Datasets for bot detection using mouse behavior
DOI: 10.21293/1818-0442-2024-27-3-118-124
Abstract: Bot detection based on the dynamic characteristics of computer mouse cursor movement is considered. The basic dynamic characteristics and 150 additional ones obtained on their basis are presented. Publicly available datasets used to detect bots based on mouse cursor movement characteristics are studied. Selfcreated BOT1 and BOT2 datasets used for system training are described. To confirm the applicability of these data sets, the dimensionality reduction and the distribution comparison of key dynamic characteristics of the sets are performed.
Keywords: bot, malicious bot, mouse movement, track, mouse dynamics datasets
Funding: This work was supported by the Ministry of Digital Development, Communications, and Mass Media of the Russian Federation, Agreement No. 40469-07/23-K dated June 30, 2023.
For citation:
Afanasieva N. S., Lozhnikov P. S. Datasets for bot detection using mouse behavior. Doklady Tomskogo gosudarstvennogo universiteta sistem upravleniya i radioelektroniki, 2024, vol. 27, no. 3, pp. 118–124. DOI: 10.21293/1818-0442-2024-27-3-118-124
Authors and copyright holders:
- Afanasieva N. S. , Omsk State Transport University (Omsk, Russia)
- Lozhnikov P. S. , Omsk State Transport University (Omsk, Russia)
- 1. Dunham K., Melnick, J. Malicious bots: an inside look into the cyber-criminal underground of the internet. Boca Raton, CRC Press, 2008, 168 p.
- 2. Suchacka G., Iwański J. Identifying legitimate Web users and bots with different traffic profiles – an Information Bottleneck approach. Knowledge-Based Systems, 2020, no. 197. Available at: https://doi.org/10.1016/j.knosys.2020.105875, free (Accessed: June 10, 2024).
- 3. What are bots. Available at: https://www.kaspersky.ru/resource-center/definitions/what-are-bots, free (Accessed 2 July 2024).
- 4. Xiao G. Bad Bots: Regulating the Scraping of Public Personal Information. Cambridge, Harv. JL & Tech, 2020, 701 p.
- 5. Bondy M. Bad Bots. The Project on International Peace and Security, Institute for the. Theory and Practice of International Relations, 2017, pp. 2016–2017.
- 6. Loginova A.O. Analiz sushchestvuyushchih podhodov k klassifikacii i tipologii botov [The analysis of existing approaches to bots classification and typology]. Innovative Technologies: Theory, Tools, Practice, 2020, vol. 1, pp. 462–467 (in Russ.).
- 7. Loginova A.O. Opredelenie atributov sobytij informacionnoj bezopasnosti svyazannyh s aktivnostyu internet botov [Defining information security events attributes related to the activity of internet bots]. Electronic Systems and Technologies. Proceedings of the 58th Scientific Conference of Postgraduate, Graduate and Undergraduate Students of Belarusian State University of Informatics and Radioelectronics, 2022, pp. 105–110 (in Russ.).
- 8. Geer D. Malicious bots threaten network security. Computer, 2005, vol. 38, no. 1, pp. 18–20.
- 9. Kolomeets M., Chechulin A. Analysis of the malicious bots market. IEEE 29th Conference of Open Innovations Association (FRUCT), 2021, pp. 199–205. Available at: https://www.researchgate.net/publication/351855704_Analysis_of_the_Malicious_Bots_Market, free (Accessed: June 13, 2024).
- 10. 2023 Imperva Bad Bot Report. Available at: https://www.imperva.com/resources/resource-library/reports/2023-imperva-bad-bot-report/, free (Accessed: June 12, 2024).
- 11. OWASP Automated Threats to Web Applications. Available at: https://owasp.org/www-project-automated-threats-to-web-applications/, free (Accessed: June 20, 2024).
- 12. Suchacka G., Cabri A., Rovetta S., Masulli F. Efficient on-the-fly Web bot detection. Knowledge-Based Systems, 2021, vol. 223. Available at: https://www.sciencedirect.com/science/article/pii/S0950705121003373, free (Accessed: June 20, 2024).
- 13. Gamboa H., Fred A. A behavioral biometric system based on human-computer interaction. Biometric Technology for Human Identification, SPIE, 2004, vol. 5404, pp. 381–392.
- 14. Afanaseva N.S., Lozhnikov P.S. Bot Detection Using Mouse Movements. 2023 Dynamics of Systems, Mechanisms and Machines (Dynamics), IEEE, 2023, pp. 1–4.
- 15. See A., Wingarz T., Radloff M., Fischer M. Detecting Web Bots via Mouse Dynamics and Communication Metadata. IFIP International Conference on ICT Systems Security and Privacy Protection, 2023, pp. 73–86.
- 16. Antal M., Egyed‐Zsigmond E. Intrusion detection using mouse dynamics. IET Biometrics, 2019, vol. 8, no. 5, pp. 285–294.
- 17. Khan S., Devlen C., Manno M., Hou D. Mouse dynamics behavioral biometrics: A survey. ACM Computing Surveys, 2024, vol. 56, no. 6, pp. 1–33.
- 18. Antal M., Fejér N. Mouse dynamics based user recognition using deep learning. Acta Universitatis Sapientiae, Informatica, 2020, vol. 12, no. 1, pp. 39–50.
- 19. Kılıç A.A., Yıldırım M., Anarım E. Bogazici mouse dynamics dataset. Data in Brief, 2021, vol. 36, p. 107094.
- 20. Maćkiewicz, A., Waldemar R. Principal components analysis (PCA). Computers & Geosciences, 1993, vol. 19, no. 3, pp. 303–342.
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