PIN Authentication Using Multi-Model Anomaly Detection in Keystroke Dynamics (original) (raw)

2019 2nd International Conference on Signal Processing and Information Security (ICSPIS), 2019

Abstract

This paper investigates the design of anomaly detectors and feature sets for Personal Information Number (PIN) authentication on mobile devices. The work involved a selection of raw data feature sets that are extracted from mobile devices, such as finger area, pressure, and timestamp. A set of primary and secondary authentication features have been formulated, which are calculated from the raw data features. The anomaly detectors are based on the outlier concept, where an input PIN's calculated feature is classified as imposter value if it is outside an acceptable zone from a central value such as the mean or median of a set of training values. The Z-Score method is used as the distance function of the anomaly detectors, and three versions are investigated; the standard deviation-based Z-Score, the modified Z-Score which uses the Median-Absolute-Deviation (MAD) and the Average-Absolute-Deviation (AAD) Z-Score function. The three single models are combined into ensemble models. Experimental work resulted in a PIN dataset from 70 subjects, where the data included genuine and imposter PIN data. The primary and secondary authentication features dataset were calculated from the raw features dataset. The results showed that the merged AAD and MAD ensemble model achieved the lowest error rate.

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