An active smartphone authentication method based on daily cyclical activity

TL;DR

This paper introduces a smartphone user authentication method based on analyzing daily cyclical activity patterns using the Prophet algorithm, which is robust to missing data and outliers, enabling accurate identification with minimal data.

Contribution

It proposes a novel behavioral activity cycle-based user identification system that requires small datasets and balances energy consumption, data quantity, and accuracy.

Findings

Achieved only 8.20% MSE in predicting 'Jumping' behavior.

System can identify users after 5 activity cycles.

Robustness to missing data and outliers demonstrated.

Abstract

Smartphones have become an important tool for people's daily lives, which brings higher security requirements in high-risk application areas, for example, mobile payment. Although the combination of physical password, fingerprint and facial recognition have improved the security to a certain extent, there still exists a high risk of being decrepted. This paper attempts an algorithm which is more suitable for studying human partial periodic activity, namely Prophet algorithm. This algorithm has strong robustness for missing data and trend change, and can deal with outliers well. The experimental results on the UniMiB SHAR DATA show that the user simply needs to do 5 cycles of specified actions to realize the prediction of the next time series. The Error analysis of cross validation was applied to 4 different indicators, and the Mean Squared Error of the optimal result "Jumping" behavior was only 8.20%. With these appealing features, The main contribution of this paper is to propose a smart phone user identification system based on behavioral activity cycle, which can be replicated in other behavioral studies. Another outstanding feature of such a system is the capability of fitting models using small data set by exploiting behavioral characteristics derived from periodicity and thus reducing dependence on sensor scanning frequency, therefore the system balances among energy consumption, data quantity and fitting accuracy.