Intrusion detection using mouse dynamics

Intrusion detection using mouse dynamics

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Biometrics — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Compared to other behavioural biometrics, mouse dynamics is a less explored area. General purpose data sets containing unrestricted mouse usage data are usually not available. The Balabit data set was released in 2016 for a data science competition which, despite the few users covered, can be considered the first adequate publicly available data set. This study presents a performance evaluation study on this data set for impostor detection. The existence of very short benchmark test sessions makes this data set challenging. Raw data were segmented into mouse actions, such as mouse move, point and click, and drag and drop, and then several features were extracted. Mouse data is not sensitive; therefore, it is possible to collect negative mouse dynamics data and to use two-class classifiers. Impostor detection performance was measured using decisions based on several actions. The best performance of 0.92 area under curve was obtained on the benchmark test sessions of the data set. Drag and drop mouse actions proved to be the best actions for impostor detection despite the fact that this type of action represented only 10% of the data set.


    1. 1)
      • 1. Morales, A., Fierrez, J., Tolosana, R., et al: ‘Keystroke biometrics ongoing competition’, IEEE Access, 2016, 4, pp. 77367746.
    2. 2)
      • 2. Kratky, P., Chuda, D.: ‘Recognition of web users with the aid of biometric user model’, J. Intell. Inf. Syst., 2018, 4, pp. 126.
    3. 3)
      • 3. Ahmed, A.A.E., Traore, I.: ‘A new biometric technology based on mouse dynamics’, IEEE Trans. Dependable Secur. Comput., 2007, 4, (3), pp. 165179.
    4. 4)
      • 4. Shen, C., Cai, Z., Guan, X.: ‘Continuous authentication for mouse dynamics: a pattern-growth approach’. Proc. 2012 42nd Annual IEEE/IFIP Int. Conf. on Dependable Systems and Networks (DSN), Washington, DC, USA, IEEE Computer Society, 2012, pp. 112.
    5. 5)
      • 5. Shen, C., Cai, Z., Guan, X., et al: ‘User authentication through mouse dynamics’, IEEE Trans. Inf. Forensics Sec., 2013, 8, (1), pp. 1630.
    6. 6)
      • 6. Shen, C., Cai, Z., Guan, X., et al: ‘Performance evaluation of anomaly-detection algorithms for mouse dynamics’, Comput. Secur., 2014, 45, pp. 156171.
    7. 7)
      • 7. Fulop, A., Kovacs, L., Kurics, T., et al: ‘Balabit mouse dynamics challenge data set’. 2016. Available at, accessed 26 January 2019.
    8. 8)
      • 8. Gamboa, H., Fred, A.: ‘A behavioral biometric system based on human-computer interaction’. Proc. SPIE 5404, Biometric Technology for Human Identification, 25 August 2004, 2004, vol. 5404, pp. 381392.
    9. 9)
      • 9. Nakkabi, Y., Traore, I., Ahmed, A.A.E.: ‘Improving mouse dynamics biometric performance using variance reduction via extractors with separate features’, Trans. Syst. Man Cybern. A, 2010, 40, (6), pp. 13451353.
    10. 10)
      • 10. Ahmed, A.A.E., Traore, I.: ‘Dynamic sample size detection in continuous authentication using sequential sampling’. Proc. 27th Annual Computer Security Applications Conf. (ACSAC'11), New York, NY, USA, ACM, 2011, pp. 169176.
    11. 11)
      • 11. Zheng, N., Paloski, A., Wang, H.: ‘An efficient user verification system using angle-based mouse movement biometrics’, ACM Trans. Inf. Syst. Secur., 2016, 18, (3), pp. 11:111:27.
    12. 12)
      • 12. Zheng, N., Paloski, A., Wang, H.: ‘An efficient user verification system via mouse movements’. Proc. 18th ACM Conf. on Computer and Communications Security (CCS'11), New York, NY, USA, ACM, 2011, pp. 139150.
    13. 13)
      • 13. Feher, C., Elovici, Y., Moskovitch, R., et al: ‘User identity verification via mouse dynamics’, Inf. Sci., 2012, 201, pp. 1936.
    14. 14)
      • 14. Chuda, D., Kratky, P.: ‘Usage of computer mouse characteristics for identification in web browsing’. Proc. 15th Int. Conf. on Computer Systems and Technologies (CompSysTech'14), New York, NY, USA, ACM, 2014, pp. 218225.
    15. 15)
      • 15. Chuda, D., Kratky, P., Tvarozek, J.: ‘Mouse clicks can recognize web page visitors!’. Proc. 24th Int. Conf. on World Wide Web (WWW'15 Companion), New York, NY, USA, ACM, 2015, pp. 2122.
    16. 16)
      • 16. Hinbarji, Z., Albatal, R., Gurrin, C.: ‘Dynamic user authentication based on mouse movements curves’. MultiMedia Modeling, 2015, pp. 111122.
    17. 17)
      • 17. Carneiro, D., Castillo, J.C., Novais, P., et al: ‘Multimodal behavioral analysis for non-invasive stress detection’, Expert Syst. Appl., 2012, 39, (18), pp. 1337613389.
    18. 18)
      • 18. Carneiro, D., Novais, P., Pego, J.M., et al: ‘Using mouse dynamics to assess stress during online exams’, in Onieva, E., Santos, I., Osaba, E., et al (Eds.): ‘Hybrid artificial intelligent systems’ (Springer International Publishing, Cham, 2015), pp. 345356.
    19. 19)
      • 19. Guo, Q., Agichtein, E.: ‘Towards predicting web searcher gaze position from mouse movements’. CHI'10 Extended Abstracts on Human Factors in Computing Systems, 2010, pp. 36013606.
    20. 20)
      • 20. Antal, M., Szabo, L.Z.: ‘An evaluation of one-class and two-class classification algorithms for keystroke dynamics authentication on mobile devices’. 2015 20th Int. Conf. on Control Systems and Computer Science, 2015, pp. 343350.
    21. 21)
      • 21. Hall, M., Frank, E., Holmes, G., et al: ‘The weka data mining software: an update’, SIGKDD Explor. Newsl., 2009, 11, (1), pp. 1018.
    22. 22)
      • 22. Breiman, L.: ‘Random forests’, Mach. Learn., 2001, 45, (1), pp. 532.
    23. 23)
      • 23. Fawcett, T.: ‘An introduction to ROC analysis’, Pattern Recognit. Lett., 2006, 27, (8), pp. 861874.
    24. 24)
      • 24. Mondal, S., Bours, P.: ‘Performance evaluation of continuous authentication systems’, IET Biometrics, 2015, 4, (4), pp. 220226.

Related content

This is a required field
Please enter a valid email address