Real-Time Filtering Non-Intentional Bid Request on Demand-Side Platform
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Internet Traffic Analysis and Secure E-voting · Spam and Phishing Detection · Network Security and Intrusion Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
EECS International Graduate Programs, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
TenMax AD Tech Lab, Taipei 222, Taiwan
Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
Funding
the Ministry of Science and Technology of Taiwan
References (20)
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