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Predicting Useful Information From Typing Patterns Using a Bootstrapped-Based Homogeneous Ensemble Approach

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Applied Computing for Software and Smart Systems

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 555))

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Abstract

Nowadays, the way a user connects with computing devices is being analysed with the goal of extracting useful information for some interesting applications beyond user authentication. It enables the development of next-generation intelligent human-computer interaction, auto-profiling, and soft biometrics. In this paper, a bootstrapped-based homogeneous ensemble model has been proposed without wasting rare samples in each bootstrapped training set to overcome the uneven distribution of classes (common in keystroke dynamics) for predicting users’ traits, fine motor skills, and cognitive deficiency automatically using the user’s daily typing habit. This model is lightweight, faster, and could be implemented in low-configured devices like smartphones. The proposed model has been verified with a more realistic evaluation and several shared and authentic keystroke dynamics (KD) datasets and achieved 93.21% of accuracy in predicting age group, 65.35% in identifying gender, 87.14% for handedness, 77.14% in the case of hand(s) used determination, 91.25% in predicting qualification, 74.44% in recognising typing skill, 58.45% in observing lie, 84.12% in the determination of Parkinson’s disease (PD), and 99.14% in predicting emotional stress (ES). The proposed model might be used for a wide range of more interesting applications, including automatic user profiling in social networking, age-restricted access control to protect kids from Internet threats, age- and gender-specific product recommendations in e-commerce, medical diagnostics at-home environment for better treatment and therapy management, soft biometric traits in improving biometric models, learner’s cognitive deficiency in effective online teaching and learning, cognitive deficiency marker in a competitive examination, unbiased online feedback collection, and online inquiry correctness measurement, to mention a few.

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References

  1. Abinaya R Sowmiya R (2021) Soft biometric based keystroke classification using PSO optimized neural network. Mater Today: Proc 1–4. https://doi.org/10.1016/j.matpr.2021.01.733

  2. Adams WR (2017) High-accuracy detection of early Parkinson’s Disease using multiple characteristics of finger movement while typing. PLoS ONE 12(11):1–20. https://doi.org/10.1371/journal.pone.0188226

  3. Bernardi ML, Cimitile M, Martinelli F, Mercaldo F (2019) Keystroke analysis for user identification using deep neural networks. In: Proceedings of the international joint conference on neural networks. https://doi.org/10.1109/IJCNN.2019.8852068

  4. Chen T, He T, Benesty M, Khotilovich V, Tang Y, Cho H, Chen K, Mitchell R, Cano I, Zhou T, Li M, Xie J, Lin M, Geng Y, Li Y (2020) Package “xgboost” CRAN 10.1145/2939672.2939785, ‘github.com/dmlc/xgboost/issues’

    Google Scholar 

  5. Dacunhasilva DR, Wang Z, Gutierrez-Osuna R (2021) Towards participant-independent stress detection using instrumented peripherals. IEEE Trans Affect Comput 1–18. https://doi.org/10.1109/TAFFC.2021.3061417

  6. Dacunhasilva DR, Wang Z, Gutierrez-Osuna R (2021) Towards participant-independent stress detection using instrumented peripherals. IEEE Trans Affect Comput 1–18. https://doi.org/10.1109/TAFFC.2021.3061417

  7. Davarci E, Soysal B, Erguler I, Aydin SO, Dincer O, Anarim E (2017) Age group detection using smartphone motion sensors. In: 2017 25th European Signal Processing Conference (EUSIPCO), pp 2265–2269 (2017)

    Google Scholar 

  8. Dhir N, Edman M, Sanchez Ferro Á, Stafford T, Bannard C (2020) Identifying robust markers of Parkinson’s disease in typing behaviour using a CNN-LSTM network. In: Proceedings of the 24th conference on computational natural language learning, pp 578–595. https://doi.org/10.18653/v1/2020.conll-1.47

  9. Forsen G, Nelson M, Staron RJ (1977) Personal attributes authentication techniques. Technical report, Rome Air Development Center

    Google Scholar 

  10. Gao F, Mei X, Chen AC (2015) Delayed finger tapping and cognitive responses in preterm-born male teenagers with mild spastic diplegia. Pediatric Neurol 52(2):206–213. https://doi.org/10.1016/j.pediatrneurol.2014.04.012

  11. Ghosh S, Sahu S, Ganguly N, Mitra B, De P (2019) EmoKey: an emotion-aware smartphone keyboard for mental health monitoring. In: 2019 11th international conference on communication systems and networks, COMSNETS 2019. https://doi.org/10.1109/COMSNETS.2019.8711078

  12. Giancardo L, Sánchez-Ferro A, Arroyo-Gallego T, Butterworth I, Mendoza CS, Montero P, Matarazzo M, Obeso JA, Gray ML, Estépar RSJ (2016) Computer keyboard interaction as an indicator of early Parkinson’s disease. Sci Rep 6:1–10. https://doi.org/10.1038/srep34468

  13. Giot R, El-Abed M, Rosenberger C (2012) Web-based benchmark for keystroke dynamics biometric systems: a statistical analysis. In: Intelligent information hiding andmultimedia signal processing (IIH-MSP), pp 11–15. http://arxiv.org/abs/1207.0784

  14. Giot R, Rosenberger C (2012) A new soft biometric approach for keystroke dynamics based on gender recognition. Int J Inf Technol Manag (IJITM) Special Issue Adv Trends Biometrics 11(August): 1–16. https://doi.org/10.1504/IJITM.2012.044062

  15. Hooman Oroojeni MJ, Oldfield J, Nicolaou MA (2019) Detecting early Parkinson’s disease from keystroke dynamics using the tensor-train decomposition. In: European signal processing conference, vol 2019-Septe. https://doi.org/10.23919/EUSIPCO.2019.8902562

  16. Hubel KA, Yund EW, Herron TJ, Woods DL (2013) Computerized measures of finger tapping: Reliability, malingering and traumatic brain injury. J Clin Exp Neuropsychol 35(7):745–758. https://doi.org/10.1080/13803395.2013.824070

  17. Iakovakis D, Hadjidimitriou S, Charisis V, Bostanjopoulou S, Katsarou Z, Klingelhoefer L, Mayer S, Reichmann H, DIas SB, DIniz JA, Trivedi D, Chaudhuri RK, Hadjileontiadis LJ (2019) Early Parkinson’s disease detection via touchscreen typing analysis using convolutional neural networks. In: Proceedings of the annual international conference of the IEEE engineering in medicine and biology society, EMBS, pp 3535–3538. https://doi.org/10.1109/EMBC.2019.8857211

  18. Iakovakis D, Hadjidimitriou S, Charisis V, Bostanjopoulou S, Katsarou Z, Klingelhoefer L, Mayer S, Reichmann H, DIas SB, DIniz JA, Trivedi D, Chaudhuri RK, Hadjileontiadis LJ (2019) Early Parkinson’s disease detection via touchscreen typing analysis using convolutional neural networks. In: Proceedings of the annual international conference of the IEEE engineering in medicine and biology society, EMBS. https://doi.org/10.1109/EMBC.2019.8857211

  19. Iakovakis D, Hadjidimitriou S, Charisis V, Bostantjopoulou S, Katsarou Z, Klingelhoefer L, Reichmann H, Dias SB, Diniz JA, Trivedi D, Chaudhuri KR, Hadjileontiadis LJ (2018) Motor impairment estimates via touchscreen typing dynamics toward Parkinson’s Disease detection from data harvested in-the-wild. Frontiers ICT 5. https://doi.org/10.3389/fict.2018.00028

  20. Iakovakis D, Hadjidimitriou S, Charisis V, Bostantzopoulou S, Katsarou Z, Hadjileontiadis LJ (2018) Touchscreen typing-pattern analysis for detecting fine motor skills decline in early-stage Parkinson’s disease. Sci Rep 8(1):1–13. https://doi.org/10.1038/s41598-018-25999-0

  21. Killourhy KS (2012) A Scientific Understanding of Keystroke Dynamics. PhD thesis

    Google Scholar 

  22. Lam K, Meijer K, Loonstra F, Coerver E, Twose J, Redeman E, Moraal B, Barkhof F, de Groot V, Uitdehaag B, Killestein J (2020) Real-world keystroke dynamics are a potentially valid biomarker for clinical disability in multiple sclerosis. Mult Scler J 135245852096879. https://doi.org/10.1177/1352458520968797

  23. Lim YM, Ayesh A, Stacey M (2020) Continuous stress monitoring under varied demands using unobtrusive devices. Int J Hum-Comput Interact 36(4). https://doi.org/10.1080/10447318.2019.1642617

  24. Milne A, Farrahi K, Nicolaou MA (2018) Less is more: univariate modelling to detect early Parkinson’s Disease from keystroke dynamics. In: Lecture notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol 11198 LNAI. https://doi.org/10.1007/978-3-030-01771-2_28

  25. Monaro M, Galante C, Spolaor R, Li QQ, Gamberini L, Conti M, Sartori G (2018) Covert lie detection using keyboard dynamics. Sci Rep. https://doi.org/10.1038/s41598-018-20462-6

  26. Monaro M, Zampieri I, Sartori G, Pietrini P, Orrù G (2021) The detection of faked identity using unexpected questions and choice reaction times. Psychol Res 85(6):2474–2482. https://doi.org/10.1007/s00426-020-01410-4

  27. Ntracha A, Iakovakis D, Hadjidimitriou S, Charisis VS, Tsolaki M, Hadjileontiadis LJ (2020) Detection of mild cognitive impairment through natural language and touchscreen typing processing. Front Digit Health 2(October):1–13. https://doi.org/10.3389/fdgth.2020.567158

  28. Oyebola O, Adesina AO (2021) Predicting age group and gender of smartphone users using keystroke biometrics. Malays J Sci Adv Technol 1(4):124–128

    Google Scholar 

  29. Pentel A (2017) High precision handedness detection based on short input keystroke dynamics. In: 8th international conference on information, intelligence, systems and applications, IISA 2017. https://doi.org/10.1109/IISA.2017.8316380

  30. Pham TD (2018) Pattern analysis of computer keystroke time series in healthy control and early-stage Parkinson’s disease subjects using fuzzy recurrence and scalable recurrence network features. J Neurosci Methods 307. https://doi.org/10.1016/j.jneumeth.2018.05.019

  31. Rocha R, Carneiro D, Costa R, Analide C (2020) Continuous authentication in mobile devices using behavioral biometrics. Adv Intell Syst Comput. https://doi.org/10.1007/978-3-030-24097-4_23

  32. Roy S, Roy U, Sinha D (2019) Analysis of typing pattern in identifying soft biometric information and its impact in user recognition. In: Advances in intelligent systems and computing, vol 699. Springer, Singapore. https://doi.org/10.1007/978-981-10-7590-2_5

  33. Roy S, Roy U, Sinha DD (2020) Deep learning approach in predicting personal traits based on the way user type on touchscreen. In: Advances in intelligent systems and computing, vol 999. https://doi.org/10.1007/978-981-13-9042-5_27

  34. Roy S, Roy U, Sinha D (2018) Identifying soft biometric traits through typing pattern on touchscreen phone. In: Social transformation—digital way, pp 546–561. Springer, Singapore. https://doi.org/10.1007/978-981-13-1343-1_46

  35. Roy S, Roy U, Sinha D (2018) Protection of kids from internet threats : a machine learning approach for classification of age group based on typing pattern. In: Proceedings of the international multiconference of engineers and computer scientists, vol I

    Google Scholar 

  36. Roy S, Roy U, Sinha D (2019) Analysis of typing pattern in identifying soft biometric information and its impact in user recognition. In: Information technology and applied mathematics, advances in intelligent systems and computing, pp. 69–83. Springer, Singapore. https://doi.org/10.1007/978-981-10-7590-2_5

  37. Roy S, Roy U, Sinha D (2022) Identifying age group and gender based on activities on touchscreen. Int J Biom 14(1):61. https://doi.org/10.1504/ijbm.2022.10042835

  38. Roy S, Roy U, Sinha D (2017) User authentication: keystroke dynamics with soft biometric features. In: Group, CPT & F (ed) Internet of Things (IOT) technologies, applications, challenges and solutions, chap. 6, pp 105–124. CRC Press, Boca Raton, FL 33487–2742, 1 edn.

    Google Scholar 

  39. Roy S, Roy U, Sinha D (2018) Protection of kids from internet threats: a machine learning approach for classification of age-group based on typing pattern. In: Lecture notes in engineering and computer science

    Google Scholar 

  40. Roy S, Roy U, Sinha D (2018) The probability of predicting personality traits by the way user types on touch screen. In: Innovations in systems and software engineering, pp 1–8. https://doi.org/10.1007/s11334-018-0317-6

  41. Sağbaş EA, Korukoglu S, Balli S (2020) Stress detection via keyboard typing behaviors by using smartphone sensors and machine learning techniques. J Med Syst 44(4). https://doi.org/10.1007/s10916-020-1530-z

  42. Shute S, Ko RK, Chaisiri S (2017) Attribution using keyboard row based behavioural biometrics for handedness recognition. In: Proceedings—16th IEEE international conference on trust, security and privacy in computing and communications, 11th IEEE international conference on big data science and engineering and 14th IEEE international conference on embedded software and systems. https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.363

  43. Tsimperidis I, Arampatzis A (2020) The keyboard knows about you. Int J Technoethics 11(2). https://doi.org/10.4018/ijt.2020070103

  44. Tsimperidis I, Rostami S, Katos V (2017) Age detection through keystroke dynamics from user authentication failures. Int J Digit Crime Forensics. https://doi.org/10.4018/IJDCF.2017010101

  45. Tsimperidis I, Rostami S, Wilson K, Katos V (2021) User attribution through keystroke dynamics-based author age estimation. In: Lecture notes in networks and systems, pp 47–61. Springer, Cham. https://doi.org/10.1007/978-3-030-64758-2_4, https://link.springer.com/chapter/10.1007/978-3-030-64758-2_4

  46. Tsimperidis I, Yucel C, Katos V (2021) Age and gender as cyber attribution features in keystroke dynamic-based user classification processes. Electronics (Switzerland) 10(7):1–14. https://doi.org/10.3390/electronics10070835

  47. Udandarao V, Agrawal M, Kumar R, Shah RR (2020) On the inference of soft biometrics from typing patterns collected in a multi-device environment. In: Proceedings—2020 IEEE 6th international conference on multimedia big data, BigMM 2020, pp 76–85. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/BigMM50055.2020.00021

  48. Yaacob MN, Syed Idrus SZ, Wan Mustafa WA, Jamlos MA, Abd Wahab MH (2021) Identification of the exclusivity of individual’s typing style using soft biometric elements. Ann Emerg Technol Comput 5(Special issue 5):10–26. https://doi.org/10.33166/aetic.2021.05.002

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Calcutta, Acharya Prafulla Chandra Roy Siksha Prangan, JD-2, Sector - III, Saltlake City, Kolkata, 700106, India

    Soumen Roy, Devadatta Sinha & Rajat Kumar Pal

  2. Department of Computer System Sciences, VisvaBharati, Santiniketan, 731235, India

    Utpal Roy

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  1. Soumen Roy
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  2. Utpal Roy
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  3. Devadatta Sinha
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  4. Rajat Kumar Pal
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Correspondence to Soumen Roy .

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Editors and Affiliations

  1. School of Information Technology, University of Calcutta, Kolkata, India

    Rituparna Chaki

  2. Department of Environmental Sciences, Informatics and Statistics, Ca’ Foscari University, Venice, Italy

    Agostino Cortesi

  3. Faculty of Computer Science, Bialystok University of Technology, Bialystok, Poland

    Khalid Saeed

  4. Department of Computer Science and Engineering, University of Calcutta, Kolkata, India

    Nabendu Chaki

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Roy, S., Roy, U., Sinha, D., Pal, R.K. (2023). Predicting Useful Information From Typing Patterns Using a Bootstrapped-Based Homogeneous Ensemble Approach. In: Chaki, R., Cortesi, A., Saeed, K., Chaki, N. (eds) Applied Computing for Software and Smart Systems. Lecture Notes in Networks and Systems, vol 555. Springer, Singapore. https://doi.org/10.1007/978-981-19-6791-7_1

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