Research Article Appliance of Neuron Networks in Cryptographic Systems

Abstract: This study is dedicated to the examination of a problem of postquantum encryption algorithms which are connected with a potential crisis in modern cryptography that is caused by appearance of quantum computers. General problem formulation is given as well as an example of danger from the quantum algorithms against classical cryptosystems. Existing postquantum systems are analyzed and the complication of their realization and cryptosecurity are estimated. Among the others algorithms on the basis of neural networks are chosen as a starting point. The study demonstrates neuro cryptographic protocol based on a three-level neural network of the direct propagation. There was evaluated it's cryptosecurity and analyzed three types of this algorithm attack to show the reality of the hypothesis that neuro cryptography is currently one of the most promising post quantum cryptographic systems

Brain epilepsy seizure detection using bio-inspired krill herd and artificial alga optimized neural network approaches

© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. Nowadays, Epilepsy is one of the chronic severe neurological diseases; it has been identified with the help of brain signal analysis. The brain signals are recorded with the help of electrocorticography (ECoG), Electroencephalogram (EEG). From the brain signal, the abnormal brain functions are a more challenging task. The traditional systems are consuming more time to predict unusual brain patterns. Therefore, in this paper, effective bio-inspired machine learning techniques are utilized to predict the epilepsy seizure from the EEG signal with maximum recognition accuracy. Initially, patient brain images are collected by placing the electrodes on their scalp. From the brain signal, different features are extracted that are analyzed with the help of the Krill Herd algorithm for selecting the best features. The selected features are processed using an artificial alga optimized general Adversarial Networks. The network recognizes the intricate and abnormal seizure patterns. Then the discussed state-of-art methods are examined simulation results

DL Multi-sensor information fusion service selective information scheme for improving the Internet of Things based user responses

Multi-sensor information fusion aids different services to meet the application requirements through independent and joint data assimilation. The role of multiple sensors in smart connected applications helps to improve their efficiency regardless of the users. However, the assimilation of different information is subject to resource and time constraints at the time of application response. This results in partial fulfillment of the application services, and hence, this article introduces a service selective information fusion processing (SSIFP) scheme. The proposed scheme identifies service-specific sensor information for satisfying the application service demands. The identification process is eased with deep recurrent learning in determining the level of sensor information fusion. This level identification reduces the unavailability of services (resource constraint) and delays in application services (time constraint). Through this identification, the applications\u27 precise demands are detected, and selective fusion is performed to mitigate the issues above. The proposed system\u27s performance is verified using the metrics delay, fusion rate, service loss, and backlogs

Information Technology for Maximizing Energy Consumption for Useful Information Traffic in a Dense Wi-Fi 6/6E Ecosystem

In Wi-Fi standards, a relatively narrow range of frequency spectrums is declared as working, on the operation of which additional restrictions are imposed in different countries. When creating dense wireless network ecosystems focused on massive information traffic, this circumstance causes significant interference even in the case of using Wi-Fi 6/6E-compatible equipment. An effective solution to this problem is the implementation of a centralized management mechanism for the relevant parameters of the target network ecosystem. The growing attention to ecology and rational use of electricity makes the problem of maximizing energy consumption for useful information traffic in a dense Wi-Fi 6/6E ecosystem an urgent task. Only the addressed information traffic between the transmitter and the target subscriber, which are subjects of the OFDMA technology and the MU-MIMO multiple access system (with an emphasis on the latter), is considered useful. To solve the problem, the authors formalized the Wi-Fi 6/6E ecosystem’s energy consumption model, which takes into account the specifics of OFDMA and MU-MIMO, the influence of the communication channel characteristics on the speed of target information transfer, and detailed energy consumption for maintaining the network infrastructure in a functional state. Based on the created model, the research problem is represented by the difference between two monotonic functions, relative to which the problem of optimization with restrictions is set. The process of solving this problem is presented in the form of information technology with a branch-and-bound hierarchy and a nested unconditional optimization problem. The results of simulated modelling in the MATLAB-NS3 environment showed a significant advantage of the authors’ approach. The energy power consumption by the Wi-Fi 6/6E ecosystem, the parameters of which were adjusted with the help of the authors’ information technology, decreased by more than four times

Entropy-metric estimation of the small data models with stochastic parameters

The formalization of dependencies between datasets, taking into account specific hypotheses about data properties, is a constantly relevant task, which is especially acute when it comes to small data. The aim of the study is to formalize the procedure for calculating optimal estimates of probability density functions of parameters of linear and nonlinear dynamic and static small data models, created taking into account specific hypotheses regarding the properties of the studied object. The research methodology includes probability theory and mathematical statistics, information theory, evaluation theory, and stochastic mathematical programming methods. The mathematical apparatus presented in the article is based on the principle of maximization of information entropy on sets determined as a result of a small number of censored measurements of “input'' and “output'' entities in the presence of noise. These data structures became the basis for the formalization of linear and nonlinear dynamic and static models of small data with stochastic parameters, which include both controlled and noise-oriented input and output measurement entities. For all variants of the above-mentioned small data models, the tasks of determining the optimal estimates of the probability density functions of the parameters were carried out. Formulated optimization problems are reduced to the forms canonical for the stochastic linear programming problem with probabilistic constraints

Intelligent traffic engineering in software-defined vehicular networking based on multi-path routing

© 2013 IEEE. This paper addresses traffic engineering (TE) issues in software-defined vehicular networking (SDVN). A brief analysis of the features of SDVN, which improves the efficiency of TE in SDVN, is presented. The feasibility of using multi-path routing with TE is substantiated. A procedure and an example of the formation of multiple routes based on a modified wave routing algorithm are given. Considering the features of the SDVN technology, a modified TE method is proposed, which reduces both the time complexity of forming multiple paths and the path reconfiguration time. The dynamic path reconfiguration algorithm is presented