使用改进的Lenet-5卷积神经网络检测点击欺诈

C. D. Fernando, C. Walgampaya
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引用次数: 0

摘要

在线广告在过去的几十年里迅速发展,在全球创造了数十亿美元的商业市场。当涉及到数字平台时,点击欺诈可以被确定为常见的不当行为之一。这导致了广告发布商收入的增加和广告商的巨大损失。因此,检测点击欺诈已成为网络营销的主要关注点。最近的研究提出了不同类型的基于机器学习的方法来检测这些欺诈活动。在本研究中,我们提出了一种改进的Lenet-5卷积神经网络来识别点击欺诈。该算法利用基于Lenet-5的卷积神经网络的深度特征,达到99.09%的准确率。
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Detecting Click Fraud Using an Improved Lenet-5 Convolution Neural Network
Online advertising has grown drastically over the last couple of decades by making billions worth of business markets all over the world. Click Fraud can be identified as one of the common malpractices when it comes to digital platforms. This leads to an increase in the revenue of the Ad publishers and huge losses for the advertisers. Hence the need of detecting click fraud has become a major concern in online marketing. Recent studies have proposed different kinds of machine learning based approaches to detect these fraud activities. In this study, we propose an improved Lenet-5 Convolution Neural Network to identify click fraud. This proposed novel deep learning algorithm was able to achieve an accuracy of 99.09% by using deep features of the proposed Lenet-5 based Convolution Neural Network.
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