An investigation of genetic algorithm-based feature selection techniques applied to keystroke dynamics biometrics

  • Tuany Mariah do Nascimento UFRN
  • Andrelyne Vitória de Oliveira UFRN
  • Márjory Abreu UFRN
  • Laura Emmanuella Santana UFRN


Due to the continuous use of social networks, users can be vulnerable to online situations such as paedophilia treats. One of the ways to do the investigation of an alleged pedophile is to verify the legitimacy of the genre that it claims. One possible technique to adopt is keystroke dynamics analysis. However, this technique can extract many attributes, causing a negative impact on the accuracy of the classifier due to the presence of redundant and irrelevant attributes. Thus, this work using the wrapper approach in features selection using genetic algorithms and as KNN, SVM and Naive Bayes classifiers. Bringing as best result the SVM classifier with 90% accuracy, identifying what is most suitable for both bases.


Antal, M. and Nemes, G. (2016). Gender recognition from mobile biometric data. In 2016 IEEE 11th International Symposium on Applied Computational Intelligence and Informatics (SACI), pages 243–248.

Da Silva, V. R., De Araujo Silva, J. C. G., and Da Costa-Abreu, M. (2016). A new brazilian hand-based behavioural biometrics database: data collection and analysis. In 7th International Conference on Imaging for Crime Detection and Prevention (ICDP 2016), pages 1–6.

Darabseh, A. and Namin, A. S. (2015). Effective user authentications using keystroke dynamics based on feature selections. In 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), pages 307–312.

Faceli, K., Lorena, A. C., J.Gama, and Carvalho, A. C. P. L. F. (2011). Articial Intelligence: A Machine Learning Approach. LTC, Rio de Janeiro.

Fairhurst, M. and Da Costa-Abreu, M. (2011). Using keystroke dynamics for gender identication in social network environment. In 4th International Conference on Imaging for Crime Detection and Prevention 2011 (ICDP 2011), pages 1–6.

Giot, R., El-Abed, M., and Rosenberger, C. (2009). Greyc keystroke: A benchmark for keystroke dynamics biometric systems. In 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, pages 1–6.

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. (2009). The weka data mining software: An update. SIGKDD Explorations, 11(1):10–18.

Kawamura, A. and Chakraborty, B. (2017). A hybrid approach for optimal feature subset selection with evolutionary algorithms. In 2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST), pages 564–568.

Santana, L. E. A. S. (2012). Optimization classiers committees: An approach based on lter for selecting subsets of attributes. PhD thesis, Universidade Federal do Rio Grande do Norte, Natal.

Santana, L. E. A. S. and Canuto, A. M. P. (2014). Filter-based optimization techniques for selection of feature subsets in ensemble systems. Expert Systems with Applications, 41(4, Part 2):1622 – 1631.

Tsimperidis, I., Arampatzis, A., and Karakos, A. (2018). Keystroke dynamics features for gender recognition. Digital Investigation, 24:4 – 10.
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DO NASCIMENTO, Tuany Mariah; DE OLIVEIRA, Andrelyne Vitória; ABREU, Márjory; SANTANA, Laura Emmanuella. An investigation of genetic algorithm-based feature selection techniques applied to keystroke dynamics biometrics. In: WORKSHOP DE TRABALHOS DE INICIAÇÃO CIENTÍFICA E DE GRADUAÇÃO - SIMPÓSIO BRASILEIRO DE SEGURANÇA DA INFORMAÇÃO E DE SISTEMAS COMPUTACIONAIS (SBSEG), 19. , 2019, São Paulo. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2019 . p. 43-46. DOI: