博碩士論文 104522607 詳細資訊




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姓名 蘇菲亞(Sufia Adha Putri)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 非侵入式手機使用者識別機制 : 線上機械學習研究
(Nonintrusive Behavioral Biometric Authentication on Smartphones: An Online Learning Approach)
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摘要(中) 傳統驗證方法如PIN和密碼因容易被破解或是複製已經被證明是不安全的,為了解決這個問題,在數位安全機制的研究上出現越來越多針對生物識別安全的實踐。基於生物特徵的認證機制的目標是利用生理或行為特徵來驗證合法使用者身份。雖然大多數的生物識別系統使用物理資料作為特徵,但生理特徵仍然容易被偽造。本研究提出了使用生物識別資料,特別是行為資料作為特徵的智慧型手機認證系統。使用行為資料作為特徵帶來的優點是認證密鑰會適應使用者特質,因此使用者不需要記住認證密鑰,且行為數據很難被詐騙、仿冒。此外,在這項研究中所提出的認證系統是基於非侵入式方法設計的,非侵入式方法可以更好的在背景運行以監測使用者行為。

過往的生物識別安全文獻當中,多半是使用批量學習方法來訓練驗證系統。本研究中,採用線上學習方式來提供了一種新穎的方法,為提出的系統隨時間推移建構用戶模型。線上學習方式不僅較符合現實世界的情況,還能提供比批量學習更快的訓練時間。實驗結果顯示,線上學習方式比過去在這一領域的其他方法快75%。線上學習方式的另一個好處是該模型能夠適應使用者行為變化。實驗結果顯示,線上學習方式的EER值較其他不會更新的模型較低,線上學習方法也讓模型隨時間推移改進,現在時間點的註冊者模型的EER值相較於之前時間點更新的模型來的更低。本研究也對我們提出的線上學習方法和與批量學習方法使用盒鬚圖統計分析方法進行比較效能。根據實驗結果顯示,本研究提出的方法之第一四分位數優於批次學習,而中位數接近於批量學習
摘要(英) The traditional authentication mechanisms such as Personal Identification Number (PIN) and password had proven to be insecure as they can be cracked or duplicated easily. To address this issue, the advancement of research on digital security mechanism has seen an increasing interest on the implementation of biometric security system. The objective of biometric-based authentication mechanism is to utilize either physical or behavioral feature to authenticate the genuine users. Even though most biometric authentication systems use physical data as the feature, the physical feature is still prone to the spoofing attack. In this work, a smart-phone authentication system that uses biometric data, specifically behavioral data, to authenticate user is proposed. Using behavioral data as a feature gives the benefit that the authentication key will adapt to the user characteristic so the users do not need to remember the authentication key. The behavioral feature is also difficult to be spoofed and imitated. Moreover, the proposed authentication system is designed based on the non-intrusive approach. The proposed non-intrusive authentication mechanism offers a better solution as the system monitors user′s behavior in the background.

Generally, most researches in the biometric security mechanism use batch learning approach to train authentication system. In this work, online learning approach is adopted to offer a novel method to build the user model over a period of time. Not only mimics the real world situations, online learning approach offers a faster training time when it is compared to batch learning. In the experiment results, the online learning approach has an around 75\% faster training time compared with the previous works in this field.

Another benefit of online learning approach is the capability of the model to be adaptive to user behavior change. In the experiment results, the online learning approach shows lower Equal Error Rate (EER) than the model that is never updated. The online learning approach also shows the model improvement over period of time. Comparing the result of the updated models in online learning approach over a period of time, the current user model has a lower EER than the previously updated model. In this work, the performance of proposed online learning approach is also compared to batch learning approach using box plot statistical analysis. From the experiment results, it showed that the first quartile of proposed work is better than the batch learning and the proposed work has median value near to the result of batch learning.
關鍵字(中) ★ 驗證
★ 手機
★ 安全
★ 生物特徵
關鍵字(英) ★ authentication
★ smart phone
★ security
★ biometric
論文目次 摘要 i
Abstract ii
Acknowledgement iv
Contents v
List of Figuresvi
List of Tablesviii
Description of symbols x
一、 Introduction 1
1.1 Background 1
1.2 Motivation 2
1.3 Research Objective 2
1.4 Thesis Structure 3
二、 Literature Review 5
2.1 Authentication 5
2.1.1 Traditional Authentication Mechanism 6
2.1.2 Re-Authentication Mechanism 7
2.2 Biometric Identification 7
2.3 Batch Processing and Learning 9
2.4 Active Learning 10
2.5 Online Processing and Learning 11
2.6 Support Vector Machine 12
2.7 Min-max Strategy 13
2.8 Iterative RELIEF Feature Weighting for Online Learning 14
2.9 Previous Works 14
2.9.1 Batch Learning 14
2.9.2 Active Learning 15
三、 Research Method 17
3.1 System Overview 18
3.2 Data Collection 20
3.3 Training Data Selection 21
3.4 Training Process 22
3.4.1 Calculating User Feature Weight 23
3.4.2 Kernel Mapping 23
3.4.3 Pool Concepts 24
3.4.4 Updating Feature Weight 25
3.5 Testing Data Selection 26
3.6 Online Learning 27
四、 Experimental Process and Result Analysis 28
4.1 Experiment Methods 28
4.2 Data Collection 28
4.3 Data Preprocessing 29
4.3.1 Touch Features Set 29
4.3.2 Orientation Features Set 31
4.4 System Modeling 33
4.5 Experiment Procedure 34
4.6 Experiment Result 35
4.6.1 User’s Improvement Classification based on EER 36
4.6.2 User’s Adaptivity Classification based on EER 38
4.6.3 Comparison of the EER of proposed work with Batch Learning 40
五、 Conclusion 46
5.1 Contribution 46
5.2 Conclusion 46
5.3 Future Works 47
5.3.1 Using different number of cluster 47
5.3.2 Remapping the hyperplane 48
5.3.3 Learning Rate calculation using sample data 48
References 49
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指導教授 梁德容、張欽圳(Deron Liang Chin-Chun Chang) 審核日期 2017-8-15
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