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A Hybrid Machine Learning Technique for Multiple Soft Biometric Based Dynamic Keystroke Pattern Recognition System

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Abstract

Recently, the keystroke biometric authentication system is the notable research area because of it is low cost and flexible integration support. Keystroke gratitude is one of the branches of biometrics that is considered to support regular passwords which mainly used for continuous authentication scenarios like online examination and military intelligence. Numerous studies have been conducted on the basis of data acquisition tools, character representation, classification methods, test protocols, and evaluations. Furthermore, the classification of biometric data is always difficult because this information is often mismatched and depends on human behavior. A private keystroke is difficult to perform and can be used for authentication. In this paper, we propose a hybrid machine learning technique for multiple soft biometric based dynamic keystroke pattern recognition system as a novelty of this study. We propose a novel multi-objective swarm optimization algorithm to select optimal features among multiple features of keystrokes. We extract different soft biometric features such as age, color, gender, weight, height and race from the users. We illustrate an optimal Cat induced whale optimization algorithm to fuse the optimal weight features of multiple biometric responses. We propose an optimal learning based recurrent neural network (OL-RNN) classifier to recognize the keystroke patterns. Finally, the performance of proposed OL-RNN classifier is compare with the obtainable state-of-art classifiers n conditions of dissimilar performance metrics.

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References

  1. Sheng Y, Phoha VV, Rovnyak SM (2005) A parallel decision tree-based method for user authentication based on keystroke patterns. IEEE Trans Syst Man, Cybern Part B Cybern 35(4):826–833

    Article  Google Scholar 

  2. Schclar A, Rokach L, Abramson A, Elovici Y (2012) User authentication based on representative users. IEEE Trans Syst, Man, Cybern, Part C Appl Rev 42(6):1669–1678

    Article  Google Scholar 

  3. Araújo LC, Sucupira LH, Lizarraga MG, Ling LL, Yabu-Uti JBT (2005) User authentication through typing biometrics features. IEEE Trans Signal Process 53(2):851–855

    Article  MathSciNet  MATH  Google Scholar 

  4. Pisani PH, Giot R, De Carvalho AC, Lorena AC (2016) Enhanced template update: application to keystroke dynamics. Comput Secur 60:134–153

    Article  Google Scholar 

  5. Wang X, Guo F, Ma JF (2012) User authentication via keystroke dynamics based on difference subspace and slope correlation degree. Digital Signal Processing 22(5):707–712

    Article  MathSciNet  Google Scholar 

  6. 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 

  7. Alpar O (2014) Keystroke recognition in user authentication using ANN based RGB histogram technique. Eng Appl Artif Intell 32:213–217

    Article  Google Scholar 

  8. Kang P, Cho S (2009) A hybrid novelty score and its use in keystroke dynamics-based user authentication. Pattern Recogn 42(11):3115–3127

    Article  MATH  Google Scholar 

  9. Hwang SS, Cho S, Park S (2009) Keystroke dynamics-based authentication for mobile devices. Comput Secur 28(1–2):85–93

    Article  Google Scholar 

  10. Bhana B, Flowerday S (2020) Passphrase and keystroke dynamics authentication: Usable security. Comput Secur 96:101925

    Article  Google Scholar 

  11. Nauman M, Ali T, Rauf A (2013) Using trusted computing for privacy preserving keystroke-based authentication in smartphones. Telecommun Syst 52(4):2149–2161

    Article  Google Scholar 

  12. Revett K (2009) A bioinformatics based approach to user authentication via keystroke dynamics. Int J Control Autom Syst 7(1):7–15

    Article  Google Scholar 

  13. Syed Z, Banerjee S, Cukic B (2016) Normalizing variations in feature vector structure in keystroke dynamics authentication systems. Software Qual J 24(1):137–157

    Article  Google Scholar 

  14. Clarke NL, Furnell SM (2007) Authenticating mobile phone users using keystroke analysis. Int J Inf Secur 6(1):1–14

    Article  Google Scholar 

  15. Xi K, Tang Y, Hu J (2011) Correlation keystroke verification scheme for user access control in cloud computing environment. Comput J 54(10):1632–1644

    Article  Google Scholar 

  16. Urtiga EVC, Moreno ED (2011) Keystroke-based biometric authentication in mobile devices. IEEE Lat Am Trans 9(3):368–375

    Article  Google Scholar 

  17. Kang P (2015) The effects of different alphabets on free text keystroke authentication: a case study on the Korean-English users. J Syst Softw 102:1–11

    Article  Google Scholar 

  18. Liu CL, Tsai CJ, Chang TY, Tsai WJ, Zhong PK (2015) Implementing multiple biometric features for a recall-based graphical keystroke dynamics authentication system on a smart phone. J Netw Comput Appl 53:128–139

    Article  Google Scholar 

  19. Ho J, Kang DK (2017) Mini-batch bagging and attribute ranking for accurate user authentication in keystroke dynamics. Pattern Recogn 70:139–151

    Article  Google Scholar 

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

    Article  Google Scholar 

  21. Ho J, Kang DK (2018) One-class naïve Bayes with duration feature ranking for accurate user authentication using keystroke dynamics. Appl Intell 48(6):1547–1564

    Article  Google Scholar 

  22. 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 

  23. Wang Y, Wu C, Zheng K, Wang X (2019) Improving reliability: User authentication on smartphones using keystroke biometrics. IEEE Access 7:26218–26228

    Article  Google Scholar 

  24. Saini BS, Singh P, Nayyar A, Kaur N, Bhatia KS, El-Sappagh S, Hu JW (2020) A three-step authentication model for mobile phone user using keystroke dynamics. IEEE Access 8:125909–125922

    Article  Google Scholar 

  25. Huang A, Gao S, Chen J, Xu L, Nathan A (2020) High security user authentication enabled by piezoelectric keystroke dynamics and machine learning. IEEE Sens J 20(21):13037–13046

    Article  Google Scholar 

  26. Kim DI, Lee S, Shin JS (2020) A new feature scoring method in keystroke dynamics-based user authentications. IEEE Access 8:27901–27914

    Article  Google Scholar 

  27. Kiyani AT, Lasebae A, Ali K, Rehman MU, Haq B (2020) Continuous user authentication featuring keystroke dynamics based on robust recurrent confidence model and ensemble learning approach. IEEE Access 8:156177–156189

    Article  Google Scholar 

  28. 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 

  29. Lu X, Zhang S, Hui P, Lio P (2020) Continuous authentication by free-text keystroke based on CNN and RNN. Comput Secur 96:101861

    Article  Google Scholar 

  30. Toosi R, Akhaee MA (2021) Time–frequency analysis of keystroke dynamics for user authentication. Futur Gener Comput Syst 115:438–447

    Article  Google Scholar 

  31. Ramu T, Suthendran K, Arivoli T (2019) Machine learning based soft biometrics for enhanced keystroke recognition system. Multimed Tools Appl 79:10029–10045

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, KSR College of Engineering, Tiruchengode, Tamilnadu, India

    V. Shanmugavalli

  2. Department of Computer Science and Engineering, Swarnandhra College of Engineering & Technology, Narasapuram West Godavari, Andhra Pradesh, India

    S. Suresh Kumar

  3. Department of Information Technology, KSR College of Engineering, Tiruchengode, Tamilnadu, India

    S. Nithya Kalyani

Authors
  1. V. Shanmugavalli
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  2. S. Suresh Kumar
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  3. S. Nithya Kalyani
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Correspondence to V. Shanmugavalli.

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Shanmugavalli, V., Suresh Kumar, S. & Nithya Kalyani, S. A Hybrid Machine Learning Technique for Multiple Soft Biometric Based Dynamic Keystroke Pattern Recognition System. Neural Process Lett 55, 10845–10871 (2023). https://doi.org/10.1007/s11063-023-11354-6

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