡欺骗检测是非常困难的任务。但是，这是我们基于云的移动互联网社会的一个众所周知的问题，必须由技术专家解决社会心理骚扰的问题。执行信用卡/借记卡欺诈检测技术的主要问题是有限数量的欺诈相关数据的可用性，例如与欺诈相关的交易量，交易日期，交易时间，地址和供应商类别代码。移动互联网世界的真相是，存在数十亿个潜在场所和电子商务网站，欺诈者可以使用信用卡/借记卡进行在线交易和付款，这使跟踪欺诈行为变得极为棘手。而且，云中欺诈检测的问题—基于物联网（IoT）的智能社会存在许多限制，例如正常人和欺诈者的行为不断变化，欺诈者试图开发和使用新的方法来执行欺诈，并且可用性极低欺诈相关基准数据集。在这篇研究文章中，作者提出了基于逻辑回归的k-fold机器学习技术（MLT），用于基于云IoT的智能社会环境中的欺诈检测和预防。在执行逻辑回归和MLT之前，k折方法会创建与银行交易相关的数据的多折。逻辑回归执行基于逻辑的回归分析，而智能机器学习方法执行注册，分类，聚类，降维，对接收到的银行交易数据进行深度学习，培训和强化学习的步骤。使用智能机器学习工具（如ROC（接收器操作特性）曲线，混淆矩阵，均值召回得分值和精确召回曲线）针对欧洲银行日常交易相关基准数据集实施建议的方法并进行进一步分析揭示了所提出的方法在基于云IoT的智能社会环境中检测欺诈行为是高效，准确和可靠的。