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- 2018
基于CNN和RNN的自由文本击键模式持续身份认证
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Abstract:
个人击键节奏模式具有很难被模仿的特点并可以用于身份认证。根据个人自由文本输入时的击键数据可以学习到个人独有的击键模式。基于对用户自由文本击键输入的检测，能够在不影响用户输入的情况下完成对用户身份的持续认证。该文提出将整体击键数据划分成固定长度的击键序列，并且根据击键的时间特征将击键序列中的击键时间数据转化成击键向量。使用卷积神经网络（convolutional neural networks，CNN）加循环神经网络（recurrent neural networks，RNN）的模型进行个人击键向量序列进行学习，用于身份认证。结果表明：模型使用公开数据集进行实验获得最优拒真率（false rejection rate，FRR）为1.95%，容假率（false acceptance rate，FAR）为4.12%，相等错误率（equal error rate，EER）为3.04%。
Abstract：Personal keystroke input patterns are difficult to imitate and can be used for identity authentication. The personal keystroke input data for a free-text can be used to learn the unique keystroke mode of a person. Detection based on a user's free-text keystrokes can be used for continuous identity authentication without affecting the user input. This paper presents a model that divides the keystroke data into fixed-length keystroke sequences and converts the keystroke time data in the keystroke sequence into a keystroke vector according to the time characteristics of the keystrokes. A convolutional neural network and a recurrent neural network are then used to learn the sequences of the personal keystroke vectors for identity authentication. The model was tested on an open data set with an optimal false rejection rate (FRR) of 1.95%, a false acceptance rate (FAR) of 4.12%, and an equal error rate (EER) of 3.04%.
| [1] | MAAS A, HEATHER C, DO C T, et al. Offering verified credentials in massive open online courses:MOOCs and technology to advance learning and learning research (Ubiquity Symposium)[J]. Ubiquity, 2014, 2014(5):1-11. |
| [2] | RYBNIK M, TABEDZKI M, ADAMSKI M, et al. An exploration of keystroke dynamics authentication using non-fixed text of various length[C]//Proceedings of 2013 International Conference on Biometrics and Kansei Engineering. Tokyo, Japan:IEEE, 2013:245-250. |
| [3] | ALSHANKETI F, TRAORE I, AHMED A A. Improving performance and usability in mobile keystroke dynamic biometric authentication[C]//Proceedings of 2016 IEEE Security and Privacy Workshops. San Jose, USA:IEEE, 2016:66-73. |
| [4] | YADAV J, PANDEY K, GUPTA S, et al. Keystroke dynamics based authentication using fuzzy logic[C]//Proceedings of 2017 International Conference on Contemporary Computing. Noida, India:IEEE, 2017:1-6. |
| [5] | MAXION R A, KILLOURHY K S. Keystroke biometrics with number-pad input[C]//Proceedings of 2010 IEEE/IFIP International Conference on Dependable Systems & Networks. Chicago, USA:IEEE, 2010:201-210. |
| [6] | VURAL E, HUANG J J, HOU D Q, et al. Shared research dataset to support development of keystroke authentication[C]//Proceedings of 2014 IEEE International Joint Conference on Biometrics. Clearwater, USA:IEEE, 2014:1-8. |
| [7] | GUNETTI D, PICARDI C. Keystroke analysis of free text[J]. ACM Transactions on Information and System Security, 2005, 8(3):312-347. |
| [8] | BERGADANO F, GUNETTI D, PICARDI C. User authentication through keystroke dynamics[J]. ACM Transactions on Information and System Security, 2002, 5(4):367-397. |
| [9] | KAMBOURAKIS G, DAMOPOULOS D, PAPAMARTZIVANOS D, et al. Introducing touchstroke:Keystroke-based authentication system for smartphones[J]. Security and Communication Networks, 2016, 9(6):542-554. |
| [10] | ANTAL M, SZABó L Z. An evaluation of one-class and two-class classification algorithms for keystroke dynamics authentication on mobile devices[C]//Proceedings of 2015 International Conference on Control Systems and Computer Science. Bucharest, Romania:IEEE, 2015:343-350. |
| [11] | BOURS P. Continuous keystroke dynamics:A different perspective towards biometric evaluation[J]. Information Security Technical Report, 2012, 17(1-2):36-43. |
| [12] | HUANG J J, HOU D Q, SCHUCKERS S, et al. Effect of data size on performance of free-text keystroke authentication[C]//Proceedings of 2015 IEEE International Conference on Identity, Security and Behavior Analysis. Hong Kong, China:IEEE, 2015:1-7. |
| [13] | SUN Y, CEKER H, UPADHYAYA S. Shared keystroke dataset for continuous authentication[C]//Proceedings of 2016 IEEE International Workshop on Information Forensics and Security. Abu Dhabi, United Arab Emirates:IEEE, 2016:1-6. |
| [14] | MURPHY C, HUANG J J, HOU D Q, et al. Shared dataset on natural human-computer interaction to support continuous authentication research[C]//Proceedings of 2017 IEEE International Joint Conference on Biometrics. Denver, USA:IEEE, 2017:525-530. |
| [15] | ALI M L, THAKUR K, TAPPERT C C, et al. Keystroke biometric user verification using hidden Markov model[C]//Proceedings of 2016 IEEE International Conference on Cyber Security and Cloud Computing. Beijing, China:IEEE, 2016:204-209. |
| [16] | HUANG J J, HOU D Q, SCHUCKERS S, et al. Benchmarking keystroke authentication algorithms[C]//Proceedings of 2017 IEEE Workshop on Information Forensics and Security. Rennes, France:IEEE, 2017:1-6. |
| [17] | SHIMSHON T, MOSKOVITCH R, ROKACH L, et al. Continuous verification using keystroke dynamics[C]//Proceedings of 2010 International Conference on Computational Intelligence and Security. Nanning, China:IEEE, 2010:411-415. |
| [18] | AHMED A A, TRAORE I. Biometric recognition based on free-text keystroke dynamics[J]. IEEE Transactions on Cybernetics, 2014, 44(4):458-472. |
| [19] | PUTRI A N, ASNAR Y D W, AKBAR S. A continuous fusion authentication for Android based on keystroke dynamics and touch gesture[C]//Proceedings of 2016 International Conference on Data and Software Engineering. Denpasar, Indonesia:IEEE, 2016:1-6. |
| [20] | LI B R, SUN H, GAO Y, et al. Enhanced free-text keystroke continuous authentication based on dynamics of wrist motion[C]//Proceedings of 2017 IEEE Workshop on Information Forensics and Security. Rennes, France:IEEE, 2017:1-6. |
