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Quality and Security of Critical Infrastructure Systems
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Quality and Security of Critical Infrastructure Systems

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 16838

Special Issue Editors

Dr. Ivan Izonin

E-Mail Website
Guest Editor
Department of Artificial Intelligence, Lviv Polytechnic National University, Lviv 79000, Ukraine
Interests: artificial neural networks; few-shot learning; ensemble learning; non-iterative learning algorithms; engineering and medical applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tetiana Hovorushchenko

E-Mail Website
Guest Editor
Department of Computer Engineering & Information Systems, Khmelnytskyi National University, Khmelnytskyi, Ukraine
Interests: information technologies; software quality audit and assurance; verification and validation of critical software; information systems and technologies for medicine
Special Issues, Collections and Topics in MDPI journals
Dr. Shishir K. Shandilya

E-Mail Website
Guest Editor
1. SECURE - Centre of Excellence in Cyber Security, VIT Bhopal, Sehore, India
2. Executive Director, National Cyber Defence Research Centre, New Delhi, India
3. Visiting Researcher, Liverpool Hope University, Liverpool, UK
Interests: cyber security; nature-inspired cyber computing

Special Issue Information

Dear Colleagues,

The amount of information is constantly growing, and thus the issue of information security is becoming more acute. At the current stage of economic development, when information management becomes a critical business function, malware attacks on critical infrastructure systems and software bugs in such systems pose real threats. Every 12 months, 50% of industrial companies in the world experience one to five cyber incidents. The loss of the world economy as a result of cyber-attacks is USD 445 billion. Cyber-attacks on critical infrastructure systems and software errors in critical infrastructure systems pose a real threat to the security of the human community, leading to human casualties, environmental cataclysms, and significant financial losses. If the company works with the data of individuals, then cyber-attacks and information theft are risk factors that cause reputational and financial damage to the company.

Currently, all areas of human activity are related to computer systems and software, so the current problems in the use of computer systems and software are currently the reliable protection of information from cyber threats and malware as well as the quality assurance of software and computer systems. Known methods and tools in the field of cybersecurity and software quality assurance are unable to provide the reliable protection of information from malware, the detection and disposal of malware, and cannot ensure the required software quality of critical infrastructure systems.

Achieving high-quality software and computer systems as well as their cybersecurity is a key factor in their effective use, and is one of the main needs of customers.

This Special Issue aims to disseminate and discuss models and methods of quality and security of critical infrastructure systems that support sophisticated solutions to improve and ensure the quality and security of software and computer systems. We will only consider knowledge-intensive solutions that outline existing issues for the quality and security of critical infrastructure systems and offer reliable and accurate solutions. Original, unpublished studies in different application areas on the following main topics are welcome:

  • Software systems quality;
  • Software systems security;
  • Software systems reliability;
  • Cybersecurity;
  • Computer systems quality;
  • Computer systems security;
  • Computer systems reliability.

Dr. Ivan Izonin
Prof. Dr. Tetiana Hovorushchenko
Dr. Shishir K. Shandilya
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (6 papers)

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Editorial

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3 pages, 150 KiB  
Editorial
Quality and Security of Critical Infrastructure Systems
by Ivan Izonin, Tetiana Hovorushchenko and Shishir Kumar Shandilya
Big Data Cogn. Comput. 2024, 8(1), 10; https://doi.org/10.3390/bdcc8010010 - 22 Jan 2024
Viewed by 1310
Abstract
The amount of information is constantly growing, and thus, the issue of information security is becoming more acute [...] Full article
(This article belongs to the Special Issue Quality and Security of Critical Infrastructure Systems)

Research

Jump to: Editorial

21 pages, 5814 KiB  
Article
Intelligent Method for Classifying the Level of Anthropogenic Disasters
by Khrystyna Lipianina-Honcharenko, Carsten Wolff, Anatoliy Sachenko, Ivan Kit and Diana Zahorodnia
Big Data Cogn. Comput. 2023, 7(3), 157; https://doi.org/10.3390/bdcc7030157 - 21 Sep 2023
Cited by 1 | Viewed by 1557
Abstract
Anthropogenic disasters pose a challenge to management in the modern world. At the same time, it is important to have accurate and timely information to assess the level of danger and take appropriate measures to eliminate disasters. Therefore, the purpose of the paper [...] Read more.
Anthropogenic disasters pose a challenge to management in the modern world. At the same time, it is important to have accurate and timely information to assess the level of danger and take appropriate measures to eliminate disasters. Therefore, the purpose of the paper is to develop an effective method for assessing the level of anthropogenic disasters based on information from witnesses to the event. For this purpose, a conceptual model for assessing the consequences of anthropogenic disasters is proposed, the main components of which are the following ones: the analysis of collected data, modeling and assessment of their consequences. The main characteristics of the intelligent method for classifying the level of anthropogenic disasters are considered, in particular, exploratory data analysis using the EDA method, classification based on textual data using SMOTE, and data classification by the ensemble method of machine learning using boosting. The experimental results confirmed that for textual data, the best classification is at level V and level I with an error of 0.97 and 0.94, respectively, and the average error estimate is 0.68. For quantitative data, the classification accuracy of Potential Accident Level relative to Industry Sector is 77%, and the f1-score is 0.88, which indicates a fairly high accuracy of the model. The architecture of a mobile application for classifying the level of anthropogenic disasters has been developed, which reduces the time required to assess consequences of danger in the region. In addition, the proposed approach ensures interaction with dynamic and uncertain environments, which makes it an effective tool for classifying. Full article
(This article belongs to the Special Issue Quality and Security of Critical Infrastructure Systems)
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19 pages, 3116 KiB  
Article
Application of Artificial Intelligence for Fraudulent Banking Operations Recognition
by Bohdan Mytnyk, Oleksandr Tkachyk, Nataliya Shakhovska, Solomiia Fedushko and Yuriy Syerov
Big Data Cogn. Comput. 2023, 7(2), 93; https://doi.org/10.3390/bdcc7020093 - 10 May 2023
Cited by 5 | Viewed by 6580
Abstract
This study considers the task of applying artificial intelligence to recognize bank fraud. In recent years, due to the COVID-19 pandemic, bank fraud has become even more common due to the massive transition of many operations to online platforms and the creation of [...] Read more.
This study considers the task of applying artificial intelligence to recognize bank fraud. In recent years, due to the COVID-19 pandemic, bank fraud has become even more common due to the massive transition of many operations to online platforms and the creation of many charitable funds that criminals can use to deceive users. The present work focuses on machine learning algorithms as a tool well suited for analyzing and recognizing online banking transactions. The study’s scientific novelty is the development of machine learning models for identifying fraudulent banking transactions and techniques for preprocessing bank data for further comparison and selection of the best results. This paper also details various methods for improving detection accuracy, i.e., handling highly imbalanced datasets, feature transformation, and feature engineering. The proposed model, which is based on an artificial neural network, effectively improves the accuracy of fraudulent transaction detection. The results of the different algorithms are visualized, and the logistic regression algorithm performs the best, with an output AUC value of approximately 0.946. The stacked generalization shows a better AUC of 0.954. The recognition of banking fraud using artificial intelligence algorithms is a topical issue in our digital society. Full article
(This article belongs to the Special Issue Quality and Security of Critical Infrastructure Systems)
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13 pages, 1895 KiB  
Article
Two-Stage PNN–SVM Ensemble for Higher Education Admission Prediction
by Khrystyna Zub, Pavlo Zhezhnych and Christine Strauss
Big Data Cogn. Comput. 2023, 7(2), 83; https://doi.org/10.3390/bdcc7020083 - 23 Apr 2023
Cited by 1 | Viewed by 1515
Abstract
In this paper, we investigate the methods used to evaluate the admission chances of higher education institutions’ (HEI) entrants as a crucial factor that directly influences the admission efficiency, quality of education results, and future students’ life-long trajectories. Due to the conditions of [...] Read more.
In this paper, we investigate the methods used to evaluate the admission chances of higher education institutions’ (HEI) entrants as a crucial factor that directly influences the admission efficiency, quality of education results, and future students’ life-long trajectories. Due to the conditions of uncertainty surrounding the decision-making process that determines the admission of entrants and the inability to independently assess the probability of potential outcomes, we propose the application of the machine learning (ML) model as an algorithm that provides decision-making support. The proposed model includes the support vector machine (SVM) stacking ensemble, which expands the input data set obtained using the Probabilistic Neural Network (PNN). The basic algorithms include four SVM ensemble methods with different kernel functions and Logistic Regression (LR) as a meta-algorithm. We evaluate the accuracy of the developed model in three stages: comparison with existing ML methods; comparison with a single-based model that comprises it; and comparison with a similar stacking model and with other types of ensembles (boosting, begging). The results of the designed two-stage PNN–SVM ensemble model provided an accuracy of 94% and possessed acquired superiority in the comparison stages. The obtained results enable the use of the presented model in the subsequent stages of the development of an intellectual support system for decision making regarding entrants’ admission. Full article
(This article belongs to the Special Issue Quality and Security of Critical Infrastructure Systems)
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15 pages, 3678 KiB  
Article
An Approach for Opening Doors with a Mobile Robot Using Machine Learning Methods
by Lesia Mochurad, Yaroslav Hladun, Yevgen Zasoba and Michal Gregus
Big Data Cogn. Comput. 2023, 7(2), 69; https://doi.org/10.3390/bdcc7020069 - 06 Apr 2023
Cited by 2 | Viewed by 2047
Abstract
One of the tasks of robotics is to develop a robot’s ability to perform specific actions for as long as possible without human assistance. One such step is to open different types of doors. This task is essential for any operation that involves [...] Read more.
One of the tasks of robotics is to develop a robot’s ability to perform specific actions for as long as possible without human assistance. One such step is to open different types of doors. This task is essential for any operation that involves moving a robot from one room to another. This paper proposes a versatile and computationally efficient algorithm for an autonomous mobile robot opening different types of doors, using machine learning methods. The latter include the YOLOv5 object detection model, the RANSAC iterative method for estimating the mathematical model parameters, and the DBSCAN clustering algorithm. Alternative clustering methods are also compared. The proposed algorithm was explored and tested in simulation and on a real robot manufactured by SOMATIC version Dalek. The percentage of successful doors opened out of the total number of attempts was used as an accuracy metric. The proposed algorithm reached an accuracy of 95% in 100 attempts. The result of testing the door-handle detection algorithm on simulated data was an error of 1.98 mm in 10,000 samples. That is, the average distance from the door handle found by the detector to the real one was 1.98 mm. The proposed algorithm has shown high accuracy and the ability to be applied in real time for opening different types of doors. Full article
(This article belongs to the Special Issue Quality and Security of Critical Infrastructure Systems)
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16 pages, 3458 KiB  
Article
An Obstacle-Finding Approach for Autonomous Mobile Robots Using 2D LiDAR Data
by Lesia Mochurad, Yaroslav Hladun and Roman Tkachenko
Big Data Cogn. Comput. 2023, 7(1), 43; https://doi.org/10.3390/bdcc7010043 - 01 Mar 2023
Cited by 10 | Viewed by 2777
Abstract
Obstacle detection is crucial for the navigation of autonomous mobile robots: it is necessary to ensure their presence as accurately as possible and find their position relative to the robot. Autonomous mobile robots for indoor navigation purposes use several special sensors for various [...] Read more.
Obstacle detection is crucial for the navigation of autonomous mobile robots: it is necessary to ensure their presence as accurately as possible and find their position relative to the robot. Autonomous mobile robots for indoor navigation purposes use several special sensors for various tasks. One such study is localizing the robot in space. In most cases, the LiDAR sensor is employed to solve this problem. In addition, the data from this sensor are critical, as the sensor is directly related to the distance of objects and obstacles surrounding the robot, so LiDAR data can be used for detection. This article is devoted to developing an obstacle detection algorithm based on 2D LiDAR sensor data. We propose a parallelization method to speed up this algorithm while processing big data. The result is an algorithm that finds obstacles and objects with high accuracy and speed: it receives a set of points from the sensor and data about the robot’s movements. It outputs a set of line segments, where each group of such line segments describes an object. The two proposed metrics assessed accuracy, and both averages are high: 86% and 91% for the first and second metrics, respectively. The proposed method is flexible enough to optimize it for a specific configuration of the LiDAR sensor. Four hyperparameters are experimentally found for a given sensor configuration to maximize the correspondence between real and found objects. The work of the proposed algorithm has been carefully tested on simulated and actual data. The authors also investigated the relationship between the selected hyperparameters’ values and the algorithm’s efficiency. Potential applications, limitations, and opportunities for future research are discussed. Full article
(This article belongs to the Special Issue Quality and Security of Critical Infrastructure Systems)
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