A Study of User Behavior Recognition-Based PIN Entry Using Machine Learning Technique


KIPS Transactions on Computer and Communication Systems, Vol. 7, No. 5, pp. 127-136, May. 2018
10.3745/KTCCS.2018.7.5.127, Full Text:
Keywords: Smartphone, Authentication Protocol, User Behavior Recognition, Machine Learning, Pin
Abstract

In this paper, we propose a PIN entry method that combines with machine learning technique on smartphone. We use not only a PIN but also touch time intervals and locations as factors to identify whether the user is correct or not. In the user registration phase, a remote server was used to train/create a machine learning model using data that collected from end-user device (i.e. smartphone). In the user authentication phase, the pre-trained model and the saved PIN was used to decide the authentication success or failure. We examined that there is no big inconvenience to use this technique (FRR: 0%) and more secure than the previous PIN entry techniques (FAR : 0%), through usability and security experiments, as a result we could confirm that this technique can be used sufficiently. In addition, we examined that a security incident is unlikely to occur (FAR: 5%) even if the PIN is leaked through the shoulder surfing attack experiments.


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Cite this article
[IEEE Style]
C. Jung, Z. Dagvatur, R. Jang, D. Nyang and K. Lee, "A Study of User Behavior Recognition-Based PIN Entry Using Machine Learning Technique," KIPS Transactions on Computer and Communication Systems, vol. 7, no. 5, pp. 127-136, 2018. DOI: 10.3745/KTCCS.2018.7.5.127.

[ACM Style]
Changhun Jung, Zayabaatar Dagvatur, RhongHo Jang, DaeHun Nyang, and KyungHee Lee. 2018. A Study of User Behavior Recognition-Based PIN Entry Using Machine Learning Technique. KIPS Transactions on Computer and Communication Systems, 7, 5, (2018), 127-136. DOI: 10.3745/KTCCS.2018.7.5.127.