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

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余米藍(Milan Yunidha Wantari) 查詢紙本館藏

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資訊工程學系

論文名稱

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)

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摘要(中)

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。
最後，在我們完成所有的實驗後，我們得到的結論是三種方法都有效的處理行為變化，因為他們的EER比較差的基準模型(領域適應)更好，訓練時間比最佳的基準模型(重新訓練)更短。最有效的方法是第二種方法，適應的最近質心分類器，它在提出的三種方法中有最佳的EER及最短的訓練時間，EER平均為2.55%。

摘要(英)

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%.

關鍵字(中)

★ 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

關鍵字(英)

★ implicit authentication
★ behavioral biometric
★ behavior changes
★ domain adaptation
★ support vector machine
★ adaptive nearest centroid classifier

論文目次

TABLE OF CONTENTS

摘要 i
ABSTRACT ii
Acknowledgment iii
TABLE OF CONTENTS iv
LIST OF FIGURES vi
LIST OF TABLES viii
Introduction 1
1.1. Background 1
1.2. Motivation 2
1.3. Research Objective 4
1.4. Limitation of the Study 5
1.5. Thesis Structure 5
Literature Review 7
2.1. Authentication 7
2.2. Biometric Authentication 8
2.3. Support Vector Machine 9
2.4. SVM Retraining 10
2.5. Transfer Learning 10
2.5.1. Domain Adaptation 12
2.6. Previous Research 12
2.6.1. Online learning Approach 12
2.6.2. Domain Adaptation Approach 12
2.7. Centroid Classifier 13
2.8. Euclidean Distance 13
2.9. Ensemble Learning 14
2.10. Equal Error Rate 14
Research Data 17
3.1. Data Collection 17
3.2. Raw Data to Feature Data 18
3.2.1. Touch Feature Set 18
3.2.2. Orientation Feature Set 20
3.3. Histogram 22
Research Method 23
4.1. System Architecture 23
4.2. Shift testing data 26
4.3. Adaptive Nearest Centroid Classifier 26
4.4. Ensemble Model 28
4.5. Training and Testing 29
Experimental Process and Result Analysis 37
5.1. Experiment Setup 37
5.2. Experiment Result 39
5.2.1. EER Comparison 39
5.2.2. Training Time Comparison 43
5.2.3. Testing Time Comparison 47
5.3. Discussion 48
Conclusion 51
6.1. Conclusion 51
6.2. Future Works 51
7. Bibliography 53

參考文獻

[1] S. Krishnamoorthy, “Identification of User Behavioural Biometrics for Authentication using Keystroke Dynamics and Machine Learning,” University of Windsor, 2018.
[2] S. Vongsingthong and S. Boonkrong, “A survey on smartphone authentication,” Walailak J. Sci. Technol., vol. 12, no. 1, pp. 1–19, 2015.
[3] H. Gamboa and A. Fred, “A Behavioural Biometric System Based on Human Computer Interaction,” Proc. Vol. 5404 Biometric Technol. Hum. Identif., 2004.
[4] K. Saeed, New directions in behavioral biometrics. 2016.
[5] D. K. Srivastava and L. Bhambhu, “Data Classification Using Support Vector Machine,” J. Theor. Appl. Inf. Technol., vol. 12, pp. 1–7, 2005.
[6] Y. Yang, J. Li, and Y. Yang, “The research of the fast SVM classifier method,” 2015 12th Int. Comput. Conf. Wavelet Act. Media Technol. Inf. Process. ICCWAMTIP 2015, no. 1, pp. 121–124, 2015.
[7] E. Bagarinao, T. Kurita, M. Higashikubo, and H. Inayoshi, “Adapting SVM image classifiers to changes in imaging conditions using incremental SVM: An application to car detection,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 5996 LNCS, no. PART 3, pp. 363–372, 2010.
[8] Qiang Yang and S. J. Pan, “A Survey on Transfer Learning,” IEEE Trans. Knowl. Data Eng., vol. 22, no. 10, pp. 1345--1359, 2010.
[9] “Domain adaptation,” 2019. [Online]. Available: https://en.wikipedia.org/wiki/Domain_adaptation.
[10] T. Das, “Machine Learning algorithms for Image Classification of hand digits and face recognition dataset,” Int. Res. J. Eng. Technol., vol. 04, no. 12, pp. 640–649, 2017.
[11] Pbarrett.net, “Euclidean Distance Whitepaper,” Tech. Whitepaper Ser. 6, 2005.
[12] M. Greenacre, “Chapter 5 Measures of distance between samples: Euclidean,” Univerysity Pompeu Fabra, pp. 1–10, 2008.
[13] Z.-H. Zhou, Ensemble learning. Springer, Boston, MA, 2009.
[14] C. Lin, C. Chang, and D. Liang, “Nonintrusive Authentication of Smartphone Users by Using Behavioral Biometrics Based on the Orientation Sensor and Touchscreen.”
[15] C. C. Lin, C. C. Chang, and D. Liang, “A novel non-intrusive user authentication method based on touchscreen of smartphones,” Proc. - 2013 Int. Symp. Biometrics Secur. Technol. ISBAST 2013, pp. 212–216, 2013.
[16] D.Wisnu, “Implicit Behavioral Authentication for Unstable Smartphones User based on Domain Adaptation,” NATIONAL CENTRAL UNIVERSITY, 2018.

指導教授

梁德容博士張欽圳博士(Dr. Deron Liang Dr. Chin-Chun Chang)

審核日期

2019-7-17

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