scholarly journals An approach to identifying threats of extracting confidential data from automated control systems based on internet technologies

2021 ◽  
Vol 15 (3) ◽  
pp. 35-47
Author(s):  
Vladimir Kuzmin ◽  
Artem Menisov
Keyword(s):  
Neural Network ◽  
Information Security ◽  
Control Systems ◽  
Short Term Memory ◽  
Methodological Approach ◽  
The State ◽  
External Information ◽  
Automated Control ◽  
Universal Sentence ◽  
Confidential Data

Together with ubiquitous, global digitalization, cybercrime is growing and developing rapidly. The state considers the creation of an environment conducive to information security to be a strategic goal for the development of the information society in Russia. However, the question of how the “state of protection of the individual, society and the state from internal and external information threats” should be achieved in accordance with the “Information Security” and the “Digital Economy of Russia 2024” programs remains open. The aim of this study is to increase the efficiency whereby automated control systems identify confidential data from html-pages to reduce the risk of using this data in the preparatory and initial stages of attacks on the infrastructure of government organizations. The article describes an approach that has been developed to identify confidential data based on the combination of several neural network technologies: a universal sentence encoder and a neural network recurrent architecture of bidirectional long-term short-term memory. The results of an assessment in comparison with modern means of natural language text processing (SpaCy) showed the merits and prospects of the practical application of the methodological approach.

scholarly journals Learners Demographics Classification on MOOCs During the COVID-19: Author Profiling via Deep Learning Based on Semantic and Syntactic Representations

2021 ◽  
Vol 6 ◽  
Author(s):  
Tahani Aljohani ◽  
Alexandra I. Cristea
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Prediction Models ◽  
Short Term Memory ◽  
Methodological Approach ◽  
High Accuracy ◽  
Directional Model ◽  
Textual Representations ◽  
The One

Massive Open Online Courses (MOOCs) have become universal learning resources, and the COVID-19 pandemic is rendering these platforms even more necessary. In this paper, we seek to improve Learner Profiling (LP), i.e. estimating the demographic characteristics of learners in MOOC platforms. We have focused on examining models which show promise elsewhere, but were never examined in the LP area (deep learning models) based on effective textual representations. As LP characteristics, we predict here the employment status of learners. We compare sequential and parallel ensemble deep learning architectures based on Convolutional Neural Networks and Recurrent Neural Networks, obtaining an average high accuracy of 96.3% for our best method. Next, we predict the gender of learners based on syntactic knowledge from the text. We compare different tree-structured Long-Short-Term Memory models (as state-of-the-art candidates) and provide our novel version of a Bi-directional composition function for existing architectures. In addition, we evaluate 18 different combinations of word-level encoding and sentence-level encoding functions. Based on these results, we show that our Bi-directional model outperforms all other models and the highest accuracy result among our models is the one based on the combination of FeedForward Neural Network and the Stack-augmented Parser-Interpreter Neural Network (82.60% prediction accuracy). We argue that our prediction models recommended for both demographics characteristics examined in this study can achieve high accuracy. This is additionally also the first time a sound methodological approach toward improving accuracy for learner demographics classification on MOOCs was proposed.

scholarly journals Using a probabilistic neural network to assess the state of information security

2021 ◽  
Vol 2032 (1) ◽  
pp. 012051
Author(s):  
M E Sukhoparov ◽  
I A Sikarev ◽  
T M Tatarnikova ◽  
I S Lebedev ◽  
A A Butsanets
Keyword(s):  
Neural Network ◽  
Information Security ◽  
Probabilistic Neural Network ◽  
The State

scholarly journals Organization of Secure Communication between Programmable Logic Controllers Using the Open Platform Communications Protocol

2021 ◽  
Vol 2096 (1) ◽  
pp. 012125
Author(s):  
F R Ametov ◽  
E A Bekirov ◽  
M M Asanov
Keyword(s):  
Information Security ◽  
Control Systems ◽  
Secure Communication ◽  
Third Party ◽  
Programmable Logic ◽  
Automated Control ◽  
Open Platform ◽  
Programmable Logic Devices ◽  
Communications Protocol ◽  
Technical Features

Abstract Modern automated control systems in industrial and private enterprises are represented by a set of programmable logic devices and components from various manufacturers. They operate based on many of the most widely used protocols and interfaces. Some manufacturers have their own solutions that are mainly focused only on their product, without direct or indirect compatibility with third-party solutions. Many control systems used now operate on relatively old solutions that cannot be partially modernized due to lack of technical resources or financial unprofitability. The paper considers the most popular industrial protocols Modbus and Profibus, analyzes their advantages, as well as features of the structure and functionality. The study of the operation problems and information security of modern control systems is considered, solutions for their elimination are analyzed. A solution for the control systems modernization was proposed based on the analysis. It can become effective and financially justified due to its technical features, allowing it to be adapted to existing solutions. Conclusions about the effectiveness of the proposed solution were formulated based on the data collected and the goals achieved.

Legal and regulatory framework of Information security of automated process control systems

2018 ◽  
pp. 75-85
Author(s):  
T. Yu. Kirilina ◽  
E. N. Gorbaneva ◽  
A. V. Poznyakevich
Keyword(s):  
Information Security ◽  
Process Control ◽  
Control Systems ◽  
Russian Federation ◽  
Critical Value ◽  
The State ◽  
Regulatory Framework ◽  
Process Control Systems ◽  
The Russian Federation ◽  
Automated Process

Nowadays protection of automated process control systems in the Russian Federation is one of the most important problems in the field of information security. Number of cyberthreats is increasing dramatically that has critical value for an ecological, social and macroeconomic component of the state. 

HUMANITARIAN ASPECTS OF INFORMATION SECURITY. APPROACHES. DISCOURSE (PREQUEL)

2020 ◽  
pp. 585-596
Author(s):  
Петр Юрьевич Филяк ◽  
Виктория Вячеславовна Пименова ◽  
Александр Григорьевич Остапенко ◽  
Сергей Александрович Ермаков
Keyword(s):  
Information Security ◽  
State Level ◽  
The State ◽  
Macro Level ◽  
External Information ◽  
Points Of View ◽  
The Individual

Настоящая статья посвящена информационной безопасности. Точнее, одной из составляющей понятия информационная безопасность, а если говорить более стандартизованно, то одному из ее аспектов, то есть выражаясь корректно, профессиональным языком (используя профессиональную стандартизованную терминологию) - «Гуманитарным аспектам информационной безопасности» (ГАИБ). Если говорить о ГАИБ, то рассмотрении этого термина необходимо начинать с макро уровня сферы информационной безопасности, то есть на уровне обеспечения информационной безопасности в масштабах государства. Если рассматривать информационную безопасность как состояние защищенности личности, общества и государства от внутренних и внешних информационных угроз, то в качестве целей и задач ГАИБ должны рассматриваться прежде всего защищенность как отдельной личности ( humanitas - гуманитарный ), так и всего социума, который и являет собой в итоге и общество и государство в целом, причем рассматривать ее с разных точек зрения ( аспектов ). Отсюда и происходит обозначенный выше термин - гуманитарные аспекты информационной безопасности (ГАИБ). Таковы исходные императивы при рассмотрении вопросов и проблем, затрагиваемых в рамках настоящей статьи. This article is devoted to information security., the term should be considered from the macro level of the information security sphere, that is, at the level of information security at the state level. If we consider information security as a state of protection of the individual, society and the state from internal and external information threats, then the objectives and objectives of the HAIS should be considered first of all the protection as an individual (humanitas - humanitarian) and the whole society, which is in the end both society and the state as a whole, and consider it from different points of view(aspects). Hence the term mentioned above - the humanitarian aspects of information security (HAIS). These are the initial imperatives in dealing with the issues and issues raised in this article.

NEURAL NETWORK METHOD FOR PREDICTING THE STATE OF INFORMATION OBJECTS

2021 ◽  
pp. 37-43
Author(s):  
A. D. Obukhov
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Learning Technologies ◽  
The State ◽  
Time Interval ◽  
Problem Solution ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Method ◽  
Information Objects

The analysis and process of not only the current states of information objects, but also the prediction of future states with a certain time interval presents a major significance for adaptive information systems. This allows improving the quality and reliability of these systems functioning, reducing the delay in response to external influences, preparing for operations, and increasing the responsiveness and speed of the system. In order to solve this problem, the article proposes a neural network method for forecasting the state of information objects based on the application of machine learning technologies. A formalized algorithm for its implementation in the notation of set theory is presented. A distinctive characteristic of the designed method is the automatic determination of the optimal structure of the neural network, depending on the type of information object. The method also covers the possibility of using the complex of the previous states of the object to improve the forecast accuracy. Practical researches on approbation of the neural network method showed its efficiency and high accuracy. The following popular datasets were used as input data: Individual household electric power consumption, HAR (Human Activity Recognition) accelerometer, as well as gathered data on human relocation. LSTM (Long Short-Term Memory) neural network was applied to conduct the forecasts. The comparison of the developed method with a similar software solution DEvol (DeepEvolution) showed comparable or better indicators in terms of accuracy and time for the problem solution (1.7 times faster on average).

scholarly journals Modern Problems of Information Security in Control and Access Control Systems When Using Neural Networks

2021 ◽  
Vol 2096 (1) ◽  
pp. 012159
Author(s):  
V A Chelukhin ◽  
S E Tikhonov ◽  
Pyae Zone Aung
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Information Security ◽  
Access Control ◽  
Control Systems ◽  
Theoretical Study ◽  
Management Systems ◽  
Control And Management ◽  
Network Technologies ◽  
Neural Network Technologies

Abstract This work is devoted to a theoretical study of the investigation of incidents from the operation of access control systems using neural networks in our time. The work describes the processes of operation of control and access control systems, in which neural network technologies are most actively introduced among the components of access control and management systems, and which, from the introduction of neural networks into them, can show new vulnerabilities in the operation of the access control and management system as a whole.

scholarly journals Predicting machine failure using recurrent neural network-gated recurrent unit (RNN-GRU) through time series data

2021 ◽  
Vol 10 (2) ◽  
pp. 870-878
Author(s):  
Zainuddin Z. ◽  
P. Akhir E. A. ◽  
Hasan M. H.
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Recurrent Neural Network ◽  
Time Series Data ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
The State ◽  
Series Data ◽  
Machine Failure ◽  
Gated Recurrent Unit

Time series data often involves big size environment that lead to high dimensionality problem. Many industries are generating time series data that continuously update each second. The arising of machine learning may help in managing the data. It can forecast future instance while handling large data issues. Forecasting is related to predicting task of an upcoming event to avoid any circumstances happen in current environment. It helps those sectors such as production to foresee the state of machine in line with saving the cost from sudden breakdown as unplanned machine failure can disrupt the operation and loss up to millions. Thus, this paper offers a deep learning algorithm named recurrent neural network-gated recurrent unit (RNN-GRU) to forecast the state of machines producing the time series data in an oil and gas sector. RNN-GRU is an affiliation of recurrent neural network (RNN) that can control consecutive data due to the existence of update and reset gates. The gates decided on the necessary information to be kept in the memory. RNN-GRU is a simpler structure of long short-term memory (RNN-LSTM) with 87% of accuracy on prediction.

scholarly journals OPTIMIZATION OF EQUIPMENT RESERVE FOR INTELLECTUAL AUTOMATED SYSTEMS

2021 ◽  
Vol 2 (14) ◽  
pp. 87-99
Author(s):  
Vitaliy Chubaievskyi ◽  
Valery Lakhno ◽  
Berik Akhmetov ◽  
Olena Kryvoruchko ◽  
Dmytro Kasatkin ◽  
...  
Keyword(s):  
Neural Network ◽  
Control Systems ◽  
Optimization Problem ◽  
Destructive Interference ◽  
Network Analyzer ◽  
Calculation Methods ◽  
Automated Control ◽  
The Neural Network ◽  
The Cost ◽  
Selection Of

Algorithms for a neural network analyzer involved in the decision support system (DSS) during the selection of the composition of backup equipment (CBE) for intelligent automated control systems Smart City are proposed. A model, algorithms and software have been developed for solving the optimization problem of choosing a CBE capable of ensuring the uninterrupted operation of the IACS both in conditions of technological failures and in conditions of destructive interference in the operation of the IACS by the attackers. The proposed solutions help to reduce the cost of determining the optimal CBE for IACS by 15–17% in comparison with the results of known calculation methods. The results of computational experiments to study the degree of influence of the outputs of the neural network analyzer on the efficiency of the functioning of the CBE for IACS are presented.

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