關鍵字:非侵入式識別，使用者識別，主動學習方法，支持向量機器 ;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. The Threshold-based active learning has proposed the method that cut down the training data but it makes the error rate increase. In this research, we 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. First, to cut down the training data using optimized stop rule. Second, 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 cut down to 41% from 17 to 10 minutes with the same Error Rate.
Keywords: non-intrusive authentication, user authentication, active learning, support vector machine
