Hänsel und Gretel: algoritmo para melhoria da resiliência cibernética pela diversificação de ativos através de aprendizado de máquina
Abstract
Cybersecurity is crucial in all sectors of modern society due to the constant emergence of new threats. In this context, asset diversification is a valuable tool to limit or even prevent the spread of malware. This work presents an ongoing research for the development of an approach that aims to improve the cyber resilience of an industrial system by diversifying network resources using machine learning to find critical paths and safer alternatives.References
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Beineke, L. W. and Bagga, J. S. (2021). Line graphs and line digraphs. Springer.
Choi, S.-S., Cha, S.-H., Tappert, C. C., et al. (2010). A survey of binary similarity and distance measures. Journal of systemics, cybernetics and informatics, 8(1):43–48.
Ellis, J. E., Parker, T. W., Vandekerckhove, J., Murphy, B. J., Smith, S., Kott, A., and Weisman, M. J. (2022). An experimentation infrastructure for quantitative measurements of cyber resilience. In MILCOM 2022-2022 IEEE Military Communications Conference (MILCOM), pages 855–860. IEEE.
Li, T., Feng, C., and Hankin, C. (2018). Improving ics cyber resilience through optimal diversification of network resources. arXiv preprint arXiv:1811.00142.
Lin, C.-T., Wu, S.-L., and Lee, M.-L. (2017). Cyber attack and defense on industry control systems. In 2017 IEEE Conference on Dependable and Secure Computing, pages 524–526. IEEE.
Prieto, Y., Figueroa, M., and Pezoa, J. E. (2021). Maximizing network reliability to 0-day exploits through a heterogeneous node migration strategy. IEEE Access, 9:97747–97759.
Raj Samani (2021). McAfee labs threats report. Technical report, McAfee.
Russell, S. J. and Norvig, P. (2004). Inteligência artificial. Elsevier.
Zhang, Q., Cho, J.-H., Moore, T. J., and Nelson, F. F. (2021). Drevan: Deep reinforcement learning-based vulnerability-aware network adaptations for resilient networks. In 2021 IEEE Conference on Communications and Network Security (CNS), volume ., pages 137–145. IEEE.
Published
2023-09-18
How to Cite
ALMEIDA, Fernando Nunes de; CUNHA, Antonio Eduardo Carrilho da; SANTOS, Anderson Fernandes Pereira dos; PELLANDA, Paulo César.
Hänsel und Gretel: algoritmo para melhoria da resiliência cibernética pela diversificação de ativos através de aprendizado de máquina. In: BRAZILIAN SYMPOSIUM ON CYBERSECURITY (SBSEG), 23. , 2023, Juiz de Fora/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 546-551.
DOI: https://doi.org/10.5753/sbseg.2023.233085.
