博碩士論文 995202107 詳細資訊




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姓名 許振揚(Jhen-yang Syu)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 非侵入式多模組之手機使用者識別機制 :基於動態方法
(A non-intrusive and multimodel authentication mechanism of smartphones: a dynamics-based approach)
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摘要(中) 隨著手機功能的進步,許多應用程式如雨後春筍般出現,其中有些應用程式與使用者隱私資訊或是金錢資訊有關,若遭到盜用則損失難以估計。傳統的驗證只對使用者驗證一次,但若是密碼遺失或是遭竊取,則無法保護手機裡的資料,所以本論文提出一個直接利用手機上的方位感測器和觸控螢幕去擷取使用者操作行為並藉此行為資訊持續且不中斷的驗證使用者。本研究使用使用者上下拖曳和左右拖曳兩個應用程式來模擬使用者平時操作手機的情境,並擷取使用者在操作時的行為。根據實驗的結果顯示驗證機制的EER(Equal Error Rate)在7%以下,證實了結合方位感測器和觸控銀幕可用在使用者識別上。
摘要(英) With the progress of the smartphone, many applications appear fast. Some applications have something to do with the user’s private information and individual finance, hence, if the information is stolen, it will be a great loss. Traditional authentication only authenticates user’s password once, but if the password is stolen, the information of the smartphone will not be protected.
Therefore, in this paper, we propose a method that we directly use the smartphone orientation and touch screen to catch the information of user’s behavior, then according to this information, the smartphone will non-intrusively and continuously authenticate the smartphone user.
In this study, we use two applications which include user up/down flick and left/right flick to simulate user’s situation of using smartphone, and collect the information of user’s behavior. The experimental result indicates that the EER(Equal Error Rate) of authentication mechanism less than 7%, it confirms that the combination of orientation sensor and touch screen is useful in user verification.
關鍵字(中) ★ 方位感測器
★ 觸控螢幕
★ 使用者識別
關鍵字(英) ★ Orientation sensor
★ Touch screen
★ user authenticate
論文目次 中文摘要 i
Abstract ii
誌謝 iii
目錄 v
圖目錄 vii
表目錄 ix
一、 緒論 1
1-1 前言 1
1-2 研究動機 3
1-3 研究目的 7
1-4 論文架構 7
二、 文獻探討 8
2-1 重新驗證機制(Re-Authentication) 8
2-2 方位感測器(Orientation sensor) 9
2-3 觸控螢幕(Touch screen) 11
三、 實驗設計 13
3-1 資料收集 13
3-2 資料前處理 15
3-2-1 特徵選取 15
3-2-2 資料轉換 20
3-3 系統建模(System modeling) 20
3-3-1 抽樣方式 20
3-3-2 特徵萃取(Feature Extraction) 23
3-3-3 分類器(Classifier) 23
3-3-4 模型(Models)訓練與測試 25
四、 實驗結果與分析 27
4-1 抽樣實驗 27
4-2 特徵萃取實驗 30
4-3 個別Sensor實驗結果與分析 31
4-4 Multi-modalities實驗結果與分析 33
五、 研究結論與未來展望 35
5-1 結論 35
5-2 未來展望 36
參考文獻 38
附錄一 40
附錄二 41
附錄三 42
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[2] " what do you use your smartphone for"
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[3] "Consumers and Mobile Financial Services."
http://www.federalreserve.gov/econresdata/mobile-device-report-201203.pdf
[4] "2012年行動付款交易值將超越1,715億美元."
http://iknow.stpi.org.tw/post/Read.aspx?PostID=7162
[5] "Survey says 70% don’t password-protect mobiles: download free Mobile Toolkit."
http://nakedsecurity.sophos.com/2011/08/09/free-sophos-mobile-security-toolkit/
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[15] S. S. Khan, "Kernels for One-Class Nearest Neighbour Classification and Comparison of Chemical Spectral Data," College of Engineering and Informatics, National University of Ireland, 2010.
[16] K. Revett, H. Jahankhani, S. T. Magalhaes, and H. M. D. Santos, "A survey of user authentication based on mouse dynamics," Communications in Computer and Information Science (Global E-Security), vol. vol. 12, pp. 210-219, 2008.
[17] M. K. Jiawei Han, Jian Pei,, "Data Mining, Second Edition, Second Edition : Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)," 2006.
[18] C. Yu, R. Zhang, Y. Huang, and H. Xiong, "High-dimensional knn joins with incremental updates," Geoinformatica, vol. 14, pp. 55-82, 2010.
[19] G. Fung and O. L. Mangasarian, "Incremental support vector machine classification,", 7 th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 2002, pp. 247-260.
[20] P. E. Utgoff, "Incremental induction of decision trees," Machine Learning, Springer,vol. 4, pp. 161-186, 1989.

指導教授 梁德容(Deron Liang) 審核日期 2012-10-5
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