Algoritmos de Aprendizado de Máquina Aplicados ao Reconhecimento de Usuário Baseado na Dinâmica da Digitação: Um Estudo Comparativo

• Marco Aurélio da Silva Cruz IME
• Ronaldo Ribeiro Goldschmidt IME

DOI: https://doi.org/10.5753/sbseg.2018.4260

Resumo

Em geral, as análises comparativas feitas em trabalhos que estudam a tarefa de reconhecimento de usuários baseada na dinâmica da digitação utilizando algoritmos de Aprendizado de Máquina (AM) se restringem à avaliação considerando apenas um único conjunto de dados específico, em geral não público, o que torna difícil generalizar quais são, de fato, os algoritmos mais indicados para realizar tal tarefa. Assim, o presente trabalho tem como objetivo apresentar um estudo comparativo acerca do desempenho de alguns dos mais populares algoritmos de AM aplicados a tal tarefa utilizando cinco conjuntos de dados públicos compostos por amostras estáticas (textos fixos, como senhas, por ex.). Os experimentos realizados mostraram que o Random Forest foi capaz de superar os demais algoritmos em todos os conjuntos de dados analisados.

Referências

Ali, H., Wahyudi, and Salami, M. J. E. (2009). Keystroke pressure based typing biometrics authentication system by combining ANN and ANFIS-based classifiers. In 2009 5th International Colloquium on Signal Processing Its Applications, pages 198–203.

Ali, M. L., Monaco, J. V., Tappert, C. C., and Qiu, M. (2017). Keystroke Biometric Systems for User Authentication. Journal of Signal Processing Systems, 86(2):175–190.

Alpar, O. (2017). Frequency spectrograms for biometric keystroke authentication using neural network based classifier. Knowledge-Based Systems, 116:163–171.

Alpar, O. (2018). Biometric touchstroke authentication by fuzzy proximity of touch locations. Future Generation Computer Systems, 86:71–80.

Alshanketi, F., Traore, I., and Ahmed, A. A. (2016). Improving Performance and Usability in Mobile Keystroke Dynamic Biometric Authentication. In 2016 IEEE Security and Privacy Workshops (SPW), pages 66–73.

Alsultan, A. and Warwick, K. (2013). Keystroke dynamics authentication: a survey of free-text methods. International Journal of Computer Science Issues, 10(4):1–10.

Antal, M. and Nemes, L. (2016). The MOBIKEY Keystroke Dynamics Password Database: Benchmark Results. In Software Engineering Perspectives and Application in Intelligent Systems, pages 35–46. Springer.

Antal, M. and Szabó, L. Z. (2015). An Evaluation of One-Class and Two-Class Classification Algorithms for Keystroke Dynamics Authentication on Mobile Devices. In 2015 20th International Conference on Control Systems and Computer Science, pages 343–350.

Antal, M., Szabó, L. Z., and László, I. (2015). Keystroke dynamics on android platform. Procedia Technology, 19:820–826.

Banerjee, S. P. and Woodard, D. L. (2012). Biometric authentication and identification using keystroke dynamics: A survey. Journal of Pattern Recognition Research, 7(1):116–139.

Bhatia, A. and Hanmandlu, M. (2018). Keystroke Dynamics Based Authentication Using Possibilistic Renyi Entropy Features and Composite Fuzzy Classifier. Journal of Modern Physics, 9(02):112.

Breiman, L. (2001). Random Forests. Machine Learning, 45(1):5–32. Çeker, H. and Upadhyaya, S. (2017). Sensitivity analysis in keystroke dynamics using convolutional neural networks. In 2017 IEEE Workshop on Information Forensics and Security (WIFS), pages 1–6.

Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3):273–297.

Cover, T. and Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1):21–27.

Cruz, M. A. S., Duarte, J. C., and Goldschmidt, R. R. (2017). Dinâmica da Digitação Aplicada à Autenticação Periódica de Usuários em Ambientes Virtuais de Aprendizagem. Revista Brasileira de Informática na Educação - RBIE, 2:1–30.

Deng, Y. and Zhong, Y. (2013). Keystroke dynamics user authentication based on gaussian mixture model and deep belief nets. ISRN Signal Processing, 2013:1–30.

Deng, Y. and Zhong, Y. (2015). Keystroke dynamics advances for mobile devices using deep neural network. Recent Advances in User Authentication Using Keystroke Dynamics Biometrics, 2:59–70.

Dowland, P. S. and Furnell, S. M. (2004). A long-term trial of keystroke profiling using digraph, trigraph and keyword latencies. In IFIP International Information Security Conference, pages 275–289.

El-Abed, M., Dafer, M., and Khayat, R. E. (2014). RHU Keystroke: A mobile-based benchmark for keystroke dynamics systems. In 2014 International Carnahan Conference on Security Technology (ICCST), pages 1–4.

Faceli, K., Lorena, A. C., Gama, J., and Carvalho, A. (2011). Inteligência Artificial: Uma abordagem de aprendizado de máquina, volume 2. LTC.

Filho, J. R. M. and Freire, E. O. (2006). On the equalization of keystroke timing histograms. Pattern Recognition Letters, 27(13):1440–1446.

Fischer, A. and Igel, C. (2014). Training restricted Boltzmann machines: An introduction. Pattern Recognition, 47(1):25–39.

Gaines, R. S., Lisowski, W., Press, S. J., and Shapiro, N. (1980). Authentication by keystroke timing: Some preliminary results. Technical report, Rand Corp Santa Monica CA.

Giot, R., El-Abed, M., and Christophe, R. (2009a). GREYC Keystroke: a Benchmark for Keystroke Dynamics Biometric Systems. In IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS 2009), Washington, District of Columbia, USA. IEEE Computer Society.

Giot, R., El-Abed, M., Hemery, B., and Rosenberger, C. (2011). Unconstrained keystroke dynamics authentication with shared secret. Computers & Security, 30(6):427–445.

Giot, R., El-Abed, M., and Rosenberger, C. (2009b). Keystroke dynamics with low constraints SVM based passphrase enrollment. In 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, pages 1–6.

Giot, R., El-Abed, M., and Rosenberger, C. (2012). Web-Based Benchmark for Keystroke Dynamics Biometric Systems: A Statistical Analysis. In 2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pages 11–15.

Goldschmidt, R., Bezerra, E., and Passos, E. (2015). Data Mining: conceitos, técnicas, algoritmos, orientações e aplicações. Elsevier, Rio de Janeiro, 2. ed. edition.

Gunetti, D. and Ruffo, G. (1999). Intrusion detection through behavioral data. In IDA, volume 99, pages 383–394.

Han, J., Pei, J., and Kamber, M. (2011). Data mining: concepts and techniques. Elsevier. Ho, G. (2014). Tapdynamics: strengthening user authentication on mobile phones with keystroke dynamics. Technical report, Technical report, Stanford University.

Hu, J., Gingrich, D., and Sentosa, A. (2008). A k-nearest neighbor approach for user authentication through biometric keystroke dynamics. In Communications, 2008. ICC’08. IEEE International Conference on, pages 1556–1560.

Jain, A. K., Nandakumar, K., and Ross, A. (2016). 50 years of biometric research: Accomplishments, challenges, and opportunities. Pattern Recognition Letters, 79:80–105.

Karnan, M. and Akila, M. (2010). Personal Authentication Based on Keystroke Dynamics Using Soft Computing Techniques. In 2010 Second International Conference on Communication Software and Networks, pages 334–338.

Killourhy, K. S. and Maxion, R. A. (2009). Comparing anomaly-detection algorithms for keystroke dynamics. In Dependable Systems & Networks, 2009. DSN’09. IEEE/IFIP International Conference on, pages 125–134.

Kobojek, P. and Saeed, K. (2016). Application of Recurrent Neural Networks for User Verification based on Keystroke Dynamics. Journal of Telecommunications and Information Technology, 1(3):80.

Langley, P., Iba, W., Thompson, K., and Others (1992). An analysis of Bayesian classifiers. In Aaai, volume 90, pages 223–228.

Lee, H., Hwang, J. Y., Kim, D. I., Lee, S., Lee, S.-H., and Shin, J. S. (2018). Understanding Keystroke Dynamics for Smartphone Users Authentication and Keystroke Dynamics on Smartphones Built-In Motion Sensors. Security and Communication Networks, 2018.

Lin, C.-H., Liu, J.-C., and Lee, K.-Y. (2018). On Neural Networks for Biometric Authentication Based on Keystroke Dynamics. Sensors and Materials, 30(3):385–396.

Loy, C. C., Lai, W. K., and Lim, C. P. (2007). Keystroke Patterns Classification Using the ARTMAP-FD Neural Network. In Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2007), volume 1, pages 61–64.

Loy, C. C., Lim, C. P., and Lai, W. K. (2005). Pressure-based typing biometrics user authentication using the fuzzy ARTMAP neural network. In Proceedings of the Twelfth International Conference on Neural Information Processing (ICONIP 2005), pages 647–652. Citeseer.

Maheshwary, S., Ganguly, S., and Pudi, V. (2017). Deep secure: A fast and simple neural network based approach for user authentication and identification via keystroke dynamics. In IWAISe: First International Workshop on Artificial Intelligence in Security, page 59.

Maxion, R. A. and Killourhy, K. S. (2010). Keystroke biometrics with number-pad input. In 2010 IEEE/IFIP International Conference on Dependable Systems Networks (DSN), pages 201–210.

Meng, Y., Wong, D. S., and Kwok, L.-F. (2014). Design of Touch Dynamics Based User Authentication with an Adaptive Mechanism on Mobile Phones. In Proceedings of the 29th Annual ACM Symposium on Applied Computing, SAC ’14, pages 1680–1687, New York, NY, USA. ACM.

Ngugi, B., Kahn, B. K., and Tremaine, M. (2011). Typing Biometrics: Impact of Human Learning on Performance Quality. J. Data and Information Quality, 2(2):11:1—-11:21.

O’Gorman, L. (2003). Comparing passwords, tokens, and biometrics for user authentication. Proceedings of the IEEE, 91(12):2021–2040.

Pavaday, N. and Soyjaudah, K. M. S. (2007). Investigating performance of neural networks in authentication using keystroke dynamics. In AFRICON 2007, pages 1–8.

Pisani, P. H. and Lorena, A. C. (2012). Evolutionary neural networks applied to keystroke dynamics: Genetic and immune based. In 2012 IEEE Congress on Evolutionary Computation, pages 1–8.

Pisani, P. H. and Lorena, A. C. (2013). A systematic review on keystroke dynamics. Journal of the Brazilian Computer Society, 19(4):573–587.

Pisani, P. H., Lorena, A. C., and d. Carvalho, A. C. P. L. F. (2015). Ensemble of Adaptive Algorithms for Keystroke Dynamics. In 2015 Brazilian Conference on Intelligent Systems (BRACIS), pages 310–315.

Pisani, P. H., Lorena, A. C., and de Carvalho, A. (2018). Adaptive Biometric Systems using Ensembles. IEEE Intelligent Systems, page 1.

Rodrigues, R. N., Yared, G. F. G., Costa, C. R. d. N., Yabu-Uti, J. B. T., Violaro, F., and Ling, L. L. (2006). Biometric access control through numerical keyboards based on keystroke dynamics. In International Conference on Biometrics, pages 640–646. Springer.

Rosenblatt, F. (1962). Principles of Neurodynamics. Spartan Book, New York. Salzberg, S. L. (1994). C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993. Machine Learning, 16(3):235–240.

Sêmola, M. (2014). Gestão da segurança da informação, volume 2. Elsevier Brasil.

Sen, S. and Muralidharan, K. (2014). Putting x2018;pressure x2019; on mobile authentication. In 2014 Seventh International Conference on Mobile Computing and Ubiquitous Networking (ICMU), pages 56–61.

Sheng, Y., Phoha, V. V., and Rovnyak, S. M. (2005). A parallel decision tree-based method for user authentication based on keystroke patterns. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 35(4):826–833.

Shimshon, T., Moskovitch, R., Rokach, L., and Elovici, Y. (2010). Clustering di-graphs for continuously verifying users according to their typing patterns. In 2010 IEEE 26-th Convention of Electrical and Electronics Engineers in Israel, pages 445–449.

Shinde, P., Shetty, S., and Mehra, M. (2016). Survey of Keystroke Dynamics as a Biometric for Static Authentication. International Journal of Computer Science and Information Security, 14(4):203.

Sim, T. and Janakiraman, R. (2007). Are digraphs good for free-text keystroke dynamics? In Computer Vision and Pattern Recognition, 2007. CVPR’07. IEEE Conference on, pages 1–6. IEEE.

Sulong, A., Wahyudi, and Siddiqi, M. U. (2009). Intelligent keystroke pressure-based typing biometrics authentication system using radial basis function network. In 2009 5th International Colloquium on Signal Processing Its Applications, pages 151–155.

Teh, P. S., Teoh, A. B. J., and Yue, S. (2013). A survey of keystroke dynamics biometrics. The Scientific World Journal, 2013:1–30.

Teh, P. S., Zhang, N., Teoh, A. B. J., and Chen, K. (2016). A survey on touch dynamics authentication in mobile devices. Computers & Security, 59:210–235.

Thanganayagam, R. and Thangadurai, A. (2015). Fusion approach on keystroke dynamics to enhance the performance of password authentication. In 2015 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), pages 1–6.

Thierry, E. and Chuan, C. (2018). One-class SVM for biometric authentication by keystroke dynamics for remote evaluation. Computational Intelligence, 34(1):145–160.

Wu, J. and Chen, Z. (2015). An Implicit Identity Authentication System Considering Changes of Gesture Based on Keystroke Behaviors. International Journal of Distributed Sensor Networks, 11(6).

Yu, E. and Cho, S. (2003). Novelty Detection Approach for Keystroke Dynamics Identity Verification. In Liu, J., Cheung, Y.-m., and Yin, H., editors, Intelligent Data Engineering and Automated Learning, pages 1016–1023, Berlin, Heidelberg. Springer Berlin Heidelberg.