Cryptanalysis of Telemetry Data Using Machine Learning and Deep Learning 


Vol. 48,  No. 8, pp. 992-1000, Aug.  2023
10.7840/kics.2023.48.8.992


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  Abstract

With the development of ICT technology, more data is being exchanged in the defense and aerospace fields. Cryptographic algorithms are being used to protect data and systems from hacking and ransomware, and the complexity of cryptographic modules is also greatly increased by using different cryptographic algorithms or encryption keys for each business. Therefore, through the comparison of cryptographic performance of machine learning and deep learning algorithms, we want to select algorithms to be introduced into systems that detect ransomware and increase cryptographic module management efficiency. Performance comparisons show that machine learning algorithms generally show high accuracy when the number of data frames is small, but the accuracy decreases as the number of data frames increases It took a long time to learn. For deep learning, CNN was selected as the most suitable algorithm. Because it complied with accuracy and learning time and showed that performance did not decrease significantly even if the number of data frame increased.

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[IEEE Style]

J. Ahn, J. Kim, T. Yi, "Cryptanalysis of Telemetry Data Using Machine Learning and Deep Learning," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 8, pp. 992-1000, 2023. DOI: 10.7840/kics.2023.48.8.992.

[ACM Style]

Joo-eon Ahn, Ji-eun Kim, and Taek-joon Yi. 2023. Cryptanalysis of Telemetry Data Using Machine Learning and Deep Learning. The Journal of Korean Institute of Communications and Information Sciences, 48, 8, (2023), 992-1000. DOI: 10.7840/kics.2023.48.8.992.

[KICS Style]

Joo-eon Ahn, Ji-eun Kim, Taek-joon Yi, "Cryptanalysis of Telemetry Data Using Machine Learning and Deep Learning," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 8, pp. 992-1000, 8. 2023. (https://doi.org/10.7840/kics.2023.48.8.992)
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