An Interpretable Free-text Keystroke Event Sequence Classification Model

ZHANG Chang; HAN Jihong; ZHANG Yuchen; LI Fulin

doi:10.11999/JEIT211567

Volume 45 Issue 2

Feb. 2023

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(2): 698-706

ZHANG Chang, HAN Jihong, ZHANG Yuchen, LI Fulin. An Interpretable Free-text Keystroke Event Sequence Classification Model[J]. Journal of Electronics & Information Technology, 2023, 45(2): 698-706. doi: 10.11999/JEIT211567

Citation:

ZHANG Chang, HAN Jihong, ZHANG Yuchen, LI Fulin. An Interpretable Free-text Keystroke Event Sequence Classification Model[J]. Journal of Electronics & Information Technology, 2023, 45(2): 698-706. doi: 10.11999/JEIT211567

Citation:

PDF( 7222 KB)

An Interpretable Free-text Keystroke Event Sequence Classification Model

doi: 10.11999/JEIT211567

Information Engineering University, Zhengzhou 450000, China

Received Date: 2021-12-27
Accepted Date: 2022-06-01
Rev Recd Date: 2022-05-22

Available Online: 2022-06-07

Publish Date: 2023-02-07

Abstract

Abstract

TypeNet is a Siamese network model based on two-layer Long-Short Term Memory (LSTM) branch structure. It has achieved good results in the classification of free-text keystroke event sequences, but lacks interpretation. Therefore, the TypeNet model is transformed, and a Siamese network TypeNet II based on a single-layer LSTM branch structure is proposed. A multi-layer perceptron is used to measure the similarity of two feature sequences reflected by the absolute value of the difference between the output embeddings of the two branches. After the model training, the multi-layer perceptron is simulated by a multivariate binomial expression. Based on the obtained multivariate binomial expression, the classification judgment of the model can be explained. The experimental results show that the classification effect of the TypeNet II model exceeds the existing TypeNet model. The results of multivariate binomial regression are generalized, and there is a nonlinear relationship between the absolute value of the difference of the embeddings and the similarity measure.
- Siamese network,
- Long-Short Term Memory (LSTM),
- Keystroke,
- Multi-layer perceptron,
- Interpretability

FullText(HTML)

References(20)

References

[1]	GAINES R S, LISOWSKI W, PRESS S J, et al. Authentication by keystroke timing: Some preliminary results[R]. R-2526-NSF, 1980.
[2]	ACIEN A, MORALES A, MONACO J V, et al. TypeNet: Deep learning keystroke biometrics[J]. IEEE Transactions on Biometrics, Behavior, and Identity Science, 2022, 4(1): 57–70. doi: 10.1109/TBIOM.2021.3112540
[3]	MONROSE F and RUBIN A D. Keystroke dynamics as a biometric for authentication[J]. Future Generation Computer Systems, 2000, 16(4): 351–359. doi: 10.1016/S0167-739X(99)00059-X
[4]	CURTIN M, TAPPERT C, VILLANI, et al. Keystroke biometric recognition on long-text input: A feasibility study[C]. Student/Faculty Research Day, CSIS, Pace University, New York City, USA, 2006.
[5]	AYOTTE B, HUANG Jiaju, BANAVAR M K, et al. Fast continuous user authentication using distance metric fusion of free-text keystroke data[C]. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, USA, 2019.
[6]	AYOTTE B, BANAVAR M, HOU Daqing, et al. Fast free-text authentication via instance-based keystroke dynamics[J]. IEEE Transactions on Biometrics, Behavior, and Identity Science, 2020, 2(4): 377–387. doi: 10.1109/TBIOM.2020.3003988
[7]	BERGADANO F, GUNETTI D, and PICARDI C. User authentication through keystroke dynamics[J]. ACM Transactions on Information and System Security, 2002, 5(4): 367–397. doi: 10.1145/581271.581272
[8]	GUNETTI D and PICARDI C. Keystroke analysis of free text[J]. ACM Transactions on Information and System Security, 2005, 8(3): 312–347. doi: 10.1145/1085126.1085129
[9]	KANG P and CHO S. Keystroke dynamics-based user authentication using long and free text strings from various input devices[J]. Information Sciences, 2015, 308: 72–93. doi: 10.1016/j.ins.2014.08.070
[10]	SINGH S and ARYA K V. Key classification: A new approach in free text keystroke authentication system[C]. 2011 Third Pacific-Asia Conference on Circuits, Communications and System (PACCS), Wuhan, China, 2011: 1–5.
[11]	TAPPERT C C, VILLANI M, and CHA S H. Keystroke biometric identification and authentication on long-text input[M]. WANG Ling and GENG Xin. Behavioral Biometrics for Human Identification: Intelligent Applications. Hershey: Medical Information Science Reference, 2010: 342–367.
[12]	MONACO J V and TAPPERT C C. The partially observable hidden Markov model and its application to keystroke dynamics[J]. Pattern Recognit, 2018, 76: 449–462. doi: 10.1016/j.patcog.2017.11.021
[13]	芦效峰, 张胜飞, 伊胜伟. 基于CNN和RNN的自由文本击键模式持续身份认证[J]. 清华大学学报:自然科学版, 2018, 58(12): 1072–1078. doi: 10.16511/j.cnki.qhdxxb.2018.26.048 LU Xiaofeng, ZHANG Shengfei, and YI Shengwei. Free-text keystroke continuous authentication using CNN and RNN[J]. Journal of Tsinghua University:Science and Technology, 2018, 58(12): 1072–1078. doi: 10.16511/j.cnki.qhdxxb.2018.26.048
[14]	DHAKAL V, FEIT A M, KRISTENSSON P O, et al. Observations on typing from 136 million keystrokes[C]. The 2018 CHI Conference on Human Factors in Computing Systems, Montreal, Canada, 2018.
[15]	MORALES A, FIERREZ J, ACIEN A, et al. SetMargin loss applied to deep keystroke biometrics with circle packing interpretation[J]. Pattern Recognition, 2021, 122: 108283. doi: 10.1016/j.patcog.2021.108283
[16]	AYOTTE B, BANAVAR M K, HOU Daqing, et al. Group leakage overestimates performance: A case study in keystroke dynamics[C]. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Nashville, USA, 2021.
[17]	美国国防部国防创新试验小组和美国陆军网络事业技术司令部定制开发BioTracker[EB/OL]. https://www.biometricupdate.com/201708/u-s-army-to-deploy-plurilock-behavioral-iometric-id-authentication-solution, 2017.
[18]	PATEL Y, OUAZZANE K, VASSILEV V T, et al. Keystroke dynamics using auto encoders[C]. 2019 International Conference on Cyber Security and Protection of Digital Services (Cyber Security), Oxford, UK, 2019.
[19]	LI Zengpeng, WANG Ding, and MORAIS E. Quantum-safe round-optimal password authentication for mobile devices[J]. IEEE Transactions on Dependable and Secure Computing, 2022, 19(3): 1885–1899. doi: 10.1109/TDSC.2020.3040776
[20]	汪定, 王平, 雷鸣. 基于RSA的网关口令认证密钥交换协议的分析与改进[J]. 电子学报, 2015, 43(1): 176–184. doi: 10.3969/j.issn.0372-2112.2015.01.028 WANG Ding, WANG Ping, and LEI Ming. Cryptanalysis and improvement of gateway-oriented password authenticated key exchange protocol based on RSA[J]. Acta Electronica Sinica, 2015, 43(1): 176–184. doi: 10.3969/j.issn.0372-2112.2015.01.028