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Data-Driven and Deep Learning Methodology for Deceptive Advertising and Phone Scams Detection

机译:欺骗性广告和数据驱动的数据驱动和深度学习方法   电话诈骗检测

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摘要

The advance of smartphones and cellular networks boosts the need of mobileadvertising and targeted marketing. However, it also triggers the unseensecurity threats. We found that the phone scams with fake calling numbers ofvery short lifetime are increasingly popular and have been used to trick theusers. The harm is worldwide. On the other hand, deceptive advertising(deceptive ads), the fake ads that tricks users to install unnecessary apps viaeither alluring or daunting texts and pictures, is an emerging threat thatseriously harms the reputation of the advertiser. To counter against these twonew threats, the conventional blacklist (or whitelist) approach and the machinelearning approach with predefined features have been proven useless.Nevertheless, due to the success of deep learning in developing the highlyintelligent program, our system can efficiently and effectively detect phonescams and deceptive ads by taking advantage of our unified framework on deepneural network (DNN) and convolutional neural network (CNN). The proposedsystem has been deployed for operational use and the experimental resultsproved the effectiveness of our proposed system. Furthermore, we keep ourresearch results and release experiment material onhttp://DeceptiveAds.TWMAN.ORG and http://PhoneScams.TWMAN.ORG if there is anyupdate.
机译:智能手机和蜂窝网络的发展推动了对移动广告和针对性营销的需求。但是,它也引发了看不见的安全威胁。我们发现,具有非常短生命周期的虚假电话号码的电话诈骗正变得越来越流行,并已被用来欺骗用户。危害是全球性的。另一方面,欺骗性广告(欺骗性广告)是一种诱骗用户通过诱使或令人生畏的文字和图片诱使他们安装不必要的应用程序的假广告,是一种严重威胁广告客户声誉的新兴威胁。为了应对这两个新威胁,传统的黑名单(或白名单)方法和具有预定义功能的机器学习方法已被证明是无用的。但是,由于深度学习在开发高度智能程序方面取得了成功,我们的系统可以有效地检测电话摄像头和利用我们在深度神经网络(DNN)和卷积神经网络(CNN)上统一的框架来欺骗广告。该拟议系统已被部署用于运营,实验结果证明了该拟议系统的有效性。此外,如果有任何更新,我们将保留研究结果并在http://DeceptiveAds.TWMAN.ORG和http://PhoneScams.TWMAN.ORG上发布实验材料。

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