Dynamic Keystroke Technique for a Secure Authentication System based on Deep Belief Nets
Received: 10 March 2023 | Revised: 9 April 2023 | Accepted: 14 April 2023 | Online: 2 June 2023
Corresponding author: Asia Othman Aljahdali
Abstract
The rapid growth of electronic assessment in various fields has led to the emergence of issues such as user identity fraud and cheating. One potential solution to these problems is to use a complementary authentication method, such as a behavioral biometric characteristic that is unique to each individual. One promising approach is keystroke dynamics, which involves analyzing the typing patterns of users. In this research, the Deep Belief Nets (DBN) model is used to implement a dynamic keystroke technique for secure e-assessment. The proposed system extracts various features from the pressure-time measurements, digraphs (dwell time and flight time), trigraphs, and n-graphs, and uses these features to classify the user's identity by applying the DBN algorithm to a dataset collected from participants who typed free text using a standard QWERTY keyboard in a neutral state without inducing specific emotions. The DBN model is designed to detect cheating attempts and is tested on a dataset collected from the proposed e-assessment system using free text. The implementation of the DBN results in an error rate of 5% and an accuracy of 95%, indicating that the system is effective in identifying users' identities and cheating, providing a secure e-assessment approach.
Keywords:
keystroke dynamics, authentication, e-assessment, dwell time, flight time, Deep Belief Network (DBN)Downloads
References
M. L. Ali, J. V. Monaco, C. C. Tappert, and M. Qiu, "Keystroke Biometric Systems for User Authentication," Journal of Signal Processing Systems, vol. 86, no. 2, pp. 175–190, Mar. 2017. DOI: https://doi.org/10.1007/s11265-016-1114-9
R. S. Gaines, W. Lisowski, S. J. Press, and N. Shapiro, "Authentication by Keystroke Timing: Some Preliminary Results," Rand, Santa Monica, CA, USA, R-256-NSF, 1980.
T. Sim and R. Janakiraman, "Are Digraphs Good for Free-Text Keystroke Dynamics?," in IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA, Jun. 2007, pp. 1–6. DOI: https://doi.org/10.1109/CVPR.2007.383393
Y. Deng and Y. Zhong, "Keystroke Dynamics User Authentication Based on Gaussian Mixture Model and Deep Belief Nets," International Scholarly Research Notices, vol. 2013, Oct. 2013, Art. no. e565183.
D. Hosseinzadeh and S. Krishnan, "Gaussian Mixture Modeling of Keystroke Patterns for Biometric Applications," IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 38, no. 6, pp. 816–826, Aug. 2008. DOI: https://doi.org/10.1109/TSMCC.2008.2001696
T. Eude and C. Chang, "One-class SVM for biometric authentication by keystroke dynamics for remote evaluation," Computational Intelligence, vol. 34, no. 1, pp. 145–160, 2018. DOI: https://doi.org/10.1111/coin.12122
K. S. Killourhy and R. A. Maxion, "Comparing anomaly-detection algorithms for keystroke dynamics," in IEEE/IFIP International Conference on Dependable Systems & Networks, Lisbon, Portugal, Jun. 2009, pp. 125–134. DOI: https://doi.org/10.1109/DSN.2009.5270346
S. Haider, A. Abbas, and A. K. Zaidi, "A multi-technique approach for user identification through keystroke dynamics," in ieee international conference on systems, man and cybernetics. "cybernetics evolving to systems, humans, organizations, and their complex interactions" (cat. no.0, Nashville, TN, USA, Oct. 2000, vol. 2, pp. 1336–1341.
R. Mattsson, "Keystroke dynamics for student authentication in online examinations," M.S. thesis, Lulea University of Technology, Lulea, Sweden, 2020.
R. Salakhutdinov, "Learning Deep Generative Models," Annual Review of Statistics and Its Application, vol. 2, no. 1, pp. 361–385, 2015. DOI: https://doi.org/10.1146/annurev-statistics-010814-020120
D. Peleg, Mastering Sublime Text. Birmingham, UK: Packt Publishing, 2013.
A. Tewari, "Keystroke Dynamics based Recognition Systems using Deep Learning: A Survey." TechRxiv, Apr. 11, 2022. DOI: https://doi.org/10.36227/techrxiv.19532269
Y. B. W. Piugie, J. Di Manno, C. Rosenberger, and C. Charrier, "Keystroke Dynamics based User Authentication using Deep Learning Neural Networks," in 2022 International Conference on Cyberworlds (CW), Kanazawa, Japan, Sep. 2022, pp. 220–227.
K. Shekhawat and D. P. Bhatt, "A novel approach for user authentication using keystroke dynamics," Journal of Discrete Mathematical Sciences and Cryptography, vol. 25, no. 7, pp. 2015–2027, Oct. 2022. DOI: https://doi.org/10.1080/09720529.2022.2133241
Y. Deng and Y. Zhong, "Keystroke Dynamics User Authentication Based on Gaussian Mixture Model and Deep Belief Nets," International Scholarly Research Notices, vol. 2013, Oct. 2013, Art. no. e565183. DOI: https://doi.org/10.1155/2013/565183
Y. B. W. Piugie, J. Di Manno, C. Rosenberger, and C. Charrier, "Keystroke Dynamics based User Authentication using Deep Learning Neural Networks," in International Conference on Cyberworlds, Kanazawa, Japan, Sep. 2022, pp. 220–227. DOI: https://doi.org/10.1109/CW55638.2022.00052
S. L. Albuquerque, C. J. Miosso, A. F. da Rocha, and P. L. R. Gondim, "Multi-Factor Authentication Protocol Based on Electrocardiography Signals for a Mobile Cloud Computing Environment," in Mobile Computing Solutions for Healthcare Systems, R. Sivakumar, D. Velev, B. Alhadidi, S. Vidhya, S. V. Francis, and B. Prabadevi, Eds. Bentham Books, 2023, pp. 62–88. DOI: https://doi.org/10.2174/9789815050592123010009
A. Alsultan, K. Warwick, and H. Wei, "Improving the performance of free-text keystroke dynamics authentication by fusion," Applied Soft Computing, vol. 70, pp. 1024–1033, Sep. 2018. DOI: https://doi.org/10.1016/j.asoc.2017.11.018
A. Andrean, M. Jayabalan, and V. Thiruchelvam, "Keystroke Dynamics Based User Authentication using Deep Multilayer Perceptron," International Journal of Machine Learning and Computing, vol. 10, no. 1, pp. 134–139, Jan. 2020. DOI: https://doi.org/10.18178/ijmlc.2020.10.1.910
A. Rahman et al., "Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms," IEEE Access, vol. 9, pp. 94625–94643, 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3092840
J. Kim, H. Kim, and P. Kang, "Keystroke dynamics-based user authentication using freely typed text based on user-adaptive feature extraction and novelty detection," Applied Soft Computing, vol. 62, pp. 1077–1087, Jan. 2018. DOI: https://doi.org/10.1016/j.asoc.2017.09.045
I. Traore, I. Woungang, M. S. Obaidat, Y. Nakkabi, and I. Lai, "Combining Mouse and Keystroke Dynamics Biometrics for Risk-Based Authentication in Web Environments," in 2012 Fourth International Conference on Digital Home, Guangzhou, China, Aug. 2012, pp. 138–145. DOI: https://doi.org/10.1109/ICDH.2012.59
M. L. Ali, K. Thakur, and M. A. Obaidat, "A Hybrid Method for Keystroke Biometric User Identification," Electronics, vol. 11, no. 17, Jan. 2022, Art. no. 2782. DOI: https://doi.org/10.3390/electronics11172782
G. Zhao, Z. Wu, Y. Gao, G. Niu, Z. L. Wang, and B. Zhang, "Multi-Layer Extreme Learning Machine-Based Keystroke Dynamics Identification for Intelligent Keyboard," IEEE Sensors Journal, vol. 21, no. 2, pp. 2324–2333, Jan. 2021. DOI: https://doi.org/10.1109/JSEN.2020.3019777
A. Acien, A. Morales, R. Vera-Rodriguez, J. Fierrez, and J. V. Monaco, "TypeNet: Scaling up Keystroke Biometrics," in IEEE International Joint Conference on Biometrics, Houston, TX, USA, Sep. 2020, pp. 1–7. DOI: https://doi.org/10.1109/IJCB48548.2020.9304908
A. Acien, A. Morales, J. V. Monaco, R. Vera-Rodriguez, and J. Fierrez, "TypeNet: Deep Learning Keystroke Biometrics," IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 4, no. 1, pp. 57–70, Jan. 2022. DOI: https://doi.org/10.1109/TBIOM.2021.3112540
L. Yang, C. Li, R. You, B. Tu, and L. Li, "TKCA: a timely keystroke-based continuous user authentication with short keystroke sequence in uncontrolled settings," Cybersecurity, vol. 4, no. 1, May 2021, Art. no. 13. DOI: https://doi.org/10.1186/s42400-021-00075-9
L. Sun et al., "Kollector: Detecting Fraudulent Activities on Mobile Devices Using Deep Learning," IEEE Transactions on Mobile Computing, vol. 20, no. 4, pp. 1465–1476, Apr. 2021. DOI: https://doi.org/10.1109/TMC.2020.2964226
J. Li, H.-C. Chang, and M. Stamp, "Free-Text Keystroke Dynamics for User Authentication," Jul. 2021. DOI: https://doi.org/10.1007/978-3-030-97087-1_15
K.-W. Tse and K. Hung, "User Behavioral Biometrics Identification on Mobile Platform using Multimodal Fusion of Keystroke and Swipe Dynamics and Recurrent Neural Network," in 10th Symposium on Computer Applications & Industrial Electronics, Kuala Lumpur, Malaysia, Apr. 2020, pp. 262–267. DOI: https://doi.org/10.1109/ISCAIE47305.2020.9108839
X. Lu, S. Zhang, P. Hui, and P. Lio, "Continuous authentication by free-text keystroke based on CNN and RNN," Computers & Security, vol. 96, Sep. 2020, Art. no. 101861. DOI: https://doi.org/10.1016/j.cose.2020.101861
N. Altwaijry, "Keystroke Dynamics Analysis for User Authentication Using a Deep Learning Approach," International Journal of Computer Science and Network Security, vol. 20, no. 12, pp. 209–216, Dec. 2020.
E. Maiorana, H. Kalita, and P. Campisi, "Deepkey: Keystroke Dynamics and CNN for Biometric Recognition on Mobile Devices," in 8th European Workshop on Visual Information Processing, Roma, Italy, Oct. 2019, pp. 181–186. DOI: https://doi.org/10.1109/EUVIP47703.2019.8946206
G. Zhao et al., "Keystroke Dynamics Identification Based on Triboelectric Nanogenerator for Intelligent Keyboard Using Deep Learning Method," Advanced Materials Technologies, vol. 4, no. 1, 2019, Art. no. 1800167. DOI: https://doi.org/10.1002/admt.201800167
M. L. Bernardi, M. Cimitile, F. Martinelli, and F. Mercaldo, "Keystroke Analysis for User Identification using Deep Neural Networks," in International Joint Conference on Neural Networks, Budapest, Hungary, Jul. 2019, pp. 1–8. DOI: https://doi.org/10.1109/IJCNN.2019.8852068
C.-H. Lin, J.-C. Liu, and K.-Y. Lee, "On Neural Networks for Biometric Authentication Based on Keystroke Dynamics," Sensors and Materials, vol. 30, no. 3, pp. 385–396, 2018. DOI: https://doi.org/10.18494/SAM.2018.1757
Y. Muliono, H. Ham, and D. Darmawan, "Keystroke Dynamic Classification using Machine Learning for Password Authorization," Procedia Computer Science, vol. 135, pp. 564–569, Jan. 2018. DOI: https://doi.org/10.1016/j.procs.2018.08.209
H. Ceker and S. Upadhyaya, "Transfer learning in long-text keystroke dynamics," in IEEE International Conference on Identity, Security and Behavior Analysis, New Delhi, India, Feb. 2017, pp. 1–6. DOI: https://doi.org/10.1109/ISBA.2017.7947710
S. Maheshwary, S. Ganguly, and V. Pudi, "Deep Secure: A Fast and Simple Neural Network based approach for User Authentication and Identification via Keystroke Dynamics," in First International Workshop on Artificial Intelligence in Security, Melbourne, VIC, Australia, Aug. 2017, pp. 60–66.
Y. Deng and Y. Zhong, "Keystroke Dynamics Advances for Mobile Devices Using Deep Neural Network," in Recent Advances in User Authentication Using Keystroke Dynamics Biometrics, Thrace, Greece: Science Gate Publishing, 2015, pp. 59–70. DOI: https://doi.org/10.15579/gcsr.vol2.ch4
A. R. Khan and L. K. Alnwihel, "A Brief Review on Cloud Computing Authentication Frameworks," Engineering, Technology & Applied Science Research, vol. 13, no. 1, pp. 9997–10004, Feb. 2023. DOI: https://doi.org/10.48084/etasr.5479
E. S. I. Harba, "Secure Data Encryption Through a Combination of AES, RSA and HMAC," Engineering, Technology & Applied Science Research, vol. 7, no. 4, pp. 1781–1785, Aug. 2017. DOI: https://doi.org/10.48084/etasr.1272
S. Hamid, N. Z. Bawany, and S. Khan, "AcSIS: Authentication System Based on Image Splicing," Engineering, Technology & Applied Science Research, vol. 9, no. 5, pp. 4808–4812, Oct. 2019. DOI: https://doi.org/10.48084/etasr.3060
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Copyright (c) 2023 Asia Othman Aljahdali, Fursan Thabit, Hanan Aldissi, Wafaa Nagro
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