Optimized Active Learning for User’s Behavior Modelling based on Non-Intrusive Smartphone - Ika Kusumaning Putri; Deron Liang; Sholeh Hadi Pramono; Rahmadwati Rahmadwati (Institute of Advanced Engineering and Science, 2017)
In order to protect the data in the smartphone, there is some protection mechanism that has been used. The current authentication uses PIN, password, and biometric-based method. These authentication methods are not sufficient due to convenience and security issue. Non-Intrusive authentication is more comfortable because it just collects user’s behavior to authenticate the user to the smartphone. Several non-intrusive authentication mechanisms were proposed but they do not care about the training sample that has a long data collection time. This paper propose a method to collect data more efficient using Optimized Active Learning. The Support Vector Machine (SVM) used to identify the effect of some small amount of training data. This proposed system has two main functionalities, to reduce the training data using optimized stop rule and maintain the Error Rate using modified model analysis to determine the training data that fit for each user. Finally, after we done the experiment, we conclude that our proposed system is better than Threshold-based Active Learning. The time required to collect the data can reduced to 41% from 17 to 10 minutes with the same Error Rate.