Real-Time Filtering Non-Intentional Bid Request on Demand-Side Platform
While real-time bidding brings a huge profit for online businesses, it also becomes a potential target for malicious purposes. In real-time bidding, the bid request traffic could be classified into two kinds: intentional and non-intentional. Intentional bid requests come from ordinal web users while...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/23/12228 |
| _version_ | 1797463623503708160 |
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| author | Thi-Thanh-An Nguyen Duy-An Ha Wen-Yuan Zhu Shyan-Ming Yuan |
| author_facet | Thi-Thanh-An Nguyen Duy-An Ha Wen-Yuan Zhu Shyan-Ming Yuan |
| author_sort | Thi-Thanh-An Nguyen |
| collection | DOAJ |
| description | While real-time bidding brings a huge profit for online businesses, it also becomes a potential target for malicious purposes. In real-time bidding, the bid request traffic could be classified into two kinds: intentional and non-intentional. Intentional bid requests come from ordinal web users while non-intentional bid requests come from abnormal web users. From the perspective of a demand-side platform (DSP), the budget of advertisers should be used as effectively as possible by limiting non-intentional traffic. Therefore, it is essential to classify and predict these two kinds of bid request traffic. In this research, we propose a real-time filtering bid requests (RFBR) model to predict whether an incoming bid request is intentional or non-intentional from the DSP’s viewpoint. Our model is built on three stages. In the first stage, we analyzed all potential attributes in the bid request scheme and figured out the relations between abnormal behaviors and their attributes; in the second stage, a classification model was built to classify normal and abnormal audiences by the extracted features and self-defined thresholds; in the third stage, a RFBR model was built to classify intentional and non-intentional bid requests. The experimental result shows that our system can effectively classify incoming bid requests. |
| first_indexed | 2024-03-09T17:53:24Z |
| format | Article |
| id | doaj.art-8384427eb52545608997f667e1d3b091 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T17:53:24Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8384427eb52545608997f667e1d3b0912023-11-24T10:33:01ZengMDPI AGApplied Sciences2076-34172022-11-0112231222810.3390/app122312228Real-Time Filtering Non-Intentional Bid Request on Demand-Side PlatformThi-Thanh-An Nguyen0Duy-An Ha1Wen-Yuan Zhu2Shyan-Ming Yuan3EECS International Graduate Programs, National Yang Ming Chiao Tung University, Hsinchu 300, TaiwanEECS International Graduate Programs, National Yang Ming Chiao Tung University, Hsinchu 300, TaiwanTenMax AD Tech Lab, Taipei 222, TaiwanDepartment of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 300, TaiwanWhile real-time bidding brings a huge profit for online businesses, it also becomes a potential target for malicious purposes. In real-time bidding, the bid request traffic could be classified into two kinds: intentional and non-intentional. Intentional bid requests come from ordinal web users while non-intentional bid requests come from abnormal web users. From the perspective of a demand-side platform (DSP), the budget of advertisers should be used as effectively as possible by limiting non-intentional traffic. Therefore, it is essential to classify and predict these two kinds of bid request traffic. In this research, we propose a real-time filtering bid requests (RFBR) model to predict whether an incoming bid request is intentional or non-intentional from the DSP’s viewpoint. Our model is built on three stages. In the first stage, we analyzed all potential attributes in the bid request scheme and figured out the relations between abnormal behaviors and their attributes; in the second stage, a classification model was built to classify normal and abnormal audiences by the extracted features and self-defined thresholds; in the third stage, a RFBR model was built to classify intentional and non-intentional bid requests. The experimental result shows that our system can effectively classify incoming bid requests.https://www.mdpi.com/2076-3417/12/23/12228online advertisingdemand-side platform (DSP)real-time biddinganomaly detectiondata mining |
| spellingShingle | Thi-Thanh-An Nguyen Duy-An Ha Wen-Yuan Zhu Shyan-Ming Yuan Real-Time Filtering Non-Intentional Bid Request on Demand-Side Platform Applied Sciences online advertising demand-side platform (DSP) real-time bidding anomaly detection data mining |
| title | Real-Time Filtering Non-Intentional Bid Request on Demand-Side Platform |
| title_full | Real-Time Filtering Non-Intentional Bid Request on Demand-Side Platform |
| title_fullStr | Real-Time Filtering Non-Intentional Bid Request on Demand-Side Platform |
| title_full_unstemmed | Real-Time Filtering Non-Intentional Bid Request on Demand-Side Platform |
| title_short | Real-Time Filtering Non-Intentional Bid Request on Demand-Side Platform |
| title_sort | real time filtering non intentional bid request on demand side platform |
| topic | online advertising demand-side platform (DSP) real-time bidding anomaly detection data mining |
| url | https://www.mdpi.com/2076-3417/12/23/12228 |
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