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研究生:尤岱亞
研究生(外文):Dyah Ayu Marhaeningtyas Galuh Wisnu
論文名稱:基於領域適應性之非侵入式手機使用者識別機制針對無固定操作習慣之使用者
論文名稱(外文):Implicit Behavioral Authentication for Unstable Smartphones User based on Domain Adaptation
指導教授:梁德容梁德容引用關係張欽圳
指導教授(外文):Deron LiangChin-Chun Chang
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:英文
論文頁數:52
中文關鍵詞:隱式認證用戶認證轉移學習域適應support vector machine
外文關鍵詞:implicit authenticationuser authenticationtransfer learningdomain adaptationsupport vector machine
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近年來,智慧型手機被世界上大多數人廣泛使用, 因此考慮智慧型手機的資訊安全已經成為不可或缺的一環, 一些非侵入式方法包括批量學習、主動學習法、和再訓練在對應使用著上已經有著些有趣的結果, 在一些特別的情況下,像是使用者沒有固定的使用習慣,我們發現根據使用者的新行為來更新模型的再訓練方法可以有效的處理這些資料, 然而,當處理較大的資料集時,則是需要相當高的計算成本跟較長的訓練時間, 另一方面,轉移學習方法的一部份即所謂的領域適應性可以有著相似的效能,但有著更高的效率, 我們對無固定操作習慣之使用者提供了基於領域適應性之非侵入式手機使用者識別機制, 此方法是將目標數據從從目標域映射到原始域,因此我們可以將原始模型應用於目標數據, 實驗結果表示,跟基本方法相比,我們所提出的方法準確性更高,訓練和測試的時間也更快, 根據我們提出的方法能有效且滿足使用者行為的任何條件。
In recent years, the smartphone is widely used by most people in the world. Thus, the smartphone security has become a necessity since smartphones have increased in popularity. Several implicit authentication approaches include batch learning, active learning, and retraining have shown interesting results to map the behavior of the user, especially unstable user. In the particular case for resolving unstable user, we found that retraining approaches which aim to retrain the classifier based on the new behavior of users showed the good ability to handle the data. Moreover, it requires high computational cost and takes a long training time if dealing with larger dataset. On the other hand, one of the parts of the transfer learning approach which is so-called domain adaptation may share a similar ability with better efficiency. This work presents implicit behavioral authentication for unstable smartphone user based on domain adaptation. The idea of this approach is to map the target data from the target domain to the original domain thus we can apply the original model to the target data. The experimental result shows that the proposed method is better in term of EER compared to the basic approach and faster in term of training time than the retraining approach. However, the effectiveness yet satisfactory performances are letting this approach capable of handling any condition of user behavior data.
摘要 i
ABSTRACT ii
Acknowledgment iii
TABLE OF CONTENTS iv
LIST OF FIGURES vi
LIST OF TABLES viii
CHAPTER 1 INTRODUCTION 1
1.1. Background 1
1.2. Motivation 3
1.3. Research Objective 4
1.4. Limitations of the Study 4
1.5. Thesis Structure 5
CHAPTER 2 LITERATURE REVIEW 6
2.1. Authentication 6
2.2. Support Vector Machine 9
2.2.1. Online Support Vector Machine 10
2.3. Retraining Strategy 11
2.4. Transfer Learning 11
2.4.1. Domain Adaptation 14
CHAPTER 3 RESEARCH METHOD 16
3.1. Experimental Method 16
3.2. Data Collection 22
3.2.1. Touch Feature Set 22
CHAPTER 4 EXPERIMENTAL PROCESS AND RESULT ANALYSIS 25
4.1. Experiment Setup 25
4.2. Experiment Result 26
4.2.1. EER Comparison 26
4.2.2. Training Time Comparison 31
CHAPTER 5 CONCLUSION 36
5.1. Conclusion 36
5.2. Future Works 37
BIBLIOGRAPHY 38
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