Ab s t r a c t. Based on the analysis of a group of two-bit two-operand operations of strict stable cryptographic coding, the relations between direct and inverse operations are established and formalized and their correctness is proved. Applying the technology of combining single-operand strict rigorous cryptographic coding into two-operand operations and using established interconnections, we propose a method of synthesis of inverse operations for known direct operations. This method provides the construction of the inverse operation by converting the second operand of two-bit two-operand operations of strict stable cryptographic coding. The article examines the entire sequence of mathematical transformations that provides the synthesis of a formalized operation model, suitable for practical application in crypto primitives, by constructing models of the relationships between operations and the synthesis of a reverse operation model. The synthesized operations are implemented both at the software and hardware levels and provide the ease of achieving the effect of strictly stable cryptographic coding. K e ywor d s : cryptographic coding; decoding; inverse operations; cryptocurrencies; permutations; encryption reliability; strict stable cryptographic coding; operation synthesis; second operand. Rudnitsky V., Berdibayev R., Breus R., Lada N., Pustovit M., 2019
The introduction of fifth generation mobile networks is underway all over the world which makes many people think about the security of the network from any hacking. Over the past few years, researchers from around the world have raised this issue intensively as new technologies seek to integrate into many areas of business and human infrastructure. This paper proposes to implement an IDS (Intrusion Detection System) machine learning approach into the 5G core architecture to serve as part of the security architecture. This paper gives a brief overview of intrusion detection datasets and compares machine learning and deep learning algorithms for intrusion detection. The models are built on the basis of two network data CICIDS2017 and CSE-CIC-IDS-2018. After testing, the ML and DL models are compared to find the best fit with a high level of accuracy. Gradient Boost emerged as the top method when we compared the best results based on metrics, displaying 99.3% for a secure dataset and 96.4% for attacks on the test set.
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