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Special Issue "Sensors and Pattern Recognition Methods for Security and Industrial Applications (SPR-SIA)"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (15 October 2021).
Please contact the Guest Editor or the Section Managing Editor at ( [email protected]) for any queries.

Special Issue Editors

Prof. Dr. Michal Choras
E-Mail Website
Guest Editor
Institute of Telecommunications and Computer Science, University of Science and Technology (UTP) in Bydgoszcz, 85-796 Bydgoszcz, Poland
Interests: pattern recognition; machine learning; AI; security; cybersecurity
Special Issues and Collections in MDPI journals
Prof. Dr. Rafal Kozik
E-Mail Website
Guest Editor
Institute of Telecommunications and Computer Science, University of Science and Technology (UTP) in Bydgoszcz, 85-796 Bydgoszcz, Poland
Interests: pattern recognition; cybersecurity; AI
Special Issues and Collections in MDPI journals
Dr. Marek Pawlicki
E-Mail Website
Guest Editor
Institute of Telecommunications and Computer Science, University of Science and Technology (UTP) in Bydgoszcz, Poland.
Interests: pattern recognition; cybersecurity; AI

Special Issue Information

Dear Colleagues,

Contemporary cyberthreats keep on evolving, powering the neverending development arms race. Critical and industrial applications are particularly sensitive to both cyber and physical attacks, placing novel security solutions in high demand.

In this Special Issue of Sensors, it is our driving idea to invite high-quality papers which open the doors to accommodating new AI paradigms, approaches, and mechanisms in the domain of applied security. This includes pattern recognition, data analysis, and machine learning for industrial applications, including e-commerce.

The Special Issue will also present novel sensors (e.g., drones, IoT) used for security as well as innovative solutions for secure software development.

Relevant topics include but are not limited to the following:

  • security of IoT
  • security of cloud, fog, and edge networks
  • security and sensors in e-commerce
  • machine learning (shallow and deep) in security and industrial applications
  • sensors for security and industrial applications
  • innovative pattern recognition solutions
  • practical applications of AI in security
  • practical applications of AI in industrial applications
  • AI methods for threat prediction, detection, and mitigation
  • anomaly detection methods
  • AI and machine learning for secure software
  • biometrics
  • secure AI solutions
  • countering and detection of adversarial attacks

Prof. Dr. Michal Choras
Prof. Dr. Rafal Kozik
Dr. Marek Pawlicki
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 papers will be 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. Sensors is an international peer-reviewed open access semimonthly 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 2200 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.

Keywords

  • security of IoT
  • cloud, fog, and edge networks
  • machine learning
  • pattern recognition
  • AI applications
  • anomaly detection
  • secure software

Published Papers (4 papers)

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Research

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Article
Detection of Safe Passage for Trains at Rail Level Crossings Using Deep Learning
Sensors 2021, 21(18), 6281; https://doi.org/10.3390/s21186281 - 18 Sep 2021
Viewed by 622
Abstract
The detection of obstacles at rail level crossings (RLC) is an important task for ensuring the safety of train traffic. Traffic control systems require reliable sensors for determining the state of anRLC. Fusion of information from a number of sensors located at the [...] Read more.
The detection of obstacles at rail level crossings (RLC) is an important task for ensuring the safety of train traffic. Traffic control systems require reliable sensors for determining the state of anRLC. Fusion of information from a number of sensors located at the site increases the capability for reacting to dangerous situations. One such source is video from monitoring cameras. This paper presents a method for processing video data, using deep learning, for the determination of the state of the area (region of interest—ROI) vital for a safe passage of the train. The proposed approach is validated using video surveillance material from a number of RLC sites in Poland. The films include 24/7 observations in all weather conditions and in all seasons of the year. Results show that the recall values reach 0.98 using significantly reduced processing resources. The solution can be used as an auxiliary source of signals for train control systems, together with other sensor data, and the fused dataset can meet railway safety standards. Full article
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Article
Application of Feature Extraction Methods for Chemical Risk Classification in the Pharmaceutical Industry
Sensors 2021, 21(17), 5753; https://doi.org/10.3390/s21175753 - 26 Aug 2021
Viewed by 328
Abstract
The features that are used in the classification process are acquired from sensor data on the production site (associated with toxic, physicochemical properties) and also a dataset associated with cybersecurity that may affect the above-mentioned risk. These are large datasets, so it is [...] Read more.
The features that are used in the classification process are acquired from sensor data on the production site (associated with toxic, physicochemical properties) and also a dataset associated with cybersecurity that may affect the above-mentioned risk. These are large datasets, so it is important to reduce them. The author’s motivation was to develop a method of assessing the dimensionality of features based on correlation measures and the discriminant power of features allowing for a more accurate reduction of their dimensions compared to the classical Kaiser criterion and assessment of scree plot. The method proved to be promising. The results obtained in the experiments demonstrate that the quality of classification after extraction is better than using classical criteria for estimating the number of components and features. Experiments were carried out for various extraction methods, demonstrating that the rotation of factors according to centroids of a class in this classification task gives the best risk assessment of chemical threats. The classification quality increased by about 7% compared to a model where feature extraction was not used and resulted in an improvement of 4% compared to the classical PCA method with the Kaiser criterion, with an evaluation of the scree plot. Furthermore, it has been shown that there is a certain subspace of cybersecurity features, which complemented with the features of the concentration of volatile substances, affects the risk assessment of chemical hazards. The identified cybersecurity factors are the number of packets lost, incorrect Logins, incorrect sensor responses, increased email spam, and excessive traffic in the computer network. To visualize the speed of classification in real-time, simulations were carried out for various systems used in Industry 4.0. Full article
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Article
The Proposition and Evaluation of the RoEduNet-SIMARGL2021 Network Intrusion Detection Dataset
Sensors 2021, 21(13), 4319; https://doi.org/10.3390/s21134319 - 24 Jun 2021
Cited by 1 | Viewed by 492
Abstract
Cybersecurity is an arms race, with both the security and the adversaries attempting to outsmart one another, coming up with new attacks, new ways to defend against those attacks, and again with new ways to circumvent those defences. This situation creates a constant [...] Read more.
Cybersecurity is an arms race, with both the security and the adversaries attempting to outsmart one another, coming up with new attacks, new ways to defend against those attacks, and again with new ways to circumvent those defences. This situation creates a constant need for novel, realistic cybersecurity datasets. This paper introduces the effects of using machine-learning-based intrusion detection methods in network traffic coming from a real-life architecture. The main contribution of this work is a dataset coming from a real-world, academic network. Real-life traffic was collected and, after performing a series of attacks, a dataset was assembled. The dataset contains 44 network features and an unbalanced distribution of classes. In this work, the capability of the dataset for formulating machine-learning-based models was experimentally evaluated. To investigate the stability of the obtained models, cross-validation was performed, and an array of detection metrics were reported. The gathered dataset is part of an effort to bring security against novel cyberthreats and was completed in the SIMARGL project. Full article
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Review

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Review
A Systematic Review of Recommender Systems and Their Applications in Cybersecurity
Sensors 2021, 21(15), 5248; https://doi.org/10.3390/s21155248 - 03 Aug 2021
Viewed by 609
Abstract
This paper discusses the valuable role recommender systems may play in cybersecurity. First, a comprehensive presentation of recommender system types is presented, as well as their advantages and disadvantages, possible applications and security concerns. Then, the paper collects and presents the state of [...] Read more.
This paper discusses the valuable role recommender systems may play in cybersecurity. First, a comprehensive presentation of recommender system types is presented, as well as their advantages and disadvantages, possible applications and security concerns. Then, the paper collects and presents the state of the art concerning the use of recommender systems in cybersecurity; both the existing solutions and future ideas are presented. The contribution of this paper is two-fold: to date, to the best of our knowledge, there has been no work collecting the applications of recommenders for cybersecurity. Moreover, this paper attempts to complete a comprehensive survey of recommender types, after noticing that other works usually mention two–three types at once and neglect the others. Full article
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