Advertisement

Applied Aspects of Implementation of Intelligent Information Technology for Fraud Detection During Mobile Applications Installation

  • Andrii Yarovyi
  • Tetiana PolhulEmail author
Conference paper
First Online:
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1080)

Abstract

The intelligent information technology for fraud detection during mobile applications installation is proposed in this paper. The structure of such intelligent information technology of fraud detection is offered in accordance with the tasks which should be solved by it: subsystem for available data analysis; subsystem for intellectual processing of available data; subsystem for developing a database and knowledge base (for detecting fraudsters); classification model building and user classification subsystem; subsystem for users’ templates formation; subsystem for the generalized fraudsters fingerprint formation. The proposed intelligent information technology allows the processing of various input data, which in the process gives the opportunity to form a generalized template of fraudster.

Keywords

Fraud detection Anomaly detection Mobile application installation Data mining Intelligent information technology 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Melnykova, N.: The basic approaches to automation of management by enterprise finances. In: 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), Lviv, pp. 288–291 (2017).  https://doi.org/10.1109/stc-csit.2017.8098788
  2. 2.
    Yarovyi, A., Polhul, T., Krylyk, L.: Rozrobka metodu vyiavlennia shakhraistva pry instaliuvanni mobilnykh dodatkiv z vykorystanniam intelektualnoho analizu danykh. In: Materialy konferentsiyi XLVII Naukovo-tekhnichna konferentsiya pidrozdiliv Vinnytskoho natsionalnoho tekhnichnoho universytetu, Vinnytsia (2018). http://ir.lib.vntu.edu.ua/bitstream/handle/123456789/22722/079.pdf?sequence=1
  3. 3.
    Our take on mobile fraud detection. http://geeks.jampp.com/data-science/mobile-fraud/
  4. 4.
    Dave, V., Guha, S., Zhang, Y.: ViceROI: Catching Click-Spam in Search Ad Networks. http://www.sysnet.ucsd.edu/~vacha/ccs13.pdf
  5. 5.
    Dave, V., Guha, S., Zhang, Y.: Measuring and fingerprinting click-spam in ad networks. In: Proceedings of the Annual Conference of the ACM Special Interest Group on Data Communication (SIGCOMM), Helsinki, Finland, pp. 175–186, August 2012Google Scholar
  6. 6.
    Yarovyi, A.A., Romanyuk, O.N., Arsenyuk, I.R., Polhul, T.D.: Program applications install fraud detection using data mining. Naukovi pratsi Donetskoho natsionalnoho tekhnichnoho universytetu. Seriya: “Informatyka, kibernetyka ta obchysliuvalna tekhnika”, issue 2(25), pp. 126–131 (2017). http://science.donntu.edu.ua/wp-content/uploads/2018/03/ikvt_2017_2_site-1.pdf
  7. 7.
    Polhul, T.D., Yarovyi, A.A.: Vyznachennia shakhraiskykh operatsiy pry vstanovlenni mobilnykh dodatkiv z vykorystanniam intelektualnoho analizu danykh. Suchasni tendentsiyi rozvytku systemnoho prohramuvannia. Tezy dopovidei, Kyiv, pp. 55–56 (2016). http://ccs.nau.edu.ua/wp-content/uploads/2017/12/%D0%A1%D0%A2%D0%A0%D0%A1%D0%9F_2016_07.pdf
  8. 8.
    Polhul, T.D., Yarovyi, A.A.: Vyznachennia shakhraiskykh operatsiy pry instaliatsiyi mobilnykh dodatkiv z vykorystanniam intelektualnoho analizu danykh. Materialy XLVI naukovo-tekhnichnoi konferentsiyi pidrozdiliv VNTU, Vinnytsia (2017)Google Scholar
  9. 9.
    Kochava Uncovers Global Ad Fraud Scam. https://www.kochava.com/
  10. 10.
  11. 11.
    Fraudlogix: Ad Fraud Solutions for Exchanges, Networks, SSPs & DSPs. https://www.fraudlogix.com/
  12. 12.
    Kraken Antibot. http://kraken.run/
  13. 13.
    AppsFlyer: Measure In-App To Grow Your Mobile Business. https://www.appsflyer.com/
  14. 14.
  15. 15.
    FraudScore: FraudScore fights ad fraud using Machine Learning. https://fraudscore.mobi/
  16. 16.
  17. 17.
    Polhul, T., Yarovyi, A.: The input data heterogeneities resolution method during mobile applications installation fraud detection. Visnyk SNU named after V. Dal’ – Severodonetsk: SNU named after V. Dal’, № 7(248), pp. 60–69 (2018)Google Scholar
  18. 18.
    Polhul, T.: Development of an intelligent system for detecting mobile app install fraud. In: Proceedings of the IRES 156th International Conference, Bangkok, Thailand, 21–22 March, 2019, pp. 25–29Google Scholar
  19. 19.
    Polhul, T.D., Yarovyi, A.A.: Heterogeneous data analysis in intelligent fraud detection systems, № 2, pp. 78–90. Visnyk of Vinnutsia Polytechnic Institute, April 2019.  https://doi.org/10.31649/1997-9266-2019-143-2-78-90
  20. 20.
    Polhul, T., Yarovyi, A.: Development of a method for fraud detection in heterogeneous data during installation of mobile applications. East. Eur. J. Enterp. Technol. 1/2(97) (2019).  https://doi.org/10.15587/1729-4061.2019.155060
  21. 21.
    Segaran, T.: Programming Collective Intelligence. Building Smart Web 2.0 Applications. O’Reilly Media, Newton (2008). 368 p.Google Scholar
  22. 22.
    Kiulian, A.H., Polhul, T.D., Khazin, M.B.: Matematychna model rekomendatsiynoho servisu na osnovi metodu kolaboratyvnoi filtratsiyi. In: Kompiuterni tekhnolohiyi ta Internet v informatsiynomu suspilstvi, pp. 226–227 (2012). http://ir.lib.vntu.edu.ua/bitstream/handle/123456789/7911/226-227.pdf?sequence=1&isAllowed=y
  23. 23.
    Guido, S., Müller, A.: Introduction to Machine Learning with Python: A Guide for Data Scientists. O’Reilly Media, Newton (2016). 400 p.Google Scholar
  24. 24.
    Yarovyi, A., Ilchenko, R., Arseniuk, I., Shemet, Y., Kotyra, A., Smailova, S.: An intelligent system of neural networking recognition of multicolor spot images of laser beam profile. In: Proceedings of SPIE 10808, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018, vol. 108081, October 2018.  https://doi.org/10.1117/12.2501691
  25. 25.
    Géron, A.: Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. Aurélien Géron, O’Reilly Media, Newton (2017). 574 p.Google Scholar
  26. 26.
    Dong, X., Qiu, P., Lü, J., Cao, L., Xu, T.: Mining top-k useful negative sequential patterns via learning. IEEE Trans. Neural Netw. Learn. Syst.  https://doi.org/10.1109/tnnls.2018.2886199
  27. 27.
    Kozhemyako, V., Timchenko, L., Yarovyy, A.: Methodological principles of pyramidal and parallel-hierarchical image processing on the base of neural-like network systems. Adv. Electr. Comput. Eng. 8(2), 54–60 (2008).  https://doi.org/10.4316/aece.2008.02010
  28. 28.
    Granik, M., Mesyura, V., Yarovyi, A.: Determining fake statements made by public figures by means of artificial intelligence. In: IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), Lviv, pp. 424–427 (2018).  https://doi.org/10.1109/stc-csit.2018.8526631
  29. 29.
    Polhul, T.D.: Information technology for the construction of intelligent systems for detecting fraud during mobile applications installation. Information Technologies and Computing Engineering, vol. 44, № 1, pp. 4–16, May 2019.  https://doi.org/10.31649/1999-9941-2019-44-1-4-16
  30. 30.
    Cielen, D., Meysman, A.D.B., Ali, M.: Introducing Data Science: Big Data, Machine Learning, and More, Using Python Tools. Manning, New york (2016). 320 p.Google Scholar
  31. 31.
    Yarovyi, A.A., Polhul, T.D.: Kompiuterna prohrama “Prohramnyi modul zboru danykh informatsiynoi tekhnolohiyi” vyiavlennia shakhraistva pry instaliuvanni prohramnykh dodatkiv. Cvidotstvo pro reiestratsiu avtorskoho prava na tvir No. 76348. Ministerstvo ekonomichnoho rozvytku i torhivli Ukrainy, Kyiv (2018)Google Scholar
  32. 32.
    Yarovyi, A.A., Polhul, T.D.: Kompiuterna prohrama “Prohramnyi modul vyznachennia skhozhosti korystuvachiv informatsiynoi tekhnolohiyi vyiavlennia shakhraistva pry instaliuvanni prohramnykh dodatkiv”. Cvidotstvo pro reiestratsiu avtorskoho prava na tvir No. 76347. Ministerstvo ekonomichnoho rozvytku i torhivli Ukrainy, Kyiv (2018) Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Vinnytsia National Technical UniversityVinnytsiaUkraine

Personalised recommendations

Cite paper

Buy options