ISSN 2071-8594

Russian academy of sciences

Editor-in-Chief

Gennady Osipov

A. E. Sulavko, S. S. Zhumazhanova, D.G. Stadnikov Identification of computer systems users by electroencephalograms parameters in the process of entering the password phrase on the keyboard

Abstract.

The article discusses the relationship of the keyboard handwriting of a computer user and the parameters of his electroencephalogram (EEG). As part of the work, an experiment was conducted to collect EEG data of 65 subjects who entered the passphrase on different keyboards at different times. An EEG analysis was performed, patterns and EEG parameters (features) were identified that can be used for person biometric identification. A method for identifying a person by the characteristics of the EEG in the process of keyboard input is proposed. A computational experiment was conducted with a large volume of test sample to assess the reliability of recognition of subjects. According to the results of the experiment, 1.62% errors were obtained. At the same time, no dependence of the EEG signs on the keyboard used by the subjects and the time of day, as well as on the variability of the signs with time, was detected.

Keywords:

electroencephalogram parameters, pattern recognition, keystroke dynamics, biometric identification, feature space, machine learning, information security.

PP. 15-27.

DOI 10.14357/20718594190202

References

1. Sulavko A.E., Volkov D.A., Zhumazhanova S.S., Borisov R.V. Subjects Authentication Based on Secret Biometric Patterns Using Wavelet Analysis and Flexible Neural Networks // 2018 XIV International Scientific-Technical Conference on Actual Problems of Electronics Instrument Engineering (APEIE). - Novosibirsk, Russia. IEEE. - October 2, 2018. - P. 218-227. DOI: 10.1109/APEIE.2018.8545676
2. Gong, S., Naresh Boddeti, V., Jain., A.K.: ‘On the Capacity of Face Representation’, arXiv preprint arXiv:1709.10433, 2017.
3. Armstrong, B.C. et al. (2015). Brainprint: Assessing the uniqueness, collectability, and permanence of a novel method for ERP. Neurocomputing, 166, 59–67.
4. Ruiz–Blondet, M.V. et al. (2016). CEREBRE: A Novel Method for Very High Accuracy Event-Related Potential Biometric Identification. IEEE Transactions on Information Forensics and Security, 11(7), 1618–1629.
5. С.М. Гончаров, Боршевников А.Е. Нейросетевой преобразователь «Биометрия – код доступа» на основе электроэнцефалограммы в современных криптографических приложениях // Вестник СибГУТИ. 2016. – № 1. – с. 17-22.
6. Salil P. Banerjee, Damon L. Woodard. Biometric Authentication and Identification using Keystroke Dynamics: ASurvey // Journal of Pattern Recognition Research, 2012. - №7. - P. 116-139.
7. Jun Chen, Guang Zhu, Jin Yang, Qingshen Jing, Peng Bai, Weiqing Yang, Xuewei Qi, Yuanjie Su, Zhong Lin Wang. Personalized Keystroke Dynamics for Self-Powered Human Machine Interfacing // ACS Nano, 2015. - 9 (1). - P. 105–116.
8. Vasilyev V.I., Sulavko A.E., Zhumazhanova S.S., Borisov R.V. Identification of the Psychophysiological State of the User Based on Hidden Monitoring in Computer Systems // Scientific and Technical Information Processing. - December 2018, Volume 45, Issue 6, pp 398–410.
9. Allen G.I., Tsukahara N. Cerebrocerebellar communication systems // Physiol. Rev. 1974. Vol. 54, № 4. P. 957–1006.
10. Gordon A.M. et al. Functional magnetic resonance imaging of motor, sensory, and posterior parietal cortical areas during performance of sequential typing movements // Exp Brain Res. 1998. Vol. 121, № 2. P. 153–166.
11. Santello M. Getting a Grasp of Theories of Sensorimotor Control of the Hand: Identification of Underlying Neural Mechanisms // Motor Control. 2015. Vol. 19, № 2. P. 149–153.
12. Higashiyama Y. et al. The Neural Basis of Typewriting: A Functional MRI Study // PLoS One. 2015. Vol. 10, № 7.
13. Mousmita Sarma, Kandarpa Kumar Sarma. Segmentation and Classification of Vowel Phonemes of Assamese Speech Using a Hybrid Neural Framework // Applied Computational Intelligence and Soft Computing. – 2012, Article ID 871324, 8 p. doi:10.1155/2012/871324.
14. Albada van S.J., Robinson P.A. Relationships between Electroencephalographic Spectral Peaks Across Frequency Bands. Front Hum Neurosci. 2013 4;7:56
15. Ivanov A.I., Lozhnikov P.S., Sulavko A.E. Evaluation of signature verification reliability based on artificial neural networks, Bayesian multivariate functional and quadratic forms // Computer Optics. - 2017. - №5. - p. 765-774.
16. DamaševiIius, R. et al. (2018). Combining Cryptography with EEG Biometrics. Computational Intelligence and Neuroscience, 2018, 11.
17. Akhmetov, B.S., Ivanov, A.I., Alimseitova, Z.K. Training of neural network biometry-code converters // 2018 News of the National Academy of Sciences of the Republic of Kazakhstan, Series of Geology and Technical Sciences. – p. 61-68.