Abstract
Continuous authentication is significant with respect to many online applications where it is desirable to monitor a user’s identity throughout an entire session; not just at the beginning of the session. One example application domain, where this is a requirement, is in relation to Massive Open Online Courses (MOOCs) when users wish to obtain some kind of certification as evidence that they have successfully competed a course. Such continuous authentication can best be realised using some forms of biometric checking; traditional user credential checking methods, for example username and password checking, only provide for “entry” authentication. In this paper, we introduce a novel method for the continuous authentication of computer users founded on keystroke dynamics (keyboard behaviour patterns); a form of behavioural biometric. The proposed method conceptualises keyboard dynamics in terms of a Multivariate-Keystroke Time Series which in turn can be transformed into the spectral domain. The time series can then be monitored dynamically for typing patterns that are indicative of a claimed user. Two transforms are considered, the Discrete Fourier Transform and the Discrete Wavelet Transform. The proposed method is fully described and evaluated, in the context of impersonation detection, using real keystroke datasets. The reported results indicate that the proposed time series mechanism produced an excellent performance, outperforming the comparator approaches by a significant margin.
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References
Ahmed, A.A.E., Traore, I.: A new biometric technology based on mouse dynamics. IEEE Trans. Dependable Secure Comput. 4(3), 165–179 (2007)
Alshehri, A., Coenen, F., Bollegala, D.: Accurate continuous and non-intrusive user authentication with multivariate keystroke streaming. In: Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, pp. 61–70. SciTePress, INSTICC (2017). https://doi.org/10.5220/0006497200610070
Alshehri, A., Coenen, F., Bollegala, D.: Spectral analysis of keystroke streams: towards effective real-time continuous user authentication. In: Proceedings of the 4th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP, pp. 62–73. SciTePress, INSTICC (2018). https://doi.org/10.5220/0006606100620073
Asha, S., Chellappan, C.: Authentication of e-learners using multimodal biometric technology. In: International Symposium on Biometrics and Security Technologies, 2008, ISBAST 2008, pp. 1–6. IEEE (2008)
Bergadano, F., Gunetti, D., Picardi, C.: User authentication through keystroke dynamics. ACM Trans. Inf. Syst. Secur. (TISSEC) 5(4), 367–397 (2002)
Bours, P.: Continuous keystroke dynamics: a different perspective towards biometric evaluation. Inf. Secur. Tech. Rep. 17(1), 36–43 (2012)
Breslow, L., Pritchard, D.E., DeBoer, J., Stump, G.S., Ho, A.D., Seaton, D.T.: Studying learning in the worldwide classroom: Research into edx’s first MOOC. Res. Pract. Assess. 8, 13–25 (2013)
Burrus, C.S., Gopinath, R.A., Guo, H.: Introduction to Wavelets and Wavelet Transforms: A Primer. Prentice-Hall Inc., Englewood Cliffs (1997)
Chan, K.P., Fu, A.W.C.: Efficient time series matching by wavelets. In: Proceedings of 15th International Conference on Data Engineering, 1999, pp. 126–133. IEEE (1999)
Cooley, J.W., Tukey, J.W.: An algorithm for the machine calculation of complex fourier series. Math. Comput. 19(90), 297–301 (1965)
Dowland, P.S., Furnell, S.M.: A long-term trial of keystroke profiling using digraph, trigraph and keyword latencies. In: Deswarte, Y., Cuppens, F., Jajodia, S., Wang, L. (eds.) SEC 2004. ITIFIP, vol. 147, pp. 275–289. Springer, Boston, MA (2004). https://doi.org/10.1007/1-4020-8143-X_18
Edwards, T.: Discrete wavelet transforms: theory and implementation. Universidad de (1991)
Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time-series databases. vol. 23. ACM (1994)
Furnell, S., Karweni, T.: Security issues in online distance learning. Vine 31(2), 28–35 (2001)
Gabor, D.: Theory of communication. part 1: the analysis of information. J. Inst. Electr. Eng.-Part III: Radio Commun. Eng. 93(26), 429–441 (1946)
Gaines, R.S., Lisowski, W., Press, S.J., Shapiro, N.: Authentication by keystroke timing: some preliminary results. Technical report, DTIC Document (1980)
Gunetti, D., Picardi, C.: Keystroke analysis of free text. ACM Trans. Inf. Syst. Secur. (TISSEC) 8(3), 312–347 (2005)
Haar, A.: Zur theorie der orthogonalen funktionensysteme. Math. Ann. 69(3), 331–371 (1910)
Itakura, F.: Minimum prediction residual principle applied to speech recognition. IEEE Trans. Acoust. Speech Signal Process. 23, 52–72 (1975)
Jain, A., Hong, L., Pankanti, S.: Biometric identification. Commun. ACM 43(2), 90–98 (2000)
Janacek, G.J., Bagnall, A.J., Powell, M.: A likelihood ratio distance measure for the similarity between the fourier transform of time series. In: Ho, T.B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 737–743. Springer, Heidelberg (2005). https://doi.org/10.1007/11430919_85
Janakiraman, R., Sim, T.: Keystroke dynamics in a general setting. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 584–593. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74549-5_62
Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Dimensionality reduction for fast similarity search in large time series databases. Knowl. Inf. Syst. 3(3), 263–286 (2001)
Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Locally adaptive dimensionality reduction for indexing large time series databases. ACM Sigmod Rec. 30(2), 151–162 (2001)
Maas, A., Heather, C., Do, C.T., Brandman, R., Koller, D., Ng, A.: Offering verified credentials in massive open online courses: MOOCs and technology to advance learning and learning research (ubiquity symposium). Ubiquity 2014(May), 2 (2014)
Maltoni, D., Maio, D., Jain, A., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer, Heidelberg (2009)
Mantyjarvi, J., Lindholm, M., Vildjiounaite, E., Makela, S.M., Ailisto, H.: Identifying users of portable devices from gait pattern with accelerometers. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005, (ICASSP’05), vol. 2, pp. ii–973. IEEE (2005)
Moini, A., Madni, A.M.: Leveraging biometrics for user authentication in online learning: a systems perspective. IEEE Syst. J. 3(4), 469–476 (2009)
Monrose, F., Rubin, A.: Authentication via keystroke dynamics. In: Proceedings of the 4th ACM Conference on Computer and Communications Security, pp. 48–56. ACM (1997)
Monrose, F., Rubin, A.D.: Keystroke dynamics as a biometric for authentication. Future Gener. Comput. Syst. 16(4), 351–359 (2000)
Niennattrakul, V., Ratanamahatana, C.A.: Shape averaging under time warping. In: 6th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2009, ECTI-CON 2009, vol. 2, pp. 626–629. IEEE (2009)
Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The feret evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1090–1104 (2000)
Revett, K.: A bioinformatics based approach to user authentication via keystroke dynamics. Int. J. Control Autom. Syst. 7(1), 7–15 (2009)
Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization fro spoken word recognition. IEEE Trans. Acoust. Speech Signal Process. 26, 43–49 (1978)
Staszewski, W.J., Worden, K., Tomlinson, G.R.: Time-frequency analysis in gearbox fault detection using the wigner-ville distribution and pattern recognition. Mech. Syst. Signal Process. 11(5), 673–692 (1997)
Traore, I.: Continuous Authentication Using Biometrics: Data, Models, and Metrics: Data, Models, and Metrics. IGI Global, Hershey (2011)
Unar, J., Seng, W.C., Abbasi, A.: A review of biometric technology along with trends and prospects. Pattern Recogn. 47(8), 2673–2688 (2014)
Vielhauer, C., Steinmetz, R.: Handwriting: feature correlation analysis for biometric hashes. EURASIP J. Appl. Signal Process. 2004, 542–558 (2004)
Wang, X., Mueen, A., Ding, H., Trajcevski, G., Scheuermann, P., Keogh, E.: Experimental comparison of representation methods and distance measures for time series data. Data Min. Knowl. Disc. 26(2), 275–309 (2013)
Wildes, R.P.: Iris recognition: an emerging biometric technology. Proc. IEEE 85(9), 1348–1363 (1997)
Wu, P.Y., Fang, C.C., Chang, J.M., Gilbert, S.B., Kung, S.: Cost-effective kernel ridge regression implementation for keystroke-based active authentication system. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6028–6032. IEEE (2014)
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Alshehri, A., Coenen, F., Bollegala, D. (2019). Behavioural Biometric Continuous User Authentication Using Multivariate Keystroke Streams in the Spectral Domain. In: Fred, A., et al. Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2017. Communications in Computer and Information Science, vol 976. Springer, Cham. https://doi.org/10.1007/978-3-030-15640-4_3
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