Prediction of cyber attacks with historical data-based forecasting system and Fuzzy logic-based risk estimation

Authors

  • Brúnó Krasnyánszki Óbudai Egyetem
  • Zlatko Čović Szabadkai Műszaki Szakfőiskola
  • Zoltán Rajnai Óbudai Egyetem

Keywords:

AI, EuRepoC, prediction, cyber attack forecasting

Abstract

In our research paper we used the EuRepoC’s database to make predictions for the future cyberattacks. We used neural networks, support vector machine modells and Fine Tree algorythm. We are also developing a Fuzzy based risk analyzer for state sponsored cyber attacks. Our research is continuing so there are only partial results in the paper. 

References

https://eurepoc.eu/about-us/ Szerző által fordított

Murphy, Kevin P.. “Machine learning - a probabilistic perspective.” Adaptive computation and machine learning series (2012).

J. M. Mendel, "Fuzzy logic systems for engineering: a tutorial," in Proceedings of the IEEE, vol. 83, no. 3, pp. 345-377, March 1995, doi: 10.1109/5.364485.

Omidvar, M., Zarei, E., Ramavandi, B., Yazdi, M. (2022). Fuzzy Bow-Tie Analysis: Concepts, Review, and Application. In: Yazdi, M. (eds) Linguistic Methods Under Fuzzy Information in System Safety and Reliability Analysis. Studies in Fuzziness and Soft Computing, vol 414. Springer, Cham. https://doi.org/10.1007/978-3-030-93352-4_3

https://eurepoc.eu/database/

European Repository of Cyber Incidents (EuRepoC) (2024) “Global Dataset of Cyber Incidents V.1.2”. doi: 10.5281/zenodo.11108195.

Montgomery, D.C., Peck, E.A., & Vining, G.G. (2012). Introduction to Linear Regression Analysis. John Wiley & Sons.

Weisberg, S. (2005). Applied Linear Regression. John Wiley & Sons.

Kutner, M.H., Nachtsheim, C.J., & Neter, J. (2004). Applied Linear Regression Models. McGraw-Hill Irwin.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.

Bishop, C.M. (2006). Pattern Recognition and Machine Learning. Springer.

https://www.researchgate.net/figure/A-sample-neural-network-layout-with-three-hidden-layers_fig5_368540750

Dr. Kutor László előadása OE NIK 2023 https://elearning.uni-obuda.hu/main/pluginfile.php/1132079/mod_resource/content/0/IS%202023-2-2.pdf

Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297.

Downloads

Published

2024-10-25

Issue

Vol. 6 No. 2 (2024): Bánki Közlemények/Bánki Reports Autumn (2024)

Section

Technical Informatics (Műszaki Informatika)

License

Copyright (c) 2024 Bánki Közlemények (Bánki Reports)

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Bánki Közlemények is loyal to open access for academic work. All the original articles and review papers published in this journal are free to access immediately from the date of publication. We don’t charge any fees for any reader to download articles and reviews for their own scholarly use.

The Bánki Közlemények also operates under the Creative Commons Licence CC-BY-NC-ND. This allows for the reproduction of articles, free of charge, for non-commercial use only and with the appropriate citation information. All authors publishing with the Bánki Közlemények accept these as the terms of publication.