MOBILE USER AUTHENTICATION ALGORITHM USING PERSONALIZED BEHAVIOR PATTERN RECOGNITION
Authors:
Junho Ahn, Department of Software, Korea National University of Transportation, Chungju, South Korea
Abstract:
Smartphone applications with inbuilt user authentication are widely used to provide mobile credential services in the real world. Certain mobile applications can detect an authentic user by recognizing his/her pin code, pattern, or through biological recognition of his/her fingerprints or iris to access servers. Such smartphone authentication methods can be time consuming and annoying for users, especially those who are not comfortable with smartphones and/or learning new techniques. In this study, an automatic user authentication algorithm was designed using personalized behavior classifications pertaining to the user’s activity, audio, and location patterns in his/her daily life. Furthermore, real-world mobile user data in terms of location, activity, and audio was collected, and the feasibility of the algorithm was evaluated by analyzing the mobile user behaviors. According to the data collected from 15 mobile users during a one-month period, the users’ behavior patterns are repeated within one week. As users have their own unique daily behavioral patterns, they can be used for the authentication process.
Keywords:
Authentication, Smartphone, Daily Pattern, Activity, Audio, Fusion
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Citations:
APA:
Ahn, J (2020). Mobile User Authentication Algorithm using Personalized Behavior Pattern Recognition. International Journal of Advanced Science and Technology (IJAST), ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, 137, 11-22. doi: 10.33832/ijast.2020.137.02.
MLA:
Ahn, Junho, “Mobile User Authentication Algorithm using Personalized Behavior Pattern Rec.” International Journal of Advanced Science and Technology, ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, vol. 137, 2020, pp. 11-22. IJAST, http://article.nadiapub.com/IJAST/Vol137/2.html.
IEEE:
[1] J. Ahn, "Mobile User Authentication Algorithm using Personalized Behavior Pattern Recognition." International Journal of Advanced Science and Technology (IJAST), ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, vol. 137, pp. 11-22, Apr 2020.
