Abstract
Together with the widening of the Internet plays in the continuous outstretch of the online environment as a potential for business has also increased the fortunes of online malicious attacks and intrusions. They all have their way of stealing the user’s identity. Usernames and passwords which are very weak can be easily cracked by the attackers. User’s credentials thus can be discovered, phished, looted, and then hacked in several different ways. Keystroke recognition is a good technology to facilitate better authentication and protect data theft and has a very minimum amount of drawbacks. Other biometrics needs additional hardware costs whereas this is based on the user typing behavior which just requires a keyboard. Thus Keystroke recognition allows authenticating users through their way of typing on a keyboard of a computer. This paper presents a biometric access control measure: access of computers via keystroke recognition and talks about how identity thefts and user data theft can be prevented by using this keystroke dynamics.
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References
Tsai, C.J., Shih, K.J.: Mining a new biometrics to improve the accuracy of keystroke dynamics-based authentication system on free-text. Appl. Soft Comput. 80, 125–137 (2019)
Nonaka, H., Kurihara, M.: Sensing pressure for authentication system using keystroke dynamics. Int. J. Comput. Intel. 1(1), 19–22 (2004)
Mantyjarvi, J., Koivumaki, J., Vuori, P.: Keystroke recognition for virtual keyboard. In: Proceedings of the IEEE International Conference on Multimedia and Expo, vol. 2, pp. 429–432. IEEE (2002)
Campisi, P., Maiorana, E., Bosco, M.L., Neri, A.: User authentication using keystroke dynamics for cellular phones. IET Signal Process. 3(4), 333–341 (2009)
Nauman, M., Ali, T., Rauf, A.: Using trusted computing for privacy preserving keystroke-based authentication in smartphones. Telecommun. Syst. 52(4), 2149–2161 (2013)
Al Solami, E., Boyd, C., Clark, A., Ahmed, I.: User-representative feature selection for keystroke dynamics. In: 2011 5th International Conference on Network and System Security, pp. 229–233. IEEE. (2011)
Wang, Y., Du, G.Y., Sun, F.X.: A model for user authentication based on manner of keystroke and principal component analysis. In: 2006 International Conference on Machine Learning and Cybernetics, pp. 2788–2792. IEEE (2006)
Maxion, R.A., Killourhy, K.S.: Keystroke biometrics with number-pad input. In: 2010 IEEE/IFIP International Conference on Dependable Systems & Networks (DSN), pp. 201–210. IEEE (2010)
Maalej, A., Kallel, I.: Does keystroke dynamics tell us about emotions? A systematic literature review and dataset construction. In: 2020 16th International Conference on Intelligent Environments (IE), pp. 60–67. IEEE (2020)
Bakhtiyari, K., Taghavi, M., Husain, H.: Hybrid affective computing—keyboard, mouse and touch screen: from review to experiment. Neural Comput. Appl. 26(6), 1277–1296 (2015)
Epp, C., Lippold, M., Mandryk, R.L.: Identifying emotional states using keystroke dynamics. In: Proceedings of the Sigchi Conference on Human Factors in Computing Systems, pp. 715–724 (2011)
Shikder, R., Rahaman, S., Afroze, F., Al Islam, A.A.: Keystroke/mouse usage based emotion detection and user identification. In: 2017 International Conference on Networking, Systems and Security (NSysS), pp. 96–104. IEEE (2017)
Kołakowska, A.: Recognizing emotions on the basis of keystroke dynamics. In: 2015 8th International Conference on Human System Interaction (HSI), pp. 291–297. IEEE (2015)
Pentel, A.: Emotions and user interactions with keyboard and mouse. In: 2017 8th International Conference on Information, Intelligence, Systems & Applications (IISA), pp. 1–6. IEEE (2017)
Lee, P.M., Tsui, W.H., Hsiao, T.C.: The influence of emotion on keyboard typing: an experimental study using visual stimuli. Biomed. Eng. Online 13(1), 1–12 (2014)
Trojahn, M., Arndt, F., Weinmann, M., Ortmeier, F.: Emotion recognition through keystroke dynamics on touchscreen keyboards. In: ICEIS, vol. 3, pp. 31–37 (2013)
Vizer, L.M.: Different strokes for different folks: individual stress response as manifested in typed text. In: CHI 2013 Extended Abstracts on Human Factors in Computing Systems, pp. 2773–2778 (2013)
Thudumu, S., Branch, P., Jin, J., Singh, J.J.: A comprehensive survey of anomaly detection techniques for high dimensional big data. J. Big Data 7(1), 1–30 (2020)
Baynath, P., Soyjaudah, K.M.S., Khan, M.H.M.: Machine learning algorithm on keystroke dynamics fused pattern in biometrics. In: 2019 Conference on Next Generation Computing Applications (NextComp), September 2019, pp. 1–6. IEEE (2019)
Krishna, G.J., Jaiswal, H., Teja, P.S.R., Ravi, V.: Keystroke based user identification with XGBoost. In: TENCON 2019–2019 IEEE Region 10 Conference (TENCON), October 2019, pp. 1369–1374. IEEE (2020)
Lamiche, I., Bin, G., Jing, Y., Yu, Z., Hadid, A.: A continuous smartphone authentication method based on gait patterns and keystroke dynamics. J. Amb. Intel. Hum. Comput. 10(11), 4417–4430 (2019)
Giot, R., Rocha, A.: Siamese networks for static keystroke dynamics authentication. In: 2019 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1–6. IEEE (2019)
Obaidat, M.S., Krishna, P.V., Saritha, V., Agarwal, S.: Advances in key stroke dynamics-based security schemes. In: Biometric-Based Physical and Cybersecurity Systems, pp. 165–187. Springer, Cham. (2019)
Javed, A.R., Beg, M.O., Asim, M., Baker, T., Al-Bayatti, A.H.: AlphaLogger: detecting motion-based side-channel attack using smartphone keystrokes. J. Amb. Intel. Hum. Comput., 1–14 (2020)
Elliot, K., Graham, J., Yassin, Y., Ward, T., Caldwell, J., Attie, T.: A comparison of machine learning algorithms in keystroke dynamics. In: 2019 International Conference on Computational Science and Computational Intelligence (CSCI), December 2019, pp. 127–132. IEEE (2020)
Kumar, P., Seth, S., Bajaj, K., Rawat, S.: Diverse security practices and comparison on key stroke dynamics. In: 2019 8th International Conference System Modeling and Advancement in Research Trends (SMART), pp. 305–309. IEEE (2019)
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Agilandeeswari, L., Ragul, V., Syed Mohammed Nihal, S., Rahaman Khan, M. (2021). A Comprehensive Analysis of Keystroke Recognition System. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_99
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