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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/72635

    Title: 基於非侵入式手機使用者識別機制即時檢測結合收斂方法收集使用者操作行為資料;Active Learning with Convergent Stop Rule to Collect User’s Behavior for Training Model Base on Non-intrusive Smartphone Authentication
    Authors: 潘珮玟;Pan, Pei-Wen
    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
    Date: 2016-10-07
    Issue Date: 2017-01-23 17:09:17 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 隨著科技進步,人們可使用智慧型手機進行收發電子信箱、線上交易(網路銀行、電子商務)、社群軟體等功能,也使智慧型手機內部資料成為有心人士覬覦的目標。
      本研究提出改善既有主動學習方法的停止條件及新樣本情境檢測方式,分析使用者Validation Accuracy曲線,增加一收斂停止方法,並改用支持向量機超平面最近距離點情境分析。最後本論文提出的改善後即時檢測收集方法與其原先比較,實驗結果則能於相同識別效果下,減少一半的訓練資料收集時間。;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 them still have problem needed too much data for training. 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.
    For this study, we propose new stopping rule and model analysis with proposed active learning method. We analyze line of SVM validation accuracy, add a new stopping rule with convergence, and replace support vector with the closest sample points as standard for model analysis.
    Finally, this study presents an optimal method to collect real-time detection (active learning) compared with old version, results are total reduce half of the training time with a same recognition results of old version.
    Appears in Collections:[資訊工程研究所] 博碩士論文

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