Anti-Counterfeit Scheme Using Monte Carlo Simulation for E-commerce in Cloud Systems

Keke Gai, Meikang Qiu, Hui Zhao, Wenyun Dai
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引用次数: 23

Abstract

E-commerce using cloud-based trading platforms has become a popular approach with the growth of global development in recent years. However, the existence of counterfeits on the platform has threatened the benefits of all stakeholders. This paper proposes a novel scheme named Anti-Counterfeit Deterministic Prediction Model (ADPM), which is designed for detecting counterfeits by using Monte Carlo Model (MCM) to predict the potential malicious information in e-commerce. We consider the discriminations of the fake merchandises a crucial issue in preventing counterfeits on the online business platforms. The proposed mechanism provides a paradigm of machine-learning with using a novel algorithm that derives from MCM. The main algorithm used in our proposed mechanism is Monte Carlo Model-based Prediction Analysis Algorithm (M-PAA). Our experiment has evaluated that the proposed approach can provision the predictions of the insecure information in e-commerce.
基于蒙特卡罗仿真的云系统电子商务防伪方案
近年来,随着全球发展的增长,利用云交易平台进行电子商务已成为一种流行的方式。然而,平台上假货的存在已经威胁到所有利益相关者的利益。本文提出了一种新的防伪确定性预测模型(ADPM)方案,该方案利用蒙特卡罗模型(MCM)预测电子商务中潜在的恶意信息来检测假货。我们认为,在网上商业平台防范假冒产品的过程中,辨别假冒商品是一个至关重要的问题。提出的机制提供了一种机器学习的范例,使用了一种源自MCM的新算法。在我们提出的机制中使用的主要算法是基于蒙特卡罗模型的预测分析算法(M-PAA)。实验结果表明,该方法可以对电子商务中的不安全信息进行预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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