Evaluating Feature Importance to Investigate Publishers Conduct for Detecting Click Fraud-Reference-Cited by-同舟云学术

Evaluating Feature Importance to Investigate Publishers Conduct for Detecting Click Fraud

Published:2023 Issue: Volume: Page:515-524
ISSN:1876-1100
Container-title:Lecture Notes in Electrical Engineering
language:
Short-container-title:

Author:

Sisodia Deepti,Sisodia Dilip Singh,Singh Deepak

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-99-0085-5_42

Reference20 articles.

1. Zhang L, Guan Y (2008) Detecting click fraud in pay-per-click streams of online advertising networks. In: Distributed Computing Systems, 2008. ICDCS’08. The 28th international conference on. pp 77–84

2. Berrar D (2012) Random forests for the detection of click fraud in online mobile advertising. In: Proceedings of 2012 international workshop on fraud detection in mobile advertising (FDMA), Singapore, pp 1–10

3. Sisodia D, Sisodia DS (2022) Feature space transformation of user-clicks and deep transfer learning framework for fraudulent publisher detection in online advertising. Appl Soft Comput 125:109142. https://doi.org/10.1016/j.asoc.2022.109142

4. Perera KS, Neupane B, Faisal MA et al (2013) A novel ensemble learning-based approach for click fraud detection in mobile advertising. In: Proceedings mining intelligence and knowledge exploration (MIKE). Springer, Berlin Heidelberg, pp 370–382

5. Sisodia D, Sisodia DS (2023) Data sampling methods for analyzing publishers conduct from highly imbalanced dataset in web advertising. In: International conference on information systems and management Science. pp 428–441

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A transfer learning framework towards identifying behavioral changes of fraudulent publishers in pay-per-click model of online advertising for click fraud detection;Expert Systems with Applications;2023-12

2. Gradient Boosting-Based Predictive Click Fraud Detection Using Manifold Criterion Variable Elimination;IFIP Advances in Information and Communication Technology;2023