The web ad-click fraud detection approach for supporting to the online advertising system

2022 ◽  
Vol 7 (1) ◽  
pp. 1
Author(s):  
Pankaj Kumar Keserwani ◽  
Mahesh Chandra Govil ◽  
Emmanuel Shubhakar Pilli
Keyword(s):  
Online Advertising ◽  
Fraud Detection ◽  
Click Fraud ◽  
Detection Approach ◽  
The Web

User click fraud detection method based on Top-Rank-k frequent pattern mining

2019 ◽  
Vol 33 (15) ◽  
pp. 1950150 ◽  
Author(s):  
Lijiao Pan ◽  
Shibiao Mu ◽  
Yingyan Wang
Keyword(s):  
Pattern Mining ◽  
Detection Method ◽  
Online Advertising ◽  
Fraud Detection ◽  
Frequent Pattern Mining ◽  
Detection Algorithm ◽  
Frequent Pattern ◽  
Good Efficiency ◽  
Click Fraud ◽  
Evaluation Score

A user click fraud detection method based on Top-Rank-k frequent pattern mining algorithm is presented to solve the click fraud problem appearing in current online advertising. Firstly, this method combines the click frequency of event samples, calculates the real evaluation score of click stream, and the click stream density function and evaluation score expression under multi-dimensional variables, and further obtains the time complexity of the next user’s click fraud process. Secondly, according to the Top-Rank-k frequent pattern, the process of click fraud detection algorithm is designed, and the click fraud user is analyzed and obtained. The results show that this method has good efficiency and correctness, and is superior to other similar algorithms.

scholarly journals Light GBM Machine Learning Algorithm to Online Click Fraud Detection

2019 ◽  
pp. 1-12
Author(s):  
Elena-Adriana MINASTIREANU ◽  
Gabriela MESNITA
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Online Advertising ◽  
Fraud Detection ◽  
Online Marketing ◽  
Machine Learning Algorithm ◽  
Advertising Industry ◽  
Click Fraud ◽  
Web Advertising ◽  
Critical Issues

In the current web advertising activities, the fraud increases the number of risks for online marketing, advertising industry and e-business. The click fraud is considered one of the most critical issues in online advertising. Even if the online advertisers make permanent efforts to improve the traffic filtering techniques, they are still looking for the best protection methods to detect click frauds.

Click-Fraud Detection for Online Advertising

2020 ◽  
pp. 261-271
Author(s):  
Roman Wiatr ◽  
Vladyslav Lyutenko ◽  
Miłosz Demczuk ◽  
Renata Słota ◽  
Jacek Kitowski
Keyword(s):  
Online Advertising ◽  
Fraud Detection ◽  
Click Fraud

scholarly journals Click Fraud Detection on the Advertiser Side

2014 ◽  
pp. 419-438 ◽  
Author(s):  
Haitao Xu ◽  
Daiping Liu ◽  
Aaron Koehl ◽  
Haining Wang ◽  
Angelos Stavrou
Keyword(s):  
Fraud Detection ◽  
Click Fraud

scholarly journals Crowdsourcing for click fraud detection

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Riwa Mouawi ◽  
Imad H. Elhajj ◽  
Ali Chehab ◽  
Ayman Kayssi
Keyword(s):  
Fraud Detection ◽  
Click Fraud

scholarly journals Detecting Anti Ad-blockers in the Wild

2017 ◽  
Vol 2017 (3) ◽  
pp. 130-146 ◽  
Author(s):  
Muhammad Haris Mughees ◽  
Zhiyun Qian ◽  
Zubair Shafiq
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Online Advertising ◽  
Arms Race ◽  
Third Party ◽  
Measurement Study ◽  
Economic Threat ◽  
In The Wild ◽  
Automated Machine Learning ◽  
The Web

Abstract The rise of ad-blockers is viewed as an economic threat by online publishers who primarily rely on online advertising to monetize their services. To address this threat, publishers have started to retaliate by employing anti ad-blockers, which scout for ad-block users and react to them by pushing users to whitelist the website or disable ad-blockers altogether. The clash between ad-blockers and anti ad-blockers has resulted in a new arms race on the Web. In this paper, we present an automated machine learning based approach to identify anti ad-blockers that detect and react to ad-block users. The approach is promising with precision of 94.8% and recall of 93.1%. Our automated approach allows us to conduct a large-scale measurement study of anti ad-blockers on Alexa top-100K websites. We identify 686 websites that make visible changes to their page content in response to ad-block detection. We characterize the spectrum of different strategies used by anti ad-blockers. We find that a majority of publishers use fairly simple first-party anti ad-block scripts. However, we also note the use of third-party anti ad-block services that use more sophisticated tactics to detect and respond to ad-blockers.

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