安全和防欺诈的信用卡在线支付系统

Baker Al Smadi, A. A. AlQahtani, Hosam Alamleh
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引用次数: 1

摘要

信用卡诈骗是影响全球个人和公司的最严重威胁之一,特别是随着每天涉及信用卡的金融交易数量的增加。最常见的威胁可能来自数据库泄露和身份盗窃。所有这些威胁使金融交易的安全面临严重风险,需要从根本上解决。本文旨在提出一个安全的在线支付系统,显著提高信用卡的安全性。通过利用持卡人和服务器之间的安全通信通道,我们的系统可以特别适应潜在的网络攻击、未经授权的用户、中间人、信用卡号码生成的猜测攻击或非法金融活动。我们的系统使用共享密钥和验证令牌,允许双方通过加密通道进行通信。此外,我们的系统被设计为在用户的机器上生成一次性信用卡号码,由服务器验证,而不通过网络共享信用卡号码。我们的方法将机器学习(ML)算法与唯一的临时信用卡号码结合在一个集成系统中,这是在线信用卡保护系统中的第一种方法。新的安全系统为每笔预定金额的交易生成一个一次性信用卡号码。同时,该系统可以利用ML算法检测潜在的欺诈行为,该算法具有新的关键特征,如IMEI或IP地址、交易位置和其他特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Secure and Fraud Proof Online Payment System for Credit Cards
Credit card fraud is one of the most critical threats affecting individuals and companies worldwide, particularly with the growing number of financial transactions involving credit cards every day. The most common threats are likely to come from database breaches and identity theft. All these threats put the security of financial transactions at severe risk and require a fundamental solution. This paper aims to suggest a secure online payment system that significantly improves credit card security. Our system can be particularly resilient to potential cyber-attacks, unauthorized users, man-in-the-middle, and guessing attacks for credit card number generation or illegal financial activities by utilizing a secure communication channel between the cardholder and server. Our system uses a shared secret and a verification token that allow both sides to communicate through an encrypted channel. Furthermore, our system is designed to generate a one-time credit card number at the user’s machine that is verified by the server without sharing the credit card number over the network. Our approach combines machine learning (ML) algorithms with unique temporary credit card numbers in one integrated system, which is the first approach in the online credit card protection system. The new security system generates a one-time-use credit card number for each transaction with a predetermined amount of money. Simultaneously, the system can detect potential fraud utilizing ML algorithm with new critical features such as the IMEI or IP address, the transaction’s location, and other features.
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