Technological Support of Real-Time Interaction in Web Clients of Analytical Fraud Detection Systems

Abstract

The optimal tools selection for design of web-based visual mining client for real time fraud detection systems was discussed. The features of modern real time fraud detection software were analyzed. The necessity of transition to using of web-based technologies for client software design was shown. The market of web-frameworks and browser to web-server data exchange technologies were investigated. Basing on experimental research the most efficient toolset for design of web-client software for real time fraud detection systems was offered.

 

Keywords: fraud detection, Visual Mining, real time data exchange, web-visualization, webSockets, MessageBus.

References
[1] J. Akhilomen, “Data Mining Application for Cyber Credit-Card Fraud Detection System” in Proceedings of the 13th Industrial Conference – Advances in Data Mining: Applications and Theoretical Aspects, New York, NY, USA, 2013, (LNAI 7987) pp. 218– 228.


[2] F. Carcillo, A. Dal Pozzolo, Y. Le Borgne, O. Caelen, Y. Mazzer, G. Bontempi, “SCARFF : A scalable framework for streaming credit card fraud detection with spark” in Information Fusion, #41 (2018), Elsevier, 2017, pp. 182–194.


[3] W.N. Robinson, A. Aria, “Sequential fraud detection for prepaid cards using hidden Markov model divergence” in Expert Systems With Applications, #91 (2018), Elsevier, 2017, pp. 235–251.


[4] Y. Peng, L. Zhang, Y. Guan, “Detecting Fraud in Internet Auction Systems” in Expert Systems With Applications, #91 (2018), Elsevier, 2017, pp. 187–199.


[5] D. Lv, M. Yu, J. Song, K. Qian, Y. Cui, “Study on Police Graphic Plotting Technology based on Web” in Proceedings of the 6th International Conference on Software and Computer Applications, (ICSA 2017), ACM, 2017, pp. 137–143.


[6] M. Pignatelli, “TnT: A set of libraries for visualizing trees and track-based annotations for the web” in Bioinformatics, Vol. 32, Iss. 16, 2016, pp. 2524– 2525.


[7] A. Arbelaiz, A. Moreno, L. Kabongo, A. García-Alonso, “X3DOM volume rendering component for web content developers” in Multimedia Tools and Applications, Vol. 76, Iss. 11, 2017, pp. 13425–13454.


[8] K. Wang, D. Neudegg, C. Yuile, M. Terkildsen, R. Marshall, M. Hyde, G. Patterson, C. Thomson, A. Kelly, Y. Tian, “Antarctic space weather data managed by IPS radio and space services of Australia” in Data Science Journal, Vol. 13, 2014, pp. PDA44-PDA50.


[9] S. Mijovic, E. Shehu, C. Buratti, “Comparing Application Layer Protocols for the Internet of Things via Experimentation” in Proceedings of the 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow, (RTSI 2016), IEEE, 2016, pp. 1-5.


[10] L.-J. Chi, C.-H. Huang, K.-T. Chuang, “Mobile-friendly and streaming web-based data visualization” in Proceedings of the Conference on Technologies and Applications of Artificial Intelligence, (TAAI 2016), IEEE, 2016, pp. 124-129.


[11] V.Y. Radygin, N.V. Lukyanova, D.Yu. Kupriyanov, “LMS in university for in-class education: Synergy of free software, competitive approach and social networks technology” in Proceedings of the International Scientific-Practical Conference Information Technologies in Education of the XXI Century, #91 (ITE-XXI), AIP Publishing, 2017, pp. 020015-1 – 020015-8.


[12] K. Shuang, K. Feng, “Research on server push methods in web browser based instant messaging applications” in Journal of Software, Vol. 8, Iss. 10, IEEE, 2013, pp. 2644–2651.


[13] http://trends.builtwith.com/framework – statistical analysis of using different frameworks in the Internet from BuiltWith Pty Ltd Company (last visit date 14.11.2017).