基於領域適應及Empirical Kernel Map處理行為改變的智慧型手機非侵入式身份識別系統

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姓名

毛拉納(Alfian Maulana Azhari) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

基於領域適應及Empirical Kernel Map處理行為改變的智慧型手機非侵入式身份識別系統
(Non-intrusive Behavioral Biometric Authentication on Smartphones for Behavior Change Handling Based on Domain Adaptation and Empirical Kernel Map)

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摘要(中)

現今智慧型手機已成為人類生活不可分割的一部分，每個人都至少擁有一部或多部的智慧型手機，人們習慣隨時隨地的使用手機以達成多種目的。因此，手機的內的資訊以及數據安全問題也越來越被重視，目前有許多研究顯示，批量學習、主動式學習以及多重再訓練等方法能可靠的用來識別使用者行為。依照行為的穩定性，智慧型手機使用者可以分成行為穩定以及行為不穩定兩種類型，穩定指的是使用者往往會以相同的方式滑動銀幕，模型不需要在短時間內做更新，而不穩定則指，使用者不但會進行長距離且快速的滑動，也可能突然進行短距離且慢速的滑動。在這兩種情況下，行為不穩定者的行為預測較穩定者更具挑戰性，而這種不穩定的滑動方式會影響數據在樣本空間的分布，因此我們也將其視為行為改變問題。本團隊發現利用使用者的新行為進行模型的重新訓練，在解決行為不穩定的情況下能得到很好的結果。然而，如果面對的是較大的資料集，則需要非常高的計算成本以及較長的訓練時間。此時，作為遷移學習方法之一的領域適應，可以在計算成本以及訓練時間上取得較好的效能。我們提出一個基於領域適應的方法可以在智能手機上提供非侵入式行為生物識別身份驗證，想法是對資料集進行分群，以便更容易識別用戶的行為，並集成一個相對穩定且對於行為變化較不敏感的特徵表示法，而實驗結果表明，該方法較Dyah[]在EER中的結果來的更加穩定，也證實了此方法可以處理使用者行為變化的問題。

關鍵字：implicit authentication、使用者識別、遷移學習、領域適應、支持向量機、K-means、kernel map

摘要(英)

Nowadays, smartphone has been almost like a primary human needs. Almost everyone has minimum amount of one of smartphone. They tend to check on or use their smartphone at anytime and anywhere for many purposes. Therefore, security is also a need to secure people’s data or information inside their smartphone. Some implicit authentication method such as batch learning, active learning, and retraining have shown promising results to recognize the behavior of the user, including the one with unstable behavior. Behavior in our case is how the user flicks on the smartphones’s screen. The stable users tend to flick the same way all the time while the unstable ones tend to change their way to flick and it might be caused by certain reasons. An example of unstable behavior is when the unstable users might flick fast and long in certain time but might flick slow and short another time. The problem where some users tend to change their way of flicking and give data that are more scattered all over the sample space is called behavior change problem. In case of solving the problem with unstable user, we have found that retraining approaches which aim to retrain the classifier based on the new behavior of users showed a very good result to handle the unstable behavior. However, it takes a high computational cost and a long training time if dealing with relatively big data set. On the other hand, one of transfer learning’s approach parts which is the domain adaptation may share a similar ability with better efficiency in terms of training time and computational cost. This work presents non-instrusive behavioral biometric authentication on smartphones for behavior change handling based on domain adptation and empirical kernel map. The idea is to cluster the data sets so that recognizing user’s behaviors is easier and to form new feature representation for the data sets which is stable and less sensitive to behavior change. The experimental results show that the proposed approach has stable results and are better than initial baseline and Dyah’s work in terms of EER. It also shows that the proposed approach can handle the behavior change problem.
Keywords: implicit authentication, user authentication, transfer learning, domain adaptation, support vector machine, k-means, kernel map

關鍵字(中)

★ 基於領域適應及Empirical Kernel Map處理行為改變的智慧型手機非侵入式身份識別系統

關鍵字(英)

★ Non-intrusive Behavioral Biometric Authentication on Smartphones for Behavior Change Handling Based on Domain Adaptation and Empirical Kernel Map

論文目次

摘要 i
ABSTRACT ii
Acknowledgments iv
TABLE OF CONTENTS v
LIST OF FIGURES vii
LIST OF TABLES ix
CHAPTER 1 INTRODUCTION 1
1.1. Background 1
1.2. Motivation 3
1.3. Research Objective 5
1.4. Thesis Structure 5
CHAPTER 2 LITERATURE REVIEW 6
2.1. Authentication 6
2.2. Support Vector Machine (SVM) 7
2.3. Domain Adaptation 8
2.4. K-means 10
2.5. Kernel Map or Kernel Trick 10
CHAPTER 3 RESEARCH METHOD 12
3.1. Experimental Method 12
3.1.1. Proposed Approach’s Training Phase 16
3.1.2. Proposed Approach’s Testing Phase 20
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 Results 27
4.2.1. EER Comparison 27
CHAPTER 5 CONCLUSION 32
5.1. Conclusion 32
5.2. Future works 34
BIBLIOGRAPHY 36

參考文獻

[1] Cha, S.-S., & Seo, B.-K. (2018). Smartphone use and smartphone addiction in middle school students in Korea: Prevalence, social networking service, and game use. Health Psychology Open.
[2] Mahfouz, A., Mahmoud, T. M., & Eldin, A. S. (October 2017). A Survey on Behavioral Biometric Authentication on Smartphones. Journal of Information Security and Applications.
[3] Wang, M., & Deng, W. (2018). Deep Visual Domain Adaptation: A Survey. ScienceDirect.
[4] Dr. Singh, S., & Yadav, A. M. (September-October 2013). Study of K-Means and Enhanced K-Means Clustering Algorithm. International Journal of Advanced Research in Computer Science.
[5] Kozak, S. (April 2019). Kernel Trick for the Cross-Section. SSRN.
[6] Idrus, S. Z., Cherrier, E., Rosenberger, C., & Schwartzmann, J.-J. (December 2013). A Review on Authentication Methods. HAL.
[7] Kotzanikolaou, P., & Douligeris, C. (2007). Network Security Current Status and Future Directions, chapter Computer Network Security: Basic Background and Current Issues.
[8] Ratha, N., Senior, A., & Bolle, R. (March 2001). Tutorial on automated biometrics. Proceedings of International Conference on Advances in Pattern Recognition. Rio De Janeiro, Brazil.
[9] Garcia, R. d., Lopez, C. A., Aghzout, O., & Alzola, J. R. (2003). Biometric identification systems. Elsevier.
[10] Tong, S., & Koller, D. (2001). Support Vector Machine Active Learning with Applications to Text Classification. Journal of Machine Learning Research.
[11] Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering (TKDE), 22(10):1345–1359.
[12] Gong, B., Grauman, K., & Sha, F. (2013). Connecting the dots with landmarks: Discriminatively learning domaininvariant features for unsupervised domain adaptation. International Conference on Machine Learning (ICML).
[13] Huang, J., Smola, J., Gretton, A., M., B. K., & Schölkopf, B. (2006). Correcting sample selection bias by unlabeled data. Advances in Neural Information Processing Systems (NIPS).
[14] Pan, S. J., Tsang, I. W., Kwok, J. T., & Yang, Q. (2011). Domain adaptation via transfer component analysis. IEEE Transactions on Neural Networks (TNN), 22(2):199-210.
[15] Ganin, Y., & Lempitsky, V. (2015). Unsupervised domain adaptation by backpropagation. International Conference on Machine Learning (ICML).
[16] Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., . . . Lempitsky, V. (2016). Domain-adversarial training of neural networks. The Journal of Machine Learning Research (JMLR), 17(1):2096–2030.
[17] Long, M., Cao, Y., Wang, J., & Jordan, M. I. (2015). Learning transferable features with deep adaptation networks. International Conference on Machine Learning (ICML).
[18] Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., & Darrell, T. (2015). Simultaneous deep transfer across domains and tasks. IEEE International Conference on Computer Vision (ICCV).
[19] Long, M., Zhu, H., Wang, J., & Jordan, M. I. (2016). Unsupervised domain adaptation with residual transfer networks. Advances in Neural Information Processing Systems (NIPS), pages 136–144.
[20] Long, M., Wang, J., & Jordan, M. I. (2017). Deep transfer learning with joint adaptation networks. International Conference on Machine Learning (ICML).
[21] Tzeng, E., Hoffman, J., Saenko, K., & Darrell, T. (2017). Adversarial discriminative domain adaptation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Wilkin, G. A., & Huang, X. (2007). K-Means Clustering Algorithms: Implementation and Comparison. Second International Multisymposium on Computer and Computational Sciences.

指導教授

梁德容張欽圳(Professor Deron Liang Professor Chin-Chun Chang)

審核日期

2019-8-7

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