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A comprehensive approach for mitigating impersonation in online assessment: integrity policy and random authentication

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Abstract

The security of online assessments is a major concern due to widespread cheating. One common form of cheating is impersonation, where students invite unauthorized persons to take assessments on their behalf. Several techniques exist to handle impersonation. Some researchers recommend use of integrity policy, but communicating the policy effectively to the students is a challenge. Others propose authentication methods like, password and fingerprint; they offer initial authentication but are vulnerable thereafter. Face recognition offers post-login authentication but necessitates additional hardware. Keystroke Dynamics (KD) has been used to provide post-login authentication without any additional hardware, but its use is limited to subjective assessment.

In this work, we address impersonation in assessments with Multiple Choice Questions (MCQ). Our approach combines two key strategies: reinforcement of integrity policy for prevention, and keystroke-based random authentication for detection of impersonation. To the best of our knowledge, it is the first attempt to use keystroke dynamics for post-login authentication in the context of MCQ. We improve an online quiz tool for the data collection suited to our needs and use feature engineering to address the challenge of high-dimensional keystroke datasets. Using machine learning classifiers, we identify the best-performing model for authenticating the students. The results indicate that the highest accuracy (83%) is achieved by the Isolation Forest classifier. Furthermore, to validate the results, the approach is applied to Carnegie Mellon University (CMU) benchmark dataset, thereby achieving an improved accuracy of 94%. Though we also used mouse dynamics for authentication, but its subpar performance leads us to not consider it for our approach.

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Data availability

The data that support the findings of this study are available at https://github.com/anitagoel/DU-Dataset. Data is provided within the manuscript.

References

  1. James, R.: Tertiary student attitudes to invigilated, online summative examinations. Int. J. Educ. Technol. High. Educ. (2016). https://doi.org/10.1186/s41239-016-0015-0

    Article  Google Scholar 

  2. Xiong, Y., Suen, H.K.: Assessment approaches in massive open online courses: possibilities, challenges and future directions. Int. Rev. Educ. 64, 241–263 (2018). https://doi.org/10.1007/s11159-018-9710-5

    Article  MATH  Google Scholar 

  3. Newton, P.M., Essex, K.: How common is cheating in online exams and did it increase during the COVID-19 pandemic? A Syst. Rev. J. Acad. Ethics. 22, 323–343 (2024). https://doi.org/10.1007/s10805-023-09485-5

    Article  MATH  Google Scholar 

  4. Susnjak, T., McIntosh, T.: ChatGPT: the end of online exam integrity? Educ. Sci. 14, 656 (2024). https://doi.org/10.3390/educsci14060656

    Article  MATH  Google Scholar 

  5. Chirumamilla, A., Sindre, G., Nguyen-Duc, A.: Cheating in e-exams and paper exams: the perceptions of engineering students and teachers in Norway. Assess. Eval. High. Educ. 45, 940–957 (2020). https://doi.org/10.1080/02602938.2020.1719975

    Article  Google Scholar 

  6. Mungai, P.K., Huang, R.: Using keystroke dynamics in a multi-level architecture to protect online examinations from impersonation. 2017 IEEE 2nd Int. Conf. Big Data Anal. ICBDA 2017. 622–627 (2017). https://doi.org/10.1109/ICBDA.2017.8078710

  7. Corrigan-Gibbs, H., Gupta, N., Northcutt, C., Cutrell, E., Thies, W.: Deterring cheating in online environments. ACM Trans. Comput. Interact. 22, 1–23 (2015). https://doi.org/10.1145/2810239

    Article  Google Scholar 

  8. Bhana, B., Flowerday, S.: Passphrase and keystroke dynamics authentication: usable security. Comput. Secur. 96, 101925 (2020). https://doi.org/10.1016/j.cose.2020.101925

    Article  Google Scholar 

  9. Al-Saraireh, J., AlJa’afreh, M.R.: Keystroke and swipe biometrics fusion to enhance smartphones authentication. Comput. Secur. 125, 103022 (2023). https://doi.org/10.1016/j.cose.2022.103022

    Article  MATH  Google Scholar 

  10. Lee, H., Hwang, J.Y., Lee, S., Kim, D.I., Lee, S.H., Lee, J., Shin, J.S.: A parameterized model to select discriminating features on keystroke dynamics authentication on smartphones. Pervasive Mob. Comput. 54, 45–57 (2019). https://doi.org/10.1016/j.pmcj.2019.02.001

    Article  MATH  Google Scholar 

  11. Thomas, P.A., Preetha Mathew, K.: A broad review on non-intrusive active user authentication in biometrics. J. Ambient. Intell. Humaniz. Comput. 14, 339–360 (2023). https://doi.org/10.1007/s12652-021-03301-x

    Article  MATH  Google Scholar 

  12. Neha, C.K.: Continuous user authentication system: a risk analysis based approach. Wirel. Pers. Commun. 108, 281–295 (2019). https://doi.org/10.1007/s11277-019-06403-0

    Article  MATH  Google Scholar 

  13. Garg, M., Goel, A.: A systematic literature review on online assessment security: Current challenges and integrity strategies. Comput. Secur. 113, 102544 (2022). https://doi.org/10.1016/j.cose.2021.102544

    Article  MATH  Google Scholar 

  14. Killourhy, K.S., Maxion, R.A.: Comparing anomaly-detection algorithms for keystroke dynamics. Proc. Int. Conf. Dependable Syst. Netw. 125–134 (2009). https://doi.org/10.1109/DSN.2009.5270346

  15. Amoako, P.Y.O., Osunmakinde, I.O.: Emerging bimodal biometrics authentication for non-venue-based assessments in open distance e-learning (OdeL) environments. Int. J. Technol. Enhanc. Learn. 12, 218–244 (2020). https://doi.org/10.1504/IJTEL.2020.106287

    Article  Google Scholar 

  16. Benson, L., Rodier, K., Enström, R., Bocatto, E.: Developing a university-wide academic integrity e-learning tutorial: a Canadian case. Int. J. Educ. Integr. 15, 1–23 (2019). https://doi.org/10.1007/s40979-019-0045-1

    Article  Google Scholar 

  17. Stoesz, B.M., Eaton, S.E., Miron, J., Thacker, E.J.: Academic integrity and contract cheating policy analysis of colleges in Ontario, Canada. Int. J. Educ. Integr. (2019). https://doi.org/10.1007/s40979-019-0042-4

    Article  Google Scholar 

  18. Sara Lowe, M., Londino-Smolar, G., Wendeln, K.E.A., Sturek, D.L.: Promoting academic integrity through a stand-alone course in the learning management system. Int J Educ Integr (2018). https://doi.org/10.1007/s40979-018-0035-8

    Article  Google Scholar 

  19. Morris, E.J.: Academic integrity matters: Five considerations for addressing contract cheating. Int. J. Educ. Integr. (2018). https://doi.org/10.1007/s40979-018-0038-5

    Article  MATH  Google Scholar 

  20. Mason, T., Gavrilovska, A., Joyner, D.A.: Collaboration Versus Cheating. 1004–1010 (2019). https://doi.org/10.1145/3287324.3287443

  21. Nguyen, J.G., Keuseman, K.J., Humston, J.J.: Minimize online cheating for online assessments during covid-19 pandemic. J. Chem. Educ. 97, 3429–3435 (2020). https://doi.org/10.1021/acs.jchemed.0c00790

    Article  MATH  Google Scholar 

  22. Lavine, M.H., Roussin, C.J.: From idea to action. J. Manag. Educ. 36, 428–455 (2012). https://doi.org/10.1177/1052562911428602

    Article  MATH  Google Scholar 

  23. Stephens, J.M., Wangaard, D.B.: The achieving with integrity seminar: an integrative approach to promoting moral development in secondary school classrooms. Int. J. Educ. Integr. 12, 3 (2016). https://doi.org/10.1007/s40979-016-0010-1

    Article  Google Scholar 

  24. East, J., Donnelly, L.: Taking responsibility for academic integrity: a collaborative teaching and learning design. J Univ Teach Learn Pract (2012). https://doi.org/10.53761/1.9.3.2

    Article  MATH  Google Scholar 

  25. Labayen, M., Vea, R., Florez, J., Aginako, N., Sierra, B.: Online student authentication and proctoring system based on multimodal biometrics technology. IEEE Access. 9, 72398–72411 (2021). https://doi.org/10.1109/ACCESS.2021.3079375

    Article  Google Scholar 

  26. Ullah, A., Xiao, H., Barker, T.: A dynamic profile questions approach to mitigate impersonation in online examinations. J. Grid Comput. 17, 209–223 (2019). https://doi.org/10.1007/s10723-018-9442-6

    Article  MATH  Google Scholar 

  27. Ullah, A., Xiao, H., Barker, T.: A study into the usability and security implications of text and image based challenge questions in the context of online examination. Educ. Inf. Technol. 24, 13–39 (2019). https://doi.org/10.1007/s10639-018-9758-7

    Article  MATH  Google Scholar 

  28. Ngqondi, T., Maoneke, P.B., Mauwa, H.: A secure online exams conceptual framework for South African universities. Soc. Sci. Humanit. Open. 3, 100132 (2021). https://doi.org/10.1016/j.ssaho.2021.100132

    Article  Google Scholar 

  29. Okada, A., Whitelock, D., Holmes, W., Edwards, C.: e-Authentication for online assessment: a mixed-method study. Br. J. Educ. Technol. 50, 861–875 (2019). https://doi.org/10.1111/bjet.12608

    Article  Google Scholar 

  30. Yang, H., Meng, X., Zhao, X., Wang, Y., Liu, Y., Kang, X., Shen, J., Huang, W.: CKDAN: Content and keystroke dual attention networks with pre-trained models for continuous authentication. Comput. Secur. 128, 103159 (2023). https://doi.org/10.1016/j.cose.2023.103159

    Article  Google Scholar 

  31. Rahman, A., Chowdhury, M.E.H., Khandakar, A., Kiranyaz, S., Zaman, K.S., Reaz, M.B.I., Islam, M.T., Ezeddin, M., Kadir, M.A.: Multimodal EEG and keystroke dynamics based biometric system using machine learning algorithms. IEEE Access. 9, 94625–94643 (2021). https://doi.org/10.1109/ACCESS.2021.3092840

    Article  Google Scholar 

  32. Amigud, A., Arnedo-Moreno, J., Daradoumis, T., Guerrero-Roldan, A.E.: Using learning analytics for preserving academic integrity. Int. Rev. Res. Open Distance Learn. 18, 192–210 (2017). https://doi.org/10.19173/irrodl.v18i5.3103

    Article  Google Scholar 

  33. Sabbah, Y.W.: Security of Online Examinations. Presented at the (2017)

  34. Renzella, J., Cain, A., Schneider, J.G.: Verifying student identity in oral assessments with deep speaker. Comput. Educ. Artif. Intell. 3, 100044 (2022). https://doi.org/10.1016/j.caeai.2021.100044

    Article  MATH  Google Scholar 

  35. Subramanian, N.S., Narayanan, S., Soumya, M.D., Jayakumar, N., Bijlani, K.: Using aadhaar for continuous test-taker presence verification in online exams. In: Advances in intelligent systems and computing, pp. 11–19. Springer, Singapore (2018)

    MATH  Google Scholar 

  36. Opgen-Rhein, J., Küppers, B., Schroeder, U.: Requirements for author verification in electronic computer science exams. CSEDU 2019 - Proc. 11th Int. Conf. Comput. Support. Educ. 2: 432–439 (2019). https://doi.org/10.5220/0007736104320439

  37. Iwasokun, G.B., Akinyokun, O.C., Omomule, T.G.: Design of e-invigilation framework using multi-modal biometrics. 2019 15th Int. Conf. Electron. Comput. Comput. ICECCO 2019. (2019). https://doi.org/10.1109/ICECCO48375.2019.9043223

  38. Eude, T., Chang, C.: One-class SVM for biometric authentication by keystroke dynamics for remote evaluation. Comput. Intell. 34, 145–160 (2018). https://doi.org/10.1111/coin.12122

    Article  MathSciNet  MATH  Google Scholar 

  39. Garg, M., Goel, A.: Efficiency of biometric recognition technology based on typing dynamics in Mooc. Turkish Online J. Distance Educ. 21, 79–87 (2020). https://doi.org/10.17718/tojde.770922

    Article  MATH  Google Scholar 

  40. Kochegurova, E.A., Zateev, R.P.: Hidden monitoring based on keystroke dynamics in online examination system. Program. Comput. Softw. 48, 385–398 (2022). https://doi.org/10.1134/S0361768822060044

    Article  Google Scholar 

  41. Alpar, O.: Biometric keystroke barcoding: a next-gen authentication framework. Expert Syst. Appl. 177, 114980 (2021). https://doi.org/10.1016/j.eswa.2021.114980

    Article  MATH  Google Scholar 

  42. Yang, L., Li, C., You, R., Tu, B., Li, L.: TKCA: a timely keystroke-based continuous user authentication with short keystroke sequence in uncontrolled settings. Cybersecurity. (2021). https://doi.org/10.1186/s42400-021-00075-9

    Article  MATH  Google Scholar 

  43. Kiyani, A.T., Lasebae, A., Ali, K., Rehman, M.U., Haq, B.: Continuous user authentication featuring keystroke dynamics based on robust recurrent confidence model and ensemble learning approach. IEEE Access. 8, 156177–156189 (2020). https://doi.org/10.1109/ACCESS.2020.3019467

    Article  Google Scholar 

  44. Choi, M., Lee, S., Jo, M., Shin, J.S.: Keystroke dynamics-based authentication using unique keypad. Sensors. 21, 1–19 (2021). https://doi.org/10.3390/s21062242

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  46. Lu, X., Zhang, S., Hui, P., Lio, P.: Continuous authentication by free-text keystroke based on CNN and RNN. Comput. Secur. 96, 101861 (2020). https://doi.org/10.1016/j.cose.2020.101861

    Article  Google Scholar 

  47. Alsuhibany, S.A., Almuqbil, A.S.: Analyzing the effectiveness of touch keystroke dynamic authentication for the arabic language. Wirel. Commun. Mob. Comput. (2021). https://doi.org/10.1155/2021/9963129

    Article  Google Scholar 

  48. Sae-Bae, N., Memon, N.: Distinguishability of keystroke dynamic template. PLoS ONE 17, 1–17 (2022). https://doi.org/10.1371/journal.pone.0261291

    Article  MATH  Google Scholar 

  49. De-Marcos, L., Martínez-Herráiz, J.J., Junquera-Sánchez, J., Cilleruelo, C., Pages-Arévalo, C.: Comparing machine learning classifiers for continuous authentication on mobile devices by keystroke dynamics. Electron. 10, (2021). https://doi.org/10.3390/electronics10141622

  50. Porwik, P., Doroz, R., Wesolowski, T.E.: Dynamic keystroke pattern analysis and classifiers with competence for user recognition. Appl. Soft Comput. 99, 106902 (2021). https://doi.org/10.1016/j.asoc.2020.106902

    Article  MATH  Google Scholar 

  51. Guan, J., Li, X., Zhang, Y.: Design and Implementation of Continuous Authentication Mechanism Based on Multimodal Fusion Mechanism. Secur. Commun. Networks. 2021, (2021). https://doi.org/10.1155/2021/6669429

  52. Buckley, O., Hodges, D., Windle, J., Earl, S.: CLICKA: Collecting and leveraging identity cues with keystroke dynamics. Comput. Secur. 120, (2022). https://doi.org/10.1016/j.cose.2022.102780

  53. El-Kenawy, E.S.M., Mirjalili, S., Abdelhamid, A.A., Ibrahim, A., Khodadadi, N., Eid, M.M.: Meta-heuristic optimization and keystroke dynamics for authentication of smartphone users. Mathematics. 10, 1–26 (2022). https://doi.org/10.3390/math10162912

    Article  MATH  Google Scholar 

  54. Kim, D.I., Lee, S., Shin, J.S.: a new feature scoring method in keystroke dynamics-based user authentications. IEEE Access. 8, 27901–27914 (2020). https://doi.org/10.1109/ACCESS.2020.2968918

    Article  Google Scholar 

  55. Kim, J., Kang, P.: Freely typed keystroke dynamics-based user authentication for mobile devices based on heterogeneous features. Pattern Recog 108, 107556 (2020). https://doi.org/10.1016/j.patcog.2020.107556

    Article  MATH  Google Scholar 

  56. Parkinson, S., Khan, S., Crampton, A., Xu, Q., Xie, W., Liu, N., Dakin, K.: Password policy characteristics and keystroke biometric authentication. IET Biometrics. 10, 163–178 (2021). https://doi.org/10.1049/bme2.12017

    Article  Google Scholar 

  57. Chang, T.Y., Tsai, C.J., Yeh, J.Y., Peng, C.C., Chen, P.H.: New soft biometrics for limited resource in keystroke dynamics authentication. Multimed. Tools Appl. 79, 23295–23324 (2020). https://doi.org/10.1007/s11042-020-09042-x

    Article  MATH  Google Scholar 

  58. Wang, X., Zheng, Q., Zheng, K., Wu, T.: User authentication method based on MKL for keystroke and mouse behavioral feature fusion. Secur. Commun. Netw. (2020). https://doi.org/10.1155/2020/9282380

    Article  MATH  Google Scholar 

  59. Shi, Y., Wang, X., Zheng, K., Cao, S.: User authentication method based on keystroke dynamics and mouse dynamics using HDA. Multimed. Syst. 29, 653–668 (2023). https://doi.org/10.1007/s00530-022-00997-5

    Article  MATH  Google Scholar 

  60. Abbas, S.A., Aslam, A., Rehman, A.U., Abbasi, W.A., Arif, S., Kazmi, S.Z.H.: K-Means and K-Medoids: Cluster Analysis on Birth Data Collected in City Muzaffarabad. Kashmir. IEEE Access. 8, 151847–151855 (2020). https://doi.org/10.1109/ACCESS.2020.3014021

    Article  Google Scholar 

  61. Neha, C.: Biometric re-authentication: an approach towards achieving transparency in user authentication. Multimed. Tools Appl. 78, 6679–6700 (2019)

    Article  MATH  Google Scholar 

  62. https://github.com/anitagoel/iQuiz

  63. Tsai, C.J., Shih, K.J.: Mining a new biometrics to improve the accuracy of keystroke dynamics-based authentication system on free-text. Appl. Soft Comput. J. 80, 125–137 (2019). https://doi.org/10.1016/j.asoc.2019.03.033

    Article  MATH  Google Scholar 

  64. Falah, A., Pan, L., Huda, S., Pokhrel, S.R., Anwar, A., Kumar, N.: Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs. Futur. Gener. Comput. Syst. 8, 2529–2535 (2021). https://doi.org/10.1109/GLOCOM.2016.7841839

    Article  Google Scholar 

  65. AbdelRaouf, H., Chelloug, S.A., Muthanna, A., Semary, N., Amin, K., Ibrahim, M.: Efficient convolutional neural network-based keystroke dynamics for boosting user authentication. Sensors. 23, 4898 (2023). https://doi.org/10.3390/s23104898

    Article  Google Scholar 

  66. Yousef, M., Saçar Demirci, M.D., Khalifa, W., Allmer, J.: Feature selection has a large impact on one-class classification accuracy for micrornas in plants. Adv. Bioinformatics. (2016). https://doi.org/10.1155/2016/5670851

    Article  Google Scholar 

  67. Liu, Z., Japkowicz, N., Wang, R., Liu, L.: A sub-concept-based feature selection method for one-class classification. Soft. Comput. 24, 7047–7062 (2020). https://doi.org/10.1007/s00500-020-04828-5

    Article  MATH  Google Scholar 

  68. Xie, M., Hu, J., Slay, J.: Evaluating host-based anomaly detection systems: Application of the one-class SVM algorithm to ADFA-LD. In: 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). pp. 978–982. IEEE (2014)

  69. Liu, F.T., Ting, K.M., Zhou, Z.H.: LOF Identifying density-based local outliers. SIGMOD Rec ACM Spec. Interes. Gr. Manag. Data 29, 93–104 (2008). https://doi.org/10.1109/ICDM.2008.17

    Article  MATH  Google Scholar 

  70. Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. Proc. - IEEE Int. Conf. Data Mining, ICDM.(2008). https://doi.org/10.1109/ICDM.2008.17

  71. Pisani, P.H., Giot, R., De Carvalho, A.C., Lorena, A.C.: Enhanced template update: Application to keystroke dynamics. Computers & Security. 60, 134–53 (2016)

    Article  Google Scholar 

  72. Krishna, G.J., Ravi, V.: Keystroke based User Authentication using Modified Differential Evolution. IEEE Reg. 10 Annu. Int. Conf. Proceedings/TENCON. 2019-Octob, 739–744 (2019). https://doi.org/10.1109/TENCON.2019.8929371

  73. Purwar, D.K., Vishwakarma, D., Singh, N., Khemchandani, V.: One v/s All SVM Implementation for Keystroke based Authentication System. 2019 4th Int. Conf. Inf. Syst. Comput. Netw. 268–272 (2019). https://doi.org/10.1109/ISCON47742.2019.9036203

  74. Ali, M.L., Thakur, K., Obaidat, M.A.: A Hybrid Method for Keystroke Biometric User Identification. Electronics 11(17), 2782 (2022). https://doi.org/10.3390/electronics11172782

    Article  MATH  Google Scholar 

  75. Chang, H.-C., Li, J., Ching-Seh, W., Stamp, M.: Machine learning and deep learning for fixed-text keystroke dynamics. In: Stamp, M., Visaggio, C.A., Mercaldo, F., di Troia, F. (eds.) Artificial intelligence for cybersecurity, pp. 309–329. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-030-97087-1_13

    Chapter  MATH  Google Scholar 

  76. Kharbat, F.F., Abu Daabes, A.S.: E-proctored exams during the COVID-19 pandemic: a close understanding. Educ. Inf. Technol. (2021). https://doi.org/10.1007/s10639-021-10458-7

    Article  MATH  Google Scholar 

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  1. Department of Computer Science, University of Delhi, Delhi, India

    Manika Garg

  2. The Hague University of Applied Sciences, The Hague, The Netherlands

    Manika Garg

  3. Department of Computer Science, Dyal Singh College, University of Delhi, Delhi, India

    Anita Goel

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Garg, M., Goel, A. A comprehensive approach for mitigating impersonation in online assessment: integrity policy and random authentication. Int. J. Inf. Secur. 24, 1 (2025). https://doi.org/10.1007/s10207-024-00931-y

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