Neural-Like Means for Data Streams Encryption and Decryption in Real Time

Tsmots, Ivan; Tsymbal, Yurii; Khavalko, Viktor; Skorokhoda, Oleksa; Tesluyk, Taras

doi:10.1109/dsmp.2018.8478513

Cited by 14 publications

(10 citation statements)

References 11 publications

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“…The synthesis of data acquisition, exchange and storage module (DAESM) is proposed to carry out on the basis of the method for time allocation of memory resources between external devices (workstations, microprocessor systems, intelligent sensors and actuators), which are connected to DAESM via industrial network interfaces [46]. According to this method, each j-th device, cyclically with a given period T j , is given a fixed time interval to memory access.…”

Section: The Methods For Conflict-free Data Exchangementioning

confidence: 99%

“…Thus, the developed methodology of IT for data acquisition and processing in EEMIS includes new and improved methods and models [45][46][47][48][49][50][51][52][53][54][55]: the method for synthesis of data acquisition and processing tools [41], the method for conflict-free exchange between data acquisition and processing tools in EEMIS [46], the method for intelligent tools implementation based on artificial neural networks [47] and models for analysis of structure dynamics of the synthesized tools of data acquisition and processing based on simple, colored and hierarchical Petri nets [48].…”

Section: The Structure Of Information Technology For Data Acquisition and Processing In The Eemismentioning

confidence: 99%

See 1 more Smart Citation

Methods for the Efficient Energy Management in a Smart Mini Greenhouse

Teslyuk¹,

Tsmots²,

ml.³

et al. 2022

Computers, Materials &Amp; Continua

Self Cite

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To solve the problem of energy efficiency of modern enterprise it is necessary to reduce energy consumption. One of the possible ways is proposed in this research. A multi-level hierarchical system for energy efficiency management of the enterprise is designed, it is based on the modular principle providing rapid modernization. The novelty of the work is the development of new and improvement of the existing methods and models, in particular: 1) models for dynamic analysis of IT tools for data acquisition and processing (DAAP) in multilevel energy management systems, which are based on Petri nets; 2) method of synthesis of DAAP tools in energy efficiency management information systems (EEMIS) of the enterprise which provides a reduction in hardware and time costs from 10% to 40%; 3) method of conflict-free data exchange determining the minimum memory speed for the synthesis of realtime exchanges, it reduces the cost and energy consumption; 4) method of calculating the signal of postsynaptic excitation of neural elements decreases the processing time of technological data two or more times. The proposed methods, models and tools have been tested while implementing the EEMIS of the intelligent mini-greenhouse, as a result, energy efficiency has increased by 12%-25% (depending on season and peculiarities of growing plants).

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Section: The Methods For Conflict-free Data Exchangementioning

confidence: 99%

Section: The Structure Of Information Technology For Data Acquisition and Processing In The Eemismentioning

confidence: 99%

Methods for the Efficient Energy Management in a Smart Mini Greenhouse

Teslyuk¹,

Tsmots²,

ml.³

et al. 2022

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

show abstract

“…The study of the main trends in the area of UAV onboard systems development for real-time cryptographic data protection shows that neural network methods are increasingly used for performing data encryption and decryption in such systems [28][29][30][31][32]. These publications show that the implementation of neural network methods of cryptographic data protection is performed generally by software.…”

Section: Related Workmentioning

confidence: 99%

Neural Network Technology for Cryptographic Protection of Data Transmission at UAV

Tsmots¹,

Teslyuk²,

Łukaszewicz³

et al. 2023

Preprint

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The neural network technology for real-time cryptographic data protection with symmetric keys (masking codes, neural network architecture and weights matrix) for unmanned aerial vehicles (UAV) onboard communication systems has been developed. It provides hardware and software implementation with high technical and operational characteristics. The development of neural network technology for real-time cryptographic data protection was performed using an integrated approach based on theoretical foundations of neural network cryptographic data protection, new algorithms and structures of neural network data encryption and decryption, modern element base with the possibility of programming for the structure, and computer-aided design of hardware and software tools. The development and implementation of the on-board system of neural network cryptographic data protection in real-time are based on the following principles: variable composition of equipment; modularity; conveyorization and spatial parallelism; specialization and adaptation of hardware and software to data encryption and decryption. The tabular-algorithmic method of calculating the scalar product has been improved, it provides fast calculation of the scalar product of input data for both fixed and floating-point by bringing to the largest common order of weights and forming tables of macro-partial products for them. Components of neural network cryptographic data encryption and decryption have been developed on the processor core supplemented by specialized scalar product calculation modules. The specialized hardware for neural network cryptographic data encryption was developed using VHDL for equipment programming in the Quartus II development environment ver. 13.1 and the appropriate libraries, and implemented on the basis of the FPGA EP3C16F484C6 Cyclone III family.

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“…The SGTM neural-like structure is also used to build encryption systems and data transmission of increased cryptocurrency [24]. To increase the speed of operation in the application mode and illustrate this procedure, [10] proposes a transition diagram from the neural network form to the polynomial one of SGTM neural-like structure application.…”

Section: Introductionmentioning

confidence: 99%

An Integral Software Solution of the SGTM Neural-Like Structures Implementation for Solving Different Data Mining Tasks

Tkachenko

2021

Lecture Notes on Data Engineering and Communications Technologies

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The paper presents a developed software solution that implements a new learning model and application of artificial neural networks, i.e. the Successive Geometric Transformations Model, to solve various applied data mining tasks. This model is applied to construct Feed Forward Neural Networks, which anticipates the rejection of training goal interpretation as a multi-extremal optimization procedure. The modes of use of the system developed (prediction, time series, and cascade modes) are described in detail and illustrated, as well as the structure and functions of separate constituents of the graphical user interface; data selection and download procedures; neuro-like structure parameterization capabilities for each different mode of operation of the program. Simulation of the developed integral software solution was performed while fulfilling prediction, classification, and time series forecasting. Besides, the feasibility of performing nonlinear PCA analysis at high speed is shown. When performing all the above tasks, the program outcomes are presented in both numerical form and using the appropriate one for a particular mode of visualization in 2D or 3D formats.

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Neural-Like Means for Data Streams Encryption and Decryption in Real Time

Cited by 14 publications

References 11 publications

Methods for the Efficient Energy Management in a Smart Mini Greenhouse

Methods for the Efficient Energy Management in a Smart Mini Greenhouse

Neural Network Technology for Cryptographic Protection of Data Transmission at UAV

An Integral Software Solution of the SGTM Neural-Like Structures Implementation for Solving Different Data Mining Tasks

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