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Cross-device free-text keystroke dynamics authentication using federated learning

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Abstract

Free-text keystroke dynamics, the unique typing patterns of an individual, have been applied for the security of mobile devices by providing the non-intrusive and continuous user authentication. Existing authentication approaches mainly concentrate on the keystroke dynamics when operating a specific device, and overlook the generality of keystroke dynamics for cross-device user authentication. To tackle this problem, in this paper, we propose an efficient federated free-text keystroke dynamics mechanism to mitigate the difference in keyboards for cross-device authentication. Specifically, we explore and analyze the keystroke features of various keyboards and extract cross-device keystroke features. To protect user privacy, their type of rhythm information must be kept locally. We utilize federated learning based on the auxiliary model to train the authentication model. Our proposed solution was evaluated on a large-scale data set with 168,000 users. The experimental results show that our proposed solution performs well with great robustness across different types of keyboards.

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Availability of data and materials

The dataset generated and analyzed during the current study are available in Dhakal dataset, http://userinterfaces.aalto.fi/136Mkeystrokes.

Code availability

Not applicable

References

  1. Hintze D, Füller M, Scholz S, Findling RD, Muaaz M, Kapfer P, Nüßer W, Mayrhofer R (2019) Cormorant: on implementing risk-aware multi-modal biometric cross-device authentication for android. In: Proceedings of the 17th International Conference on Advances in Mobile Computing & Multimedia, pp 117–126

  2. Yang Y, Guo B, Wang Z, Li M, Yu Z, Zhou X (2019) Behavesense: continuous authentication for security-sensitive mobile apps using behavioral biometrics. Ad Hoc Netw 84:9–18

    Article  Google Scholar 

  3. Vijayakumar T (2022) Verification system for handwritten signatures with modular neural networks. J Artif Intell 4(3):211–218

    Google Scholar 

  4. Liang Y, Samtani S, Guo B, Yu Z (2020) Behavioral biometrics for continuous authentication in the internet-of-things era: an artificial intelligence perspective. IEEE Internet Things J 7(9):9128–9143. https://doi.org/10.1109/JIOT.2020.3004077

    Article  Google Scholar 

  5. Srivastava A, Kumar A (2022) Enhancement of authentication in the IoT network. J Algeb Statist 13(3):2328–2336

    Google Scholar 

  6. Stanciu V-D, Spolaor R, Conti M, Giuffrida C (2016) On the effectiveness of sensor-enhanced keystroke dynamics against statistical attacks. In: Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy, pp 105–112

  7. Miiri EM (2021) Using behavioral profiling through keystrokes dynamics and location verification authentication as a method of securing mobile banking transactions. PhD thesis, JKUAT-COPAS

  8. Alfalahi H, Khandoker AH, Chowdhury N, Iakovakis D, Dias SB, Chaudhuri KR, Hadjileontiadis LJ (2022) Diagnostic accuracy of keystroke dynamics as digital biomarkers for fine motor decline in neuropsychiatric disorders: a systematic review and meta-analysis. Sci Rep 12(1):7690

    Article  Google Scholar 

  9. Kim J, Kang P (2020) Freely typed keystroke dynamics-based user authentication for mobile devices based on heterogeneous features. Pattern Recogn 108:107556

    Article  Google Scholar 

  10. Altwaijry N (2020) Keystroke dynamics analysis for user authentication using a deep learning approach. International Journal of Computer Science and Network Security. 20(12):209–216

    Google Scholar 

  11. Ayotte B, Banavar M, Hou D, Schuckers S (2020) Fast free-text authentication via instance-based keystroke dynamics. IEEE Trans Biometr, Behavior, Identity Sci 2(4):377–387

    Article  Google Scholar 

  12. Alsultan A, Warwick K, Wei H (2018) Improving the performance of free-text keystroke dynamics authentication by fusion. Appl Soft Comput 70:1024–1033

    Article  Google Scholar 

  13. Nivasch K, Azaria A (2021) A deep genetic method for keyboard layout optimization. In: 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI), pp 435–441 . IEEE

  14. Porwik P, Doroz R, Wesolowski TE (2021) Dynamic keystroke pattern analysis and classifiers with competence for user recognition. Appl Soft Comput 99:106902

    Article  Google Scholar 

  15. McMahan B, Moore E, Ramage D, Hampson S, Arcas BA (2017) Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp 1273–1282 . PMLR

  16. Mhenni A, Cherrier E, Rosenberger C, Amara NEB (2019) Double serial adaptation mechanism for keystroke dynamics authentication based on a single password. Comput & Sec 83:151–166

    Article  Google Scholar 

  17. Al-Obaidi NM, Al-Jarrah MM (2016) Statistical median-based classifier model for keystroke dynamics on mobile devices. In: 2016 Sixth International Conference on Digital Information Processing and Communications (ICDIPC), pp 186–191 . IEEE

  18. Tsai C-J, Shih K-J (2019) Mining a new biometrics to improve the accuracy of keystroke dynamics-based authentication system on free-text. Appl Soft Comput 80:125–137

    Article  Google Scholar 

  19. Alsuhibany SA, Almushyti M, Alghasham N, Alkhudhayr F (2019) The impact of using different keyboards on free-text keystroke dynamics authentication for Arabic language. Inf & Comput Sec 27(2):221–232

    Google Scholar 

  20. Aversano L, Bernardi ML, Cimitile M, Pecori R (2021) Continuous authentication using deep neural networks ensemble on keystroke dynamics. Peer J Comput Sci 7:525

    Article  Google Scholar 

  21. Bernardi ML, Cimitile M, Martinelli F, Mercaldo F (2019) Keystroke analysis for user identification using deep neural networks. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp 1–8 . IEEE

  22. Allen JD (2010) An analysis of pressure-based keystroke dynamics algorithms. PhD thesis, Southern Methodist University

  23. Alsuhibany SA, Almuqbil AS (2021) Analyzing the effectiveness of touch keystroke dynamic authentication for the arabic language. Wire Commu Mobile Comput 2021

  24. Kang P, Cho S (2015) Keystroke dynamics-based user authentication using long and free text strings from various input devices. Inf Sci 308:72–93

    Article  Google Scholar 

  25. Belman AK, Phoha VV (2020) Discriminative power of typing features on desktops, tablets, and phones for user identification. ACM Trans Privacy Sec (TOPS). 23(1):1–36

    Article  Google Scholar 

  26. Curtin M, Tappert C, Villani M, Ngo G, Simone J, Fort HS, Cha S (2006) Keystroke biometric recognition on long-text input: a feasibility study. Proc Int MultiConf, Engr & Comp Sci (IMECS)

  27. Lamiche I, Bin G, Jing Y, Yu Z, Hadid A (2019) A continuous smartphone authentication method based on gait patterns and keystroke dynamics. J Ambient Intell Humaniz Comput 10(11):4417–4430

    Article  Google Scholar 

  28. Yi X, Wang C, Bi X, Shi Y (2020) Palmboard: Leveraging implicit touch pressure in statistical decoding for indirect text entry. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp 1–13

  29. Li J, Chang H-C, Stamp M (2022) Free-text keystroke dynamics for user authentication. In: Cybersecurity for Artificial Intelligence, pp 357–380

  30. Sattler F, Wiedemann S, Müller K-R, Samek W (2019) Robust and communication-efficient federated learning from non-iid data. IEEE Trans Neural Netw Learn Syst 31(9):3400–3413

    Article  Google Scholar 

  31. Yu F, Rawat AS, Menon A, Kumar S (2020) Federated learning with only positive labels. In: International Conference on Machine Learning, pp 10946–10956. PMLR

  32. Hosseini H, Park H, Yun S, Louizos C, Soriaga J, Welling M (2021) Federated learning of user verification models without sharing embeddings. In: International Conference on Machine Learning, pp 4328–4336 . PMLR

  33. Lin X, Chen H, Xu Y, Xu C, Gui X, Deng Y, Wang Y (2022) Federated learning with positive and unlabeled data. In: International Conference on Machine Learning, pp 13344–13355 . PMLR

  34. Oza P, Patel VM (2021) Federated learning-based active authentication on mobile devices. In: 2021 IEEE International Joint Conference on Biometrics (IJCB), pp 1–8 . IEEE

  35. Alsultan A, Warwick K, Wei H (2017) Non-conventional keystroke dynamics for user authentication. Pattern Recogn Lett 89:53–59

  36. Dhakal V, Feit AM, Kristensson PO, Oulasvirta A (2018) Observations on typing from 136 million keystrokes. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp 1–12

  37. Levenshtein VI, et al (1966) Binary codes capable of correcting deletions, insertions, and reversals. In: Soviet Physics Doklady, vol 10, pp 707–710 . Soviet Union

  38. Guo BH, Nixon MS, Carter JN (2019) Soft biometric fusion for subject recognition at a distance. IEEE Trans Biomet, Behavior, Identity Sci 1(4):292–301

    Article  Google Scholar 

  39. Kołakowska A (2013) A review of emotion recognition methods based on keystroke dynamics and mouse movements. In: 2013 6th International Conference on Human System Interactions (HSI), pp 548–555. IEEE

  40. Pentel A (2017) Predicting age and gender by keystroke dynamics and mouse patterns. In: Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization, pp 381–385

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Acknowledgements

This work was partially supported by the National Science Fund for Distinguished Young Scholars(62025205), and the National Natural Science Foundation of China (No. 62032020, 61960206008, 61725205).

Funding

This work was partially supported by the National Science Fund for Distinguished Young Scholars(62025205), and the National Natural Science Foundation of China (No.62032020,61960206008,61725205).

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Authors and Affiliations

  1. School of Computer Science, Northwestern Polytechnical University, 1 Dongxiang Road, Chang’an District, Xi’an, 710072, Shaanxi, China

    Yafang Yang, Bin Guo, Yunji Liang, Kaixing Zhao & Zhiwen Yu

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  1. Yafang Yang
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  2. Bin Guo
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  3. Yunji Liang
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  4. Kaixing Zhao
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  5. Zhiwen Yu
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Contributions

All authors contributed to the study conception and design. The first draft of the manuscript was written by Yafang Yang and all authors commented on the previous version of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Bin Guo.

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Yang, Y., Guo, B., Liang, Y. et al. Cross-device free-text keystroke dynamics authentication using federated learning. Pers Ubiquit Comput 28, 491–505 (2024). https://doi.org/10.1007/s00779-024-01832-6

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