KEYSTROKE DYNAMICS ANALYSIS USING MACHINE LEARNING METHODS
Nataliya SHABLIY
natalinash@gmail.comTernopil Ivan Puluj National Technical University, Faculty of Computer Information Systems and Software Engineering, Computer Systems and Networks Department, Ternopil (Ukraine)
Serhii LUPENKO
Ternopil Ivan Puluj National Technical University, Faculty of Computer Information Systems and Software Engineering, Computer Systems and Networks Department, Ternopil (Ukraine)
Nadiia LUTSYK
Ternopil Ivan Puluj National Technical University, Faculty of Computer Information Systems and Software Engineering, Computer Systems and Networks Department, Ternopil (Ukraine)
Oleh YASNIY
Ternopil Ivan Puluj National Technical University, Faculty of Computer Information Systems and Software Engineering, Computer Systems and Networks Department, Ternopil (Ukraine)
Olha MALYSHEVSKA
Ivano-Frankivsk National Medical University, Department of Hygiene and Ecology, Ivano-Frankivsk (Ukraine)
Abstract
The primary objective of the paper was to determine the user based on its keystroke dynamics using the methods of machine learning. Such kind of a problem can be formulated as a classification task. To solve this task, four methods of supervised machine learning were employed, namely, logistic regression, support vector machines, random forest, and neural network. Each of three users typed the same word that had 7 symbols 600 times. The row of the dataset consists of 7 values that are the time period during which the particular key was pressed. The ground truth values are the user id. Before the application of machine learning classification methods, the features were transformed to z-score. The classification metrics were obtained for each applied method. The following parameters were determined: precision, recall, f1-score, support, prediction, and area under the receiver operating characteristic curve (AUC). The obtained AUC score was quite high. The lowest AUC score equal to 0.928 was achieved in the case of linear regression classifier. The highest AUC score was in the case of neural network classifier. The method of support vector machines and random forest showed slightly lower results as compared with neural network method. The same pattern is true for precision, recall and F1-score. Nevertheless, the obtained classification metrics are quite high in every case. Therefore, the methods of machine learning can be efficiently used to classify the user based on keystroke patterns. The most recommended method to solve such kind of a problem is neural network.
Keywords:
keystroke dynamics analysis, machine learning, neural network, supervised learning, classification problemReferences
Al-Awad, N. A., Abboud, I. K., & Al-Rawi, M. F. (2021). Genetic Algorithm-PID controller for model order reduction pantographcatenary system. Applied Computer Science, 17(2), 28-39. https://doi.org/10.23743/acs-2021-11
Google Scholar
Alyamani, A., & Yasniy, O. (2020). Classification of EEG signal by methods of machine learning. Applied Computer Science, 16(4), 56-63. https://doi.org/10.23743/acs-2020-29
Google Scholar
Biau, G., & Scornet, E. (2016). A Random Forest Guided Tour. Test, 25(2), 197–227. https://doi.org/10.1007/s11749-016-0481-7
DOI: https://doi.org/10.1007/s11749-016-0481-7
Google Scholar
Bradley, A. P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7), 1145–1159. https://doi.org/10.1016/S0031-3203(96)00142-2
DOI: https://doi.org/10.1016/S0031-3203(96)00142-2
Google Scholar
Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 1–58. https://doi.org/10.1145/1541880.1541882
DOI: https://doi.org/10.1145/1541880.1541882
Google Scholar
Dewi, W., & Utomo, W. H. (2021). Plant classification based on leaf edges and leaf morphological veins using wavelet convolutional neural network. Applied Computer Science, 17(1), 81–89. https://doi.org/10.23743/acs-2021-08
Google Scholar
Dhir, Vijay, Singh, A., Kumar, R., & Singh, G. (2010). Biometric Recognition: A Modern Era For Security. International Journal of Engineering Science and Technology, 2(8), 3364–80.
Google Scholar
Edgar, T. W., & Manz, D. O. (2017). Research Methods for Cyber Security. Syngress.
Google Scholar
Fischer, R. J., Halibozek, E. P., & Walters, D. C. (2019). Holistic Security Through the Application of Integrated Technology. Introduction to Security, 2019, 433–62. https://doi.org/10.1016/b978-0-12-805310-2.00017-2.
DOI: https://doi.org/10.1016/B978-0-12-805310-2.00017-2
Google Scholar
Gaines, R. S., Lisowski. W., Press, S. J., & Shapiro, N. (1980). Authentication by Keystroke Timing. The Rand Corporation.
Google Scholar
Gebrie, M. T., & Abie, H. (2017). Risk-Based Adaptive Authentication for Internet of Things in Smart Home EHealth. Proceedings of the 11th European Conference on Software Architecture: Companion Proceedings (ECSA'17) (pp. 102–108). Association for Computing Machinery. https://doi.org/10.1145/3129790.3129801
DOI: https://doi.org/10.1145/3129790.3129801
Google Scholar
Hwang, S.-S., Lee H., & Cho, S. (2009). Improving Authentication Accuracy Using Artificial Rhythms and Cues for Keystroke Dynamics-Based Authentication. Expert Systems with Applications, 36(7), 10649–56. https://doi.org/10.1016/j.eswa.2009.02.075
DOI: https://doi.org/10.1016/j.eswa.2009.02.075
Google Scholar
Jain, A. K., Bolle, R. M., & Pankanti, S. (2006). Biometrics. Personal Identification in Networked Society. Springer.
Google Scholar
Jain, A. K., Ross, A., & Prabhakar, S. (2004). An Introduction to Biometric Recognition. IEEE Trans. on Circuits and Systems for Video Technology, 14(1), 4-19.
DOI: https://doi.org/10.1109/TCSVT.2003.818349
Google Scholar
Javaheri, S. H., Sepehri, M. M. & Teimourpour, B. (2013). Response Modeling in Direct Marketing. A Data Mining-Based Approach for Target Selection. Data Mining Applications with R (pp. 153-180). Elsevier Inc. https://doi.org/10.1016/B978-0-12-411511-8.00006-2
DOI: https://doi.org/10.1016/B978-0-12-411511-8.00006-2
Google Scholar
Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1), 59–69.
DOI: https://doi.org/10.1007/BF00337288
Google Scholar
Markou, M., & Singh, S. (2003). Novelty detection: a review—part 1: statistical approaches. Signal Processing, 83(12), 2481–2497. https://doi.org/10.1016/j.sigpro.2003.07.018
DOI: https://doi.org/10.1016/j.sigpro.2003.07.018
Google Scholar
Miljković, D. (2010). Review of novelty detection methods. The 33rd International Convention MIPRO (pp. 593-598). IEEE.
Google Scholar
Monrose, F., Reiter, M. K., & Wetzel, S. (2002). Password Hardening Based on Keystroke Dynamics. International Journal of Information Security, 1(2), 69–83. https://doi.org/10.1007/s102070100006
DOI: https://doi.org/10.1007/s102070100006
Google Scholar
Raschka, S. (2017). Python Machine Learning. Second edition. Packt Publishing Ltd.
Google Scholar
Ru, W.G., & Eloff, J.H. (1997). Enhanced Password Authentication through Fuzzy Logic. IEEE Expert, 12, 38-45.
DOI: https://doi.org/10.1109/64.642960
Google Scholar
Sridharan, M., Rani Arulanandam, D. C., Chinnasamy, R. K., Thimmanna, S., & Dhandapani, S. (2021). Recognition of font and tamil letter in images using deep learning. Applied Computer Science, 17(2), 90–99. https://doi.org/10.23743/acs-2021-15
Google Scholar
Subasi, A. (2020). Practical Machine Learning for Data Analysis Using Python. Academic Press.
Google Scholar
Umphress, D., & Williams, G. (1985). Identity verification through keyboard characteristics. International Journal of Man-Machine Studies, 23(3), 263–273. https://doi.org/10.1016/S0020-7373(85)80036-5
DOI: https://doi.org/10.1016/S0020-7373(85)80036-5
Google Scholar
Vaibhaw, Sarraf, J., & Pattnaik, P.K. (2020). Brain–Computer Interfaces and Their Applications. An Industrial IoT Approach for Pharmaceutical Industry Growth, 2, 31-54. https://doi.org/10.1016/b978-0-12-821326-1.00002-4
DOI: https://doi.org/10.1016/B978-0-12-821326-1.00002-4
Google Scholar
Williams, B., Halloin, C., Löbel, W., Finklea, F., Lipke, E., Zweigerdt, R., & Cremaschi, S. (2020). Data-Driven Model Development for Cardiomyocyte Production Experimental Failure Prediction. Computer Aided Chemical Engineering, 48, 1639-1644. https://doi.org/10.1016/B978-0-12-823377-1.50274-3
DOI: https://doi.org/10.1016/B978-0-12-823377-1.50274-3
Google Scholar
Authors
Nataliya SHABLIYnatalinash@gmail.com
Ternopil Ivan Puluj National Technical University, Faculty of Computer Information Systems and Software Engineering, Computer Systems and Networks Department, Ternopil Ukraine
Authors
Serhii LUPENKOTernopil Ivan Puluj National Technical University, Faculty of Computer Information Systems and Software Engineering, Computer Systems and Networks Department, Ternopil Ukraine
Authors
Nadiia LUTSYKTernopil Ivan Puluj National Technical University, Faculty of Computer Information Systems and Software Engineering, Computer Systems and Networks Department, Ternopil Ukraine
Authors
Oleh YASNIYTernopil Ivan Puluj National Technical University, Faculty of Computer Information Systems and Software Engineering, Computer Systems and Networks Department, Ternopil Ukraine
Authors
Olha MALYSHEVSKAIvano-Frankivsk National Medical University, Department of Hygiene and Ecology, Ivano-Frankivsk Ukraine
Statistics
Abstract views: 636PDF downloads: 108
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Most read articles by the same author(s)
- Amina ALYAMANI, Oleh YASNIY, CLASSIFICATION OF EEG SIGNAL BY METHODS OF MACHINE LEARNING , Applied Computer Science: Vol. 16 No. 4 (2020)
Similar Articles
- Saleh ALBAHLI, A DEEP ENSEMBLE LEARNING METHOD FOR EFFORT-AWARE JUST-IN-TIME DEFECT PREDICTION , Applied Computer Science: Vol. 16 No. 3 (2020)
- Mouna TARIK, Ayoub MNIAI, Khalid JEBARI, HYBRID FEATURE SELECTION AND SUPPORT VECTOR MACHINE FRAMEWORK FOR PREDICTING MAINTENANCE FAILURES , Applied Computer Science: Vol. 19 No. 2 (2023)
- Tomasz SEDERYN, Małgorzata SKAWIŃSKA, COMPUTATIONAL ANALYSIS OF PEM FUEL CELL UNDER DIFFERENT OPERATING CONDITIONS , Applied Computer Science: Vol. 19 No. 4 (2023)
- Eduardo Sánchez-García, Javier Martínez-Falcó, Bartolomé Marco-Lajara, Jolanta Słoniec, ANALYZING THE ROLE OF COMPUTER SCIENCE IN SHAPING MODERN ECONOMIC AND MANAGEMENT PRACTICES. BIBLIOMETRIC ANALYSIS , Applied Computer Science: Vol. 20 No. 1 (2024)
- Kevin Joy DSOUZA, Zahid Ahmed ANSARI, HISTOPATHOLOGY IMAGE CLASSIFICATION USING HYBRID PARALLEL STRUCTURED DEEP-CNN MODELS , Applied Computer Science: Vol. 18 No. 1 (2022)
- Edyta ŁUKASIK, Wiktor FLIS, EFFICIENCY COMPARISON OF NETWORKS IN HANDWRITTEN LATIN CHARACTERS RECOGNITION WITH DIACRITICS , Applied Computer Science: Vol. 19 No. 4 (2023)
- Zahid Zamir, CAN THE SYSTEM, INFORMATION, AND SERVICE QUALITIES IMPACT EMPLOYEE LEARNING, ADAPTABILITY, AND JOB SATISFACTION? , Applied Computer Science: Vol. 19 No. 1 (2023)
- Robert KARPIŃSKI, Anna MACHROWSKA, Marcin MACIEJEWSKI, APPLICATION OF ACOUSTIC SIGNAL PROCESSING METHODS IN DETECTING DIFFERENCES BETWEEN OPEN AND CLOSED KINEMATIC CHAIN MOVEMENT FOR THE KNEE JOINT , Applied Computer Science: Vol. 15 No. 1 (2019)
- Łukasz SEMKŁO, Łukasz GIERZ, NUMERICAL AND EXPERIMENTAL ANALYSIS OF A CENTRIFUGAL PUMP WITH DIFFERENT ROTOR GEOMETRIES , Applied Computer Science: Vol. 18 No. 4 (2022)
- Sahar ZAMANI KHANGHAH, Keivan MAGHOOLI, EMOTION RECOGNITION FROM HEART RATE VARIABILITY WITH A HYBRID SYSTEM COMBINED HIDDEN MARKOV MODEL AND POINCARE PLOT , Applied Computer Science: Vol. 20 No. 1 (2024)
<< < 2 3 4 5 6 7 8 9 10 11 > >>
You may also start an advanced similarity search for this article.
