Keystroke Dynamic Authentication Using Combined MHR (Mean of Horner’s Rules) and Standard Deviation
Corresponding Author(s) : Didih Rizki Chandranegara
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol 4, No 1, February 2019
Abstract
Keystroke Dynamic Authentication used a behavior to authenticate the user and one of biometric authentication. The behavior used a typing speed a character on the keyboard and every user had a unique behavior in typing. To improve classification between user and attacker of Keystroke Dynamic Authentication in this research, we proposed a combination of MHR (Mean of Horner’s Rules) and standard deviation. The results of this research showed that our proposed method gave a high accuracy (93.872%) than the previous method (75.388% and 75.156%). This research gave an opportunity to implemented in real login system because our method gave the best results with False Acceptance Rate (FAR) is 0.113. The user can be used as a simple password and ignore a worrying about an account hacking in the system.
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- J. Ho and D. Kang, “One-class naïve Bayes with duration feature ranking for accurate user authentication using keystroke dynamics”, Applied Intelligence, vol. 48, no. 6, pp. 1547-1564, 2018.
- C. Lin, J. Liu, K. Lee, “On Neural Networks for Biometric Authentication Based on Keystroke Dynamics”, Sensors and Materials, vol. 30, no.3, pp. 385-396, 2018.
- F. Monrose and A. Rubin, “Keystroke dynamics as a biometric for authentication”, Future Generation Computer Systems, vol. 16, no. 4, pp. 351-359, 2000.
- M. Ali, J. Monaco, C. Tappert, and M. Qiu, “Keystroke Biometric Systems for User Authentication”, Journal of Signal Processing Systems, pp. 1-16, 2016.
- R. Joyce and G. Gupta, “Identity authentication based on keystroke latencies”, Communications of the ACM, vol. 33, no. 2, pp. 168-176, 1990.
- W. Yang and F. Fang, “Application of a Dynamic Identity Authentication Model Based on an Improved Keystroke Rhythm Algorithm”, International Journal of Communications, Network and System Sciences, vol. 2, no. 8, pp. 714-719, 2009.
- E. Maiorana, P. Campisi, N. Gonzáles-Carballo, and A. Neri, “Keystroke dynamics authentication for mobile phones”, in ACM Symposium on Applied Computing, 2011, pp. 21-26.
- A. Morales, M. Falanga, J. Fierrez, C. Sansone, and J. Ortega-Garcia, “Keystroke Dynamics Recognition based on Personal Data : A Comparative Experimental Evaluation Implementing Reproducible Research Keystroke Dynamics Recognition based on Personal Data : A Comparative Experimental Evaluation Implementing Reproducible Research”, in International Conference on Biometrics Theory, Applications and Systems (BTAS), 2015, pp. 1-6.
- D. Stefan and D. Yao, “Keystroke-dynamics authentication against synthetic forgeries”, in 6th International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2010), 2010, pp. 1-8.
- Ignacio de Mendizabal-V´azquez, Daniel de Santos-Sierra, J. Guerra-Casanova, and C. S´anchez- ´Avila, “Supervised classification methods applied to Keystroke Dynamics through Mobile Devices”, in International Carnahan Conference on Security Technology (ICCST), 2014, pp. 1-6.
- I. Sluganovi´c, A. Karlovi´c, P. Bosilj, M. Šare, and S. Horvat, “User Authentication Based on Keystroke Dynamics Analysis”, in MIPRO, 2012 Proceedings of the 35th International Convention, 2012, pp. 2136-2141.
- F. M. Al-Athari and A. Khalaf Husein, “Selection of the Best threshold in Biometric
- Authentication by Exhaustive Statistical Pre-Testing”, International Journal of Computer and Information Technology, vol.03, no. 04, pp. 787-791, 2014.
- K. S. Killourhy and R. A. Maxion, “Comparing anomaly-detection algorithms for keystroke dynamics”, in Proceedings of the International Conference on Dependable Systems and Networks, 2009, pp. 125-134.
- H. Crawford, “Keystroke Dynamics: Characteristics and Opportunities”, in Eighth Annual International Conference on Privacy, Security and Trust, 2010, 205-212.
- N. D’Lima and J. Mittal, “Password Authentication using Keystroke Biometrics”, in International Conference on Communication, Information & Computing Technology (ICCICT), 2015, pp. 1-6.
References
J. Ho and D. Kang, “One-class naïve Bayes with duration feature ranking for accurate user authentication using keystroke dynamics”, Applied Intelligence, vol. 48, no. 6, pp. 1547-1564, 2018.
C. Lin, J. Liu, K. Lee, “On Neural Networks for Biometric Authentication Based on Keystroke Dynamics”, Sensors and Materials, vol. 30, no.3, pp. 385-396, 2018.
F. Monrose and A. Rubin, “Keystroke dynamics as a biometric for authentication”, Future Generation Computer Systems, vol. 16, no. 4, pp. 351-359, 2000.
M. Ali, J. Monaco, C. Tappert, and M. Qiu, “Keystroke Biometric Systems for User Authentication”, Journal of Signal Processing Systems, pp. 1-16, 2016.
R. Joyce and G. Gupta, “Identity authentication based on keystroke latencies”, Communications of the ACM, vol. 33, no. 2, pp. 168-176, 1990.
W. Yang and F. Fang, “Application of a Dynamic Identity Authentication Model Based on an Improved Keystroke Rhythm Algorithm”, International Journal of Communications, Network and System Sciences, vol. 2, no. 8, pp. 714-719, 2009.
E. Maiorana, P. Campisi, N. Gonzáles-Carballo, and A. Neri, “Keystroke dynamics authentication for mobile phones”, in ACM Symposium on Applied Computing, 2011, pp. 21-26.
A. Morales, M. Falanga, J. Fierrez, C. Sansone, and J. Ortega-Garcia, “Keystroke Dynamics Recognition based on Personal Data : A Comparative Experimental Evaluation Implementing Reproducible Research Keystroke Dynamics Recognition based on Personal Data : A Comparative Experimental Evaluation Implementing Reproducible Research”, in International Conference on Biometrics Theory, Applications and Systems (BTAS), 2015, pp. 1-6.
D. Stefan and D. Yao, “Keystroke-dynamics authentication against synthetic forgeries”, in 6th International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2010), 2010, pp. 1-8.
Ignacio de Mendizabal-V´azquez, Daniel de Santos-Sierra, J. Guerra-Casanova, and C. S´anchez- ´Avila, “Supervised classification methods applied to Keystroke Dynamics through Mobile Devices”, in International Carnahan Conference on Security Technology (ICCST), 2014, pp. 1-6.
I. Sluganovi´c, A. Karlovi´c, P. Bosilj, M. Šare, and S. Horvat, “User Authentication Based on Keystroke Dynamics Analysis”, in MIPRO, 2012 Proceedings of the 35th International Convention, 2012, pp. 2136-2141.
F. M. Al-Athari and A. Khalaf Husein, “Selection of the Best threshold in Biometric
Authentication by Exhaustive Statistical Pre-Testing”, International Journal of Computer and Information Technology, vol.03, no. 04, pp. 787-791, 2014.
K. S. Killourhy and R. A. Maxion, “Comparing anomaly-detection algorithms for keystroke dynamics”, in Proceedings of the International Conference on Dependable Systems and Networks, 2009, pp. 125-134.
H. Crawford, “Keystroke Dynamics: Characteristics and Opportunities”, in Eighth Annual International Conference on Privacy, Security and Trust, 2010, 205-212.
N. D’Lima and J. Mittal, “Password Authentication using Keystroke Biometrics”, in International Conference on Communication, Information & Computing Technology (ICCICT), 2015, pp. 1-6.