Stacked Generalization Architecture for Predicting Publisher Behaviour from Highly Imbalanced User-Click Data Set for Click Fraud Detection
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Theoretical Computer Science,Software
Link
https://link.springer.com/content/pdf/10.1007/s00354-023-00218-1.pdf
Reference69 articles.
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3. Choi, D., Lee, K.: An artificial intelligence approach to financial fraud detection under IoT environment: a survey and implementation. Secur. Commun. Net 2018, 1–15 (2018). https://doi.org/10.1155/2018/5483472
4. Haider, C.M.R., Iqbal, A., Rahman, A.H., Rahman, M.S.: An ensemble learning based approach for impression fraud detection in mobile advertising. J. Netw. Comput. Appl. 112, 126–141 (2018). https://doi.org/10.1016/j.jnca.2018.02.021
5. Springborn, K., Barford, P.: Impression fraud in on-line advertising via pay-per-view networks. Sec Symp (2013). https://doi.org/10.4995/Thesis/10251/8685
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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
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