整體學習之非侵入式手機使用者識別機制;A non-intrusive smartphone authentication approach using ensemble learning and histogram-based features

NCUIR > College of Electrical Engineering & Computer Science > Software Engineer > Electronic Thesis & Dissertation > Item 987654321/65492

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/65492

Title:	整體學習之非侵入式手機使用者識別機制;A non-intrusive smartphone authentication approach using ensemble learning and histogram-based features
Authors:	周彥竹;Chou,Yen-chu
Contributors:	軟體工程研究所
Keywords:	使用者識別;方位感測器;觸碰螢幕;整體學習;User authentication;Touch screen;Orientation sensor;Ensemble learning;Multiple Model
Date:	2014-07-24
Issue Date:	2014-10-15 15:33:09 (UTC+8)
Publisher:	國立中央大學
Abstract:	隨著智慧型手機應用程式的進步，使手機上儲存了許多重要的個人隱私，這使手機安全機制越來越被重視。在傳統的手機鎖上，都是一次驗證的機制，因此希望藉由讀取手機使用者的行為作不中斷的識別使用者。本研究會使用手機應用程式中能持續收集使用者操作資訊，以觸碰螢幕與方位感測器當作識別使用者的特徵資訊，並依照現有的相關文獻所遭遇到的問題提出改善並強化其識別效果。本研究最後會提出一個結合整體學習（ensemble learning）與Multiple Model的驗證機制，根據實驗此驗證機制的識別效果EER（Equal Error Rate）在1.5%左右，提升了現有的驗證機制。;With the advances in smartphone applications, data on the phone to save a lot of important personal privacy, which makes mobile phone security, is increasingly valued. In the traditional phone lock, are one-time validation mechanisms, so I hope by reading the phone sensor without interrupting the user′s behavior for authentication the user. This study will be used in mobile applications can continue to collect user operation information to touch the screen and orientation sensors to identify the user as a characteristic information, and in accordance with the existing literature encountered issues raised to improve and strengthen their identification effect. Finally, this study will propose a combination of the ensemble learning and authentication mechanism by multiple models, according to the experimental results of this verification mechanism to identify EER (Equal Error Rate) of 1.5% or less, to enhance the existing authentication mechanisms.
Appears in Collections:	[Software Engineer] Electronic Thesis & Dissertation

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