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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/68738

    Title: 基於非侵入式手機使用者識別機制即時檢測使用者操作行為收集其建模資料的方法;Using Active Learning to Collect User’s Behavior for Training Model. Base on Non-intrusive Smartphone Authentication
    Authors: 陳懿婷;Chen,Yi-Ting
    Contributors: 資訊工程學系
    Keywords: 非侵入式識別機制;使用者識別;主動學習;支持向量機;non-intrusive authentication mechanism;user authentication;active learning;support vector machine
    Date: 2015-07-21
    Issue Date: 2015-09-23 14:22:50 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 隨著科技進步,智慧型手機可以經由網路連結,達到收發電子信箱、線上交易(網路銀行、電子商務)、社群軟體等功能,無形中使智慧型手機內部資料成為有心人士覬覦的目標。
    ;In order to protect the data within the smartphone, intrusive and non-intrusive user authentication mechanisms were developed. Traditional authentication mechanisms like number lock and pattern lock are intrusive user authentication mechanism. Non-intrusive user authentication mechanism doesn’t require any user interface, but collect user’s behavior in the background and authenticate it.
    Several non-intrusive authentication mechanisms were proposed, but all of them don’t care about the selection of training samples. Actually user to provide the training samples can be very time-consuming.
    This study proposes a method to collect real-time detection with the use of active learning support vector machine choose training samples to identify the effect of the acceptable range in a small amount of training samples construction of non-invasive identification mechanism.
    First, for this study, we propose two stages collection methodology of behavior.
    The first stage of the collection is to detect which behavior scenario need.
    The second stage of the collection is to for the first phase of the analysis of the situation and to collect training data modeling.
    Finally, this study presents a method to collect real-time detection (active learning) compared with batch learning, results are more than half of users to significantly reduce the training sample to achieve a good recognition results through this method.
    Appears in Collections:[資訊工程研究所] 博碩士論文

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