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The Application of Fuzzy Set Theory to Mobile Internet Advertisement Fraud Detection

Authors: Jinming Ma, Tianbing Xia, Janusz R. Getta


This paper presents the application of fuzzy set theory to implement of mobile advertisement anti-fraud systems. Mobile anti-fraud is a method aiming to identify mobile advertisement fraudsters. One of the main problems of mobile anti-fraud is the lack of evidence to prove a user to be a fraudster. In this paper, we implement an application by using fuzzy set theory to demonstrate how to detect cheaters. The advantage of our method is that the hardship in detecting fraudsters in small data samples has been avoided. We achieved this by giving each user a suspicious degree showing how likely the user is cheating and decide whether a group of users (like all users of a certain APP) together to be fraudsters according to the average suspicious degree. This makes the process more accurate as the data of a single user is too small to be predictable.

Keywords: Mobile internet, advertisement, anti-fraud, fuzzy set theory.

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[1] Buckley, James J. “Fuzzy Probability and Statistics.” Studies in Fuzziness & Soft Computing 196(2006):236.
[2] Oentaryo R., Lim E P., Finegold M. et al. Detecting Click Fraud in Online Advertising: A Data Mining Approach (J). Journal of Machine Learning Research, 1990, 15:99-140.
[3] Tian T., Zhu J., Xia F. et al. Crowd Fraud Detection in Internet Advertising (C). The 24th International Conference. International World Wide Web Conferences Steering Committee, 2015.
[4] Pooranian Z., Conti M., Hadaddi H. Online Advertising Security: Issues, Taxonomy, and Future Directions (J). arXiv preprint arXiv:2006.03986, 2020.
[5] Kandel A., Byatt WJ. Fuzzy sets, fuzzy algebra, and fuzzy statistics (J). Proc IEEE, 1978, 66(12):1619-1639.
[6] Buckley J J . Fuzzy statistics: regression and prediction (J). Soft Computing, 2005, 9(10):769-775.
[7] Uga-Rebrovs O, Kueova G. Specific Features of Descriptive Statistics with Fuzzy Random Variables (J). Information Technology and Management Science, 2018, 21:104-110.
[8] Mordeson J N . Fuzzy Mathematics (M). Physica-Verlag, 2001.