# Real-Time Filtering Non-Intentional Bid Request on Demand-Side Platform
## 1 Introduction  
Online advertising, also known as internet advertising, is any form of promotional brands or services on various online platforms that used to increase sales and brand awareness without being restricted by time and space. It has a major role in the main revenue of most websites and mobile apps. According to a report from the Internet Advertising Bureau (IAB) website, the annual revenue of online advertising surpassed 189 billion US dollars in 2021, which represented an increase of 35.4% from 50 billion US dollars over the previous year. On the other hand, real-time bidding is a new extremely effective form of online advertising which has allowed advertisers and publishers to trade their advertising impressions through real-time auctions in an advertising exchange platform. Due to the growth of RTB, it has also become a gainful target for fraudulent activities which are an attempt to defraud advertising platforms for the purpose of financial gain. Abnormal traffic, which is generated from fraudulent activities, can come from the practice of fraud in impressions, clicks, conversion, data events, etc. However, abnormal traffic is not only from non-human users but also from crawler or some real users who diverted to websites or applications without their control and awareness. For these reasons, a study [1] has classified bidding request traffic into two groups: (1) intentional bidding requests which come from ordinal web users; and (2) non-intentional bidding requests which come from abnormal web users. It is obvious that non-intentional bidding requests may harm the online advertising ecosystem. From the perspective of a DSP, this kind of traffic should be filtered out as much as possible and the use of budget should be usedas effectively as possible. Moreover, the DSP plays a role that serves the advertisers by managing their advertising budget and delivering ads to the right audiences. Unfortunately, the advertisers do not interact with \"real\" users in many cases. The previous works indicate that fraudulent activities are out of control in online advertising [1; 2; 3]. Therefore, classifying and predicting this kind of bid request traffic is very important and becomes a challenge in both industrial and academic areas. As the result, this research would like to propose a RFBR model to classify the incoming bid request. In particular, all potential attributes in the bid request scheme are first analyzed to figure out the relation between abnormal behaviors and these attributes. After abnormal behaviors are identified by extracting the features, a classification model is built by these features and corresponding thresholds to classify normal and abnormal audiences. Finally, a RFBR model was built to classify intentional and non-intentional bid requests.  
Accordingly, this work makes the following contributions:  
* We discovered the attributes of bid requests to extract the features that are useful in detecting abnormal behaviors of audiences;
* We built a model to label abnormal audiences by applying the extracted features and self-defined thresholds;
* We proposed RFBR model to classify incoming bid requests as intentional or non-intentional based on the labeled audiences.