The Industry 5.0 revolution has launched a new age of intelligent manufacturing equipment, which is crucial to our society and economy. Industry 5.0 aims to enhance human potential by integrating cutting-edge IT technology, Artificial Intelligence (AI), the Internet of Things (IoT), robots, and augmented reality into everyday living, especially in smart industrial settings, to maximize human potential. Smart Production Processes (SP2), accessible standards, and resource sharing on the web have increased network vulnerabilities as enterprises adopt them. These vulnerabilities primarily target industry IoT systems, threatening private data. Existing system issues include network vulnerabilities, production capability, and data management. Traditional applications struggle with industrial data’s bulk and complexity, causing data analysis, security, and privacy challenges. Modern industrial systems use AI and ML for big data analysis and data processing to overcome these issues. Optimizing human-machine synergy minimizes costs and equipment maintenance and boosts efficiency. An ML-assisted Smart Production Process (ML-SP2) and Intrusion Detection System (ML-IDS) have been proposed to assess and address Industrial 5.0 concerns. Data capture, predictive maintenance and optimization, transparent decision-making, and proactive maintenance are integrated into the manufacturing process with the ML-SP2. The ML-IDS detects network vulnerabilities using ensemble methods and manages distribution efficiently. The ML-IDS uses the variety of ML techniques such as random forest, decision tree and support vector machines that identifies the intruder with maximum prediction accuracy. In addition, the intruder activities are observed with the help of the convolution networks that improve the overall intruder activities recognition. The smart industrial production phase’s effectiveness is measured by the RPL (0.9), maintenance cost (2500$), production performance (1.0), VDR (97.6%), FAR, accuracy (99%), precision (94%)-recall (96%), and f1-measure (98%) compared to the existing methods such as ML-RF-FLID, NRDD-DBSCAN, and OC-SVM.

The stratified approach to software defined networks simulation has been proposed. It is based on Discrete Event System Specification formalism, atomic and coupled models concepts usage. The approach is aimed at simulation within the Windows environment, with an accent on the easiness of model reconfiguration. The proposed approach is also devoted to simulation-related overheads decrease. The atomic models of active (controller, switch, host) and passive (link) network components have been proposed. The coupled model of a software defined network comprising atomic models of active and passive components has been proposed. The estimations of the resulting coupled model complexity, with respect to the number of components basic atomic models, have been given. During experimentation, the pingall command usage scenario has been considered. For this purpose, the emulation via Mininet environment and the simulation on a basis of the proposed approach have been conducted. It has been shown that discrete-event simulation on a basis of the proposed approach is significantly less time-consuming. During the approach usage within the Windows environment, the absence of the need to utilize the Xming X Server and PuTTY utility for the purpose of visualization has been faced. The validity of the approach has been proven on a basis of the obtained experimental data. The adequacy of the resulting coupled simulation model of the network has been proven with t-criterion. The proposed approach can be used for the purpose of software defined networks validation with an accent on non-functional properties

This paper presents the investigation and comparison of TLC model checking method (TLA Checker) properties. There are two different approaches to method usage which are considered. The first one consists of a transition system states attendance by breadth-first search (BFS), and the second one by depth-first search (DFS). The Kripke structure has been chosen as a transition system model. A case study has been conducted, where composite web service usage scenario has been considered. Obtained experimental results are aimed at increasing the effectiveness of TLA+ specifications automated verification.

Context. The task of production rules extraction while processing big arrays of data has been discussed. The problem of estimation of computer system used resources while extracting production rules based on parallel computations has been solved. The research object is the process of production rules extraction. The research subject lies in methods of parallel computer systems’ resource planning. Objective. The purpose of the work is а construction of the model for estimation parallel computer systems resources used to solve applied problems based on the parallel method of production rules extraction. Method. The article deals with the model building of used resources estimation of parallel computer system while extracting production rules. The model for estimation of computer system used resources while executing the parallel method of method of production rules extraction is proposed. Synthesized model takes into account the type of computer system, the amount of processors involved to solving the task and the bandwidth of data transfer network. In addition, the model considers parameters of used mathematical equipment (the portions of parallel system nodes involved for production rules extraction based on decision trees, associative rules and negative selection). Also the parameters of solved application task are taken into account. They are the number of observations and the number of characteristics in a given set of data describing the results of observations of the object or process being studied. The synthesized neural model is a polyalgorithmic. It allows estimating two characteristics of parallel computer system while executing the parallel method of production rules extraction. The first one is time used. And the second one is the volume of memory used. Results. The software which implements the proposed model and allows predicting the time and the volume of memory used of parallel computer system while solving practice tasks has been developed. Conclusions. The conducted experiments have confirmed the proposed software operability and allow recommending it for use in practice for solving the problems of big data processing. The prospects for further research may include the creation of parallel methods for feature selection, as well as an experimental study of proposed model on more complex practical problems of different nature and dimensionality.

The approach to Software-defined Network topology verification on a basis of Temporal Logic of Actions usage has been proposed. For this purpose the concept of "action" has been utilized. The Kripke structure has been used to analytically represent and describe the specified concepts. The proposed approach makes it possible to easily reconfigure the formal specification of virtual topology, with respect to the impermanent nature of the latter. The topology verification has been conducted by way of TLA Checker model checking method usage. From the automation perspective, the breadth-first search implementation has been chosen, pushing away the need to manually specify the depth of search.