ABSTRACT
For the purpose of this study, a dataset is collected from ransomware attacks database, for a six-year period, starting from 2018 to present. The numbers of ransomware attacks for every month and year are counted and visualized in the form of sums. Different linear and nonlinear statistical models are tested and the most reliable trend lines are selected according to criteria of the highest R-squared value (R2). Trend lines for each of the observed years follow the sextic polynomial curve, with the values of R2 from 0.6910 to 0.9646. The analysis of the number of attacked sub-industries is conducted and percentage share by sub-industries for each year is calculated. The results are presented separately in the form of graphs for each year, as well as for the six-year period, indicating that the most affected were healthcare, government, and education. The attitude regarding the appearance and trends in frequency of different ransomware strains through the years is also observed. There is no regularity, but it is possible to draw some other trends, with respect to the number of recorded strains. The results of this study represent a base for future predictions for the number of attacks and ransomware strains.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Oosthoek, K., Cable, J., Smaragdakis, G.: A tale of two markets: investigating the ransomware payments economy. Commun. ACM 66(8), 74–83 (2023). https://doi.org/10.1145/3582489
Joint Cybersecurity Advisory: 2021 Top Malware Strains, White paper (2022)
August, T., Dao, D., Niculesku, M.F.: Economics of ransomware: risk interdependence and large-scale attacks. Manage. Sci. 68(12), 8979–9002 (2022). https://doi.org/10.1287/mnsc.2022.4300
Anghel, M., Racautanu, A.: A note on different types of ransomware attacks. IACR Cryptol ePrint Arch. 2019, 605 (2019)
Davies, S., Macfarlane, R., Buchanan, W.: Differential area analysis for ransomware attack detection within mixed file datasets. Comput. Secur. 108, 102377 (2021). https://doi.org/10.1016/j.cose.2021.102377
Skertic, J.: Cybersecurity Legislation and Ransomware Attacks in the United States. 2015–2019 Doctor of Philosophy (PhD), Dissertation, Political Science & Geography, Old Dominion University. (2021). https://doi.org/10.25777/c0vq-t159. https://digitalcommons.odu.edu/gpis_etds/134
Atapour-Abarghouei, A., Bonner, S., McGough, A.S.: Volenti non fit injuria: ransomware and its victims. In: Proceedings of the 2019 IEEE International Conference on Big Data (Big Data), pp. 4701–4707. USA, CA, Los Angeles (2019). https://doi.org/10.1109/BigData47090.2019.9006298
Oz, H., Aris, A., Levi, A., and A. Selcuk Uluagac, A.S.: A survey on ransomware: evolution, taxonomy, and defense solutions. ACM Comput. Surv. 54(11s), 1–37. (2022). https://doi.org/10.1145/3514229
Sgandurra, D., Muñoz-González, L., Mohsen, R., and Lupu, E.C.: Automated Dynamic Analysis of Ransomware: Benefits, Limitations and use for Detection, arXiv:1609.03020[cs.CR] (2016). https://doi.org/10.48550/arXiv.1609.03020
Beamana, C., Barkwortha, A., Akande, T.D., Hakak, S., Khanb, M.K.: Ransomware: recent advances, analysis, challenges and future research directions. Comput. Secur. 111, 102490 (2021). https://doi.org/10.1016/j.cose.2021.102490
Razaulla, S., et al.: The age of ransomware: a survey on the evolution, taxonomy, and research directions. IEEE Access 11, 40698–40723 (2023)
The Ultimate Guide to Ransomware, Egnyte Inc. (2021). https://www.egnyte.com/sites/default/files/2021-01/Egnyte_Ransomeware_White%20 Paper_Ultimate_Guide_1.pdf
Berrueta, E., Morato, D., Magaña, E., Izal, M.: Crypto-ransomware detection using machine learning models in file-sharing network scenarios with encrypted traffic. Expert Syst. Appl. 209, 118299 (2022). https://doi.org/10.1016/j.eswa.2022.118299
Sonicwall cyber threat report, Sonicwall (2023)
Du, J., Raza, S.H., Ahmad, M., Alam, I., Dar, S.H., Habib, M.A.: Digital forensics as advanced ransomware pre-attack detection algorithm for endpoint data protection. Secur. Commun. Networks 2022. Article ID 1424638: 16 pages. (2022). https://doi.org/10.1155/2022/1424638
Tertereanu, P.: Cybercrimes Ways to Pick up Electronic Devices. In: Karabegović, I., Kovačević, A., Mandžuka, S. (eds) New Technologies, Development and Application V. NT 2022. Lecture Notes in Networks and Systems, vol 472. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-05230-9_61
Blom, T., Sahebali, W., Deppe, K., Romijn, P., Donath, F., Brennenraedts, R.: Ransomware attacks on organizations and companies in the Netherlands, Management summary (EN) Commisioned by: Wetenschappelijk Onderzoek-en Documentatiecentrum (WODC), Publicationnumber: 2022.173–2319-MSEN, Date: Utrecht, 1-8-2023
Šendelj, R., Ognjanović, I.: Cyber security capacity building planning within organisations. In: Karabegović, I. (eds) New Technologies, Development and Application. NT 2018. Lecture Notes in Networks and Systems, vol 42. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-90893-9_27
Yilmaz, Y., Cetin, O., Grigore, C., Arief, B., Hernandez-Castro, J.: Personality types and ransomware victimisation. Digital Threats: Res. Pract. 4(4), 1–25. (2023). https://doi.org/10.1145/3568994
Kalaimannan, E., Sharon K., John, S.K., DuBose, T., Pinto, A.: Influences on ransomware’s evolution and predictions for the future challenges. J. Cyber Secur. Technol. 1(1), 23-31. (2017). https://doi.org/10.1080/23742917.2016.1252191
Rebecca, M.: Map of worldwide ransomware attacks (updated daily). Comparitech (2023). https://www.comparitech.com/blog/information-security/global-ransomware-attacks/
Tufegdzic, M., Arsic, S., and Trajanovic, M.: Parameter-based morphometrу of the wing of ilium. J. Anat. Soc. India 64(2), 129–135 (2015). https://doi.org/10.1016/j.jasi.2015.10.008
Dašić, P., Dašić, J., Antanasković, D., Pavićević, N.: Statistical Analysis and Modeling of Global Innovation Index (GII) of Serbia. In: Karabegović, I. (eds) New Technologies, Development and Application III. NT 2020. Lecture Notes in Networks and Systems, vol 128. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-46817-0_59
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Tufegdžić, M., Mišković, A., Dašić, P., Nedić, V. (2024). Statistical Modeling of Ransomware Attacks Trends. In: Karabegovic, I., Kovačević, A., Mandzuka, S. (eds) New Technologies, Development and Application VII. NT 2024. Lecture Notes in Networks and Systems, vol 1070. Springer, Cham. https://doi.org/10.1007/978-3-031-66271-3_10
Download citation
DOI: https://doi.org/10.1007/978-3-031-66271-3_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-66270-6
Online ISBN: 978-3-031-66271-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)