MULTIMODAL BIOMETRIK PADA KEYSTROKE USER-ADAPTIVE FEATURE DAN MAHALANOBIS DISTANCE

Authors

  • Ardityo Cahyo Putro Hutomo Hutomo Telkom University
  • Prasti Eko Yunanto Telkom University
  • Febryanti Sthevanie Telkom University

Keywords:

This research analyzes the effectiveness of the combination of User-Adaptive and Mahalanobis Distance methods in biometrics keystroke authentication systems. Using Biomey Keystroke Dataset with 40 respondents, this study aims to improve the accuracy and reliability of KD-based authentication. The developed system consists of enrollment and authentication stages, with User-Adaptive as the feature extraction method and Mahalanobis Distance for feature matching. Decision level fusion technique is applied to integrate the results of various keystroke features. The results obtained show that the fusion technique with Mahalanobis Distance shows better results compared to non-fusion features with an average error reduction of 8.73%. The optimal vector length (Fn) was found at n = 5 with an error value of 12.07%. The best threshold search resulted in a FAR of 15.6% and FRR of 6% at n = 5. The results obtained in this study show a lower error rate with an average decrease in error value of 9.9% with previous studies. This research proves the potential of Mahalanobis Distance and fusion techniques in improving the accuracy of biometrics keystroke authentication systems, opening up opportunities for the development of more reliable security systems. Further studies are recommended to explore certain patterns on the touch screen and the use of more varied datasets and real-time testing data. Keywords- authentication, biometrics

Abstract

This research analyzes the effectiveness of the combination of User-Adaptive and Mahalanobis Distance methods in biometrics keystroke authentication systems. Using Biomey Keystroke Dataset with 40 respondents, this study aims to improve the accuracy and reliability of KD-based authentication. The developed system consists of enrollment and authentication stages, with User-Adaptive as the feature extraction method and Mahalanobis Distance for feature matching. Decision level fusion technique is applied to integrate the results of various keystroke features. The results obtained show that the fusion technique with Mahalanobis Distance shows better results compared to non-fusion features with an average error reduction of 8.73%. The optimal vector length (Fn) was found at n = 5 with an error value of 12.07%. The best threshold search resulted in a FAR of 15.6% and FRR of 6% at n = 5. The results obtained in this study show a lower error rate with an average decrease in error value of 9.9% with previous studies. This research proves the potential of Mahalanobis Distance and fusion techniques in improving the accuracy of biometrics keystroke authentication systems, opening up opportunities for the development of more reliable security systems. Further studies are recommended to explore certain patterns on the touch screen and the use of more varied datasets and real-time testing data.

Keywords- authentication, biometrics

References

A. K. Jain and A. Ross, “Introduction to Biometrics,” in Handbook of Biometrics, A. K. Jain, P. Flynn, and A. A. Ross, Eds., Boston, MA: Springer US, 2008, pp. 1–22. doi: 10.1007/978-0-387-71041-9_1.

B. Ayotte, J. Huang, M. K. Banavar, D. Hou, and S. Schuckers, “Fast Continuous User Authentication Using Distance Metric Fusion of Free-Text Keystroke Data,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Jun. 2019, pp. 2380–2388. doi: 10.1109/CVPRW.2019.00292.

Y. M. Alginahi and M. N. Kabir, Eds., Authentication Technologies for Cloud Computing, IoT and Big Data. Institution of Engineering and Technology, 2019. doi: 10.1049/PBSE009E.

H. Kalita, E. Maiorana, and P. Campisi, “Keystroke Dynamics for Biometric Recognition in Handheld Devices,” in 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), Jul. 2020, pp. 410–416. doi: 10.1109/TSP49548.2020.9163524.

A. Rahman et al., “Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms,” IEEE Access, vol. 9, pp. 94625–94643, 2021, doi: 10.1109/ACCESS.2021.3092840.

A. Ross and A. K. Jain, “Multimodal biometrics: An overview,” in 2004 12th European Signal Processing Conference, Sep. 2004, pp. 1221–1224. Accessed: Jun. 23, 2024. [Online]. Available: https://ieeexplore.ieee.org/document/7080214.

Y. Zhong and Y. Deng, “A Survey on Keystroke Dynamics Biometrics: Approaches, Advances, and Evaluations,” 2015, pp. 1–22. doi: 10.15579/gcsr.vol2.ch1.

Y. Amankar, S. Gangurde, A. Khachane, and P. Y. Itankar, “User Authentication using Keystroke Analysis,” vol. 07, no. 04, 2020.

B. Ayotte, M. Banavar, D. Hou, and S. Schuckers, “Fast and Accurate Continuous User Authentication by Fusion of Instance-based, Free-text Keystroke Dynamics,” 2019 International Conference of the Biometrics Special Interest Group (BIOSIG), Sep. 2019, Accessed: Apr. 24, 2024. [Online]. Available: https://www.semanticscholar.org/paper/Fast-andAccurate-Continuous-User-Authentication-byAyotteBanavar/66249aca2610910caa80d71154db9396ac54 f771.

J. Kim, H. Kim, and P. Kang, “Keystroke dynamicsbased user authentication using freely typed text based on user-adaptive feature extraction and novelty detection,” Applied Soft Computing, vol. 62, pp. 1077–1087, Jan. 2018, doi: 10.1016/j.asoc.2017.09.045.

I. M, I. Karunanithi, and S. B. U, “Enhancing User Authentication through Keystroke Dynamics Analysis using Isolation Forest algorithm,” in 2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE), Feb. 2024, pp. 1–5. doi: 10.1109/icETITE58242.2024.10493648.

B. Ayotte, M. Banavar, D. Hou, and S. Schuckers, “Fast Free-Text Authentication via Instance-Based Keystroke Dynamics,” IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 2, no. 4, pp. 377–387, Oct. 2020, doi: 10.1109/TBIOM.2020.3003988.

J. Kim and P. Kang, “Freely typed keystroke dynamics-based user authentication for mobile devices based on heterogeneous features,” Pattern Recognition, vol. 108, p. 107556, Dec. 2020, doi: 10.1016/j.patcog.2020.107556.

Z. Qin, P. Zhao, T. Zhuang, F. Deng, Y. Ding, and T. Chen, “A survey of identity recognition via data fusion and feature learning - ScienceDirect,” Information Fusion, vol. 91, pp. 694–712, Mar. 2023, doi: 10.1016/j.inffus.2022.10.032.

A.-C. Iapa and V.-I. Cretu, “Modified Distance Metric That Generates Better Performance For The Authentication Algorithm Based On Free-Text Keystroke Dynamics,” in 2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI), May 2021, pp. 000455–000460. doi: 10.1109/SACI51354.2021.9465601.

R. Shadman, A. A. Wahab, M. Manno, M. Lukaszewski, D. Hou, and F. Hussain, “Keystroke Dynamics: Concepts, Techniques, and Applications,” Mar. 08, 2023, arXiv: arXiv:2303.04605. doi: 10.48550/arXiv.2303.04605.

I. R. Arrazaan, Sistem Autentikasi Pengguna Berbasis Keystroke Biometrics Dinamis Menggunakan Metode FACT. Universitas Telkom, S1 Informatika, 2023. Accessed: Aug. 23, 2024. [Online]. Available: https://openlibrary.telkomuniversity.ac.id/pustaka/19 7049/sistem-autentikasi-pengguna-berbasiskeystroke-biometrics-dinamis-menggunakanmetode-fact.html

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Published

2025-04-17

Issue

Vol. 4 No. 1 (2024): Desember 2024

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Articles

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