Special Issue "Quality and Security of Critical Infrastructure Systems"

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

Deadline for manuscript submissions: 31 August 2023 | Viewed by 977

Special Issue Editors

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
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
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 quarterly 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 1600 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 (1 paper)

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Research

Article
An Obstacle-Finding Approach for Autonomous Mobile Robots Using 2D LiDAR Data
Big Data Cogn. Comput. 2023, 7(1), 43; https://doi.org/10.3390/bdcc7010043 - 01 Mar 2023
Viewed by 505
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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