ML-BASED PREDICTING PRNG OUTPUT WITH SEQUENTIAL ANALYSIS
Автор: Dmytro Proskurin, Maksim Iavich, Sergiy Gnatyuk, Tetiana Okhrimenko
Организация: State University “Kyiv Aviation Institute”, Caucasus University
Категория:
Ключевые слова: Random numbers, Neural Networks, AI, Hybrid model, PRNG, cybersecurity, critical information infrastructure
Аннотация. This research goes into the predictive capabilities of neural network models, mainly focusing on recurrent neural networks (RNNs) and long-term short-term memory networks (LSTMs), and their combination in a hybrid architecture to predict the outcomes of various pseudo-random number generators (PRNGs). In this work Continuous-Output Scenario Analysis is shown.
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Maksim Iavich, Tamari Kuchukhidze, Giorgi Iashvili, Sergiy Gnatyuk Hybrid quantum random number generator for cryptographic algorithms. RADIOELECTRONIC AND COMPUTER SYSTEMS, 4, 2021. DOI: https://doi.org/10.32620/reks.2021.4.09
Maksim Iavich, Tamari Kuchukhidze, Giorgi Iashvili, Sergiy Gnatyuk, Razvan Bocu," Novel Quantum Random Number Generator with the Improved Certification Method ", International Journal of Mathematical Sciences and Computing(IJMSC), Vol.7, No.3, pp. 41-53, 2021. DOI: 10.5815/ijmsc.2021.03.05
Maksim Iavich, Tamari Kuchukhidze, Giorgi Iashvili, Sergiy Gnatyuk, Razvan Bocu," Novel Quantum Random Number Generator with the Improved Certification Method ", International Journal of Mathematical Sciences and Computing (IJMSC), Vol.7, No.3, pp. 41-53, 2021. DOI: 10.5815/ijmsc.2021.03.05
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