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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/81087

    Title: 非侵入式智慧型手機使用者生物識別機制之行為變化快速適應;Fast Adapting Behavior Changes in Implicit Behavioral Biometric-Based Authentication for Smartphone User
    Authors: 余米藍;Wantari, Milan Yunidha
    Contributors: 資訊工程學系
    Keywords: 隐式认证;行为生物特征识别;行为改变;领域适应;支持向量机;自适应最近质心分类器;implicit authentication;behavioral biometric;behavior changes;domain adaptation;support vector machine;adaptive nearest centroid classifier
    Date: 2019-07-17
    Issue Date: 2019-09-03 15:33:46 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 目前傳統的智慧型手機識別機制使用PIN碼、密碼和生物識別方法。問題是已解鎖的智慧型手機會保持解鎖狀態直至再次被鎖定。如此一來,會有一段時間可以使攻擊者竊取未鎖定的手機並竊取手機上的所有資料。連續識別方法需要為智慧型手機提供雙重安全性。非侵入式生物識別機制較好是因為使用者不會對識別有所感覺。然而,使用者行為會隨時間而變化,這會使預測不準確。每隔一段時間重新訓練是一種方法,但會花費許多時間。我們提出三種方法來建立一個能夠適應行為變化而不需重新訓練的模型。第一個方法是條件式領域適應。第二個方法是適應的最近質心分類器。第三個方法是整體學習。
    ;The current traditional smartphone authentication mechanism uses PIN, password, and biometric-based method. The problem is, the smartphones that were unlocked will stay unlock until it is actively locked again. There always exists a time frame when an attacker can steal the unlocked phone and steal all data on the device. The continuous authentication method needs to give double security to the smartphone. Implicit authentication method behavioral biometric-based is more comfortable because the user will not realize the authentication phase. But, human behavior changes over time that will make the prediction not accurate. Retraining over time can be a solution, but it will take much time. We propose three approaches to build a model that can adapt to behavior changes without retraining. The first approach is conditional domain adaptation. The second approach is the adaptive nearest centroid classifier. And the third approach is an ensemble model.
    Finally, after we did the experiment, we conclude that all approach in this work effective to handle behavior changes because has better EER than worse baseline (domain adaptation) and shorter training time than best baseline(retraining). The most effective approach was the second approach, adaptive nearest centroid classifier, that have the best EER and shortest training time among the proposed approach in this work with an average EER 2.55%.
    Appears in Collections:[資訊工程研究所] 博碩士論文

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