A Prototype Data Management Platform with Machine Learning-based Automated Turing Test for Prevention of Online Advertising Fraud

超过29%的互联网活动来自恶意非人类流量（NHT），通过实时竞价（RTB）自动放置在线显示广告的程序化平台非常容易受到点击欺诈的影响。点击欺诈发生在机器人错误地产生印象或提高显示广告的点击率时，通过人为增加出价值/频率产生欺诈性的点击付费或每千次成本收入。广告欺诈很难被发现，并直接导致广告商/媒体机构每年损失超过680亿美元的收入。以及限制每年240亿英镑的机会，利用分析数据的价值进行个性化营销。现有的软件，以防止CF使用基于规则的过滤，由于只有在访问/投标后才能检测标记NHT（阴性示例）的点击/印象（地面实况）的来源，因此RTB受到限制。有限的平台兼容性和规则生成率也无法解决快速发展的点击欺诈威胁。相比之下，MediaGamma Limited（MG）旨在开发一个原型自动图灵测试作为投标前解决方案，以防止点击欺诈使用100万个二进制投标请求功能，通过主动和持续识别来自NHT的欺诈点击/展示的特征。使用机器学习算法，通过一个新的RTB广告投放数据管理平台进行集成，通过生物行为测试的技术进行训练，MediaGamma目标能力，以防止代理商/广告商实时参与虚假投标过程。这个为期12个月的项目将花费555，152英镑，通过A/B测试在商业相关情况下演示原型数据管理平台软件模块的性能。

Over 29% of internet activity arises from malicious non-human traffic (NHT) andprogrammatic platforms for automated placement of online display advertising via real-timebidding (RTB) are highly vulnerable to Click Fraud. Click Fraud occurs when bots falselygenerate impressions or enhance the click-through rate of displayed ads, generating fraudulentpay-per-click or cost per mille revenues by artificially increasing the bid value/frequency.Advertising fraud is hard to detect and leads directly to >$68bn/yr of lost revenues byadvertisers/media agencies, as well as restricting the £24bn/yr opportunity to leverage valuefrom analytics data for personalised marketing.Existing software to prevent CF uses rules-based filtering, restricted for RTB by limitedcapability to detect the origin of a click/impression (ground truth) for labelling NHT (negativeexamples) only after a visit/bid. Limited platform compatibility and rule generation rates arealso unable to address a rapidly evolving Click Fraud threat.In contrast, MediaGamma Limited (MG) aim to develop a prototype Automated Turing Testas a pre-bid solution to prevent Click Fraud using 1 million binary bid request features, byactively and continuously identifying the characteristics of fraudulent clicks/impressions fromNHT. Using machine learning algorithms integrated through a novel Data ManagementPlatform for RTB ad placement, trained via techniques inspired from biological behaviouraltests, MediaGamma target capability to prevent an agency/advertiser from participating in afalse bidding process in real-time. This 12 month project will cost £555,152 withperformance of the prototype Data Management Platform software module demonstratedthrough A/B testing in commercially relevant situations.