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

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

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 49326
Please contact the Guest Editor or the Section Managing Editor at ([email protected]) for any queries.

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Special Issue Editors

Prof. Dr. Michal Choras

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Faculty of Telecommunications, Computer Science and Electrical Engineering, UTP University of Science and Technology, 85-796 Bydgoszcz, Poland
Interests: pattern recognition; machine learning; AI; security; cybersecurity
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Rafal Kozik

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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, Collections and Topics 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 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. 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 2600 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

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Related Special Issue

Sensors and Pattern Recognition Methods for Security and Industrial Applications (SPR-SIA)—2nd Edition in Sensors (2 articles)

Published Papers (12 papers)

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Editorial

Jump to: Research, Review

3 pages, 154 KiB

Open AccessEditorial

Sensors and Pattern Recognition Methods for Security and Industrial Applications

by Michał Choraś, Rafał Kozik and Marek Pawlicki

Sensors 2022, 22(16), 5968; https://doi.org/10.3390/s22165968 - 10 Aug 2022

Viewed by 1874

Abstract

Contemporary cyberthreats continue to evolve, powering the neverending development arms race [...] Full article

(This article belongs to the Special Issue Sensors and Pattern Recognition Methods for Security and Industrial Applications (SPR-SIA))

Research

Jump to: Editorial, Review

17 pages, 1853 KiB

Open AccessArticle

Face Presentation Attack Detection Using Deep Background Subtraction

by Azeddine Benlamoudi, Salah Eddine Bekhouche, Maarouf Korichi, Khaled Bensid, Abdeldjalil Ouahabi, Abdenour Hadid and Abdelmalik Taleb-Ahmed

Sensors 2022, 22(10), 3760; https://doi.org/10.3390/s22103760 - 15 May 2022

Cited by 15 | Viewed by 3250

Abstract

Currently, face recognition technology is the most widely used method for verifying an individual’s identity. Nevertheless, it has increased in popularity, raising concerns about face presentation attacks, in which a photo or video of an authorized person’s face is used to obtain access to services. Based on a combination of background subtraction (BS) and convolutional neural network(s) (CNN), as well as an ensemble of classifiers, we propose an efficient and more robust face presentation attack detection algorithm. This algorithm includes a fully connected (FC) classifier with a majority vote (MV) algorithm, which uses different face presentation attack instruments (e.g., printed photo and replayed video). By including a majority vote to determine whether the input video is genuine or not, the proposed method significantly enhances the performance of the face anti-spoofing (FAS) system. For evaluation, we considered the MSU MFSD, REPLAY-ATTACK, and CASIA-FASD databases. The obtained results are very interesting and are much better than those obtained by state-of-the-art methods. For instance, on the REPLAY-ATTACK database, we were able to attain a half-total error rate (HTER) of 0.62% and an equal error rate (EER) of 0.58%. We attained an EER of 0% on both the CASIA-FASD and the MSU MFSD databases. Full article

(This article belongs to the Special Issue Sensors and Pattern Recognition Methods for Security and Industrial Applications (SPR-SIA))

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23 pages, 533 KiB

Open AccessArticle

Lightweight Long Short-Term Memory Variational Auto-Encoder for Multivariate Time Series Anomaly Detection in Industrial Control Systems

by Daniel Fährmann, Naser Damer, Florian Kirchbuchner and Arjan Kuijper

Sensors 2022, 22(8), 2886; https://doi.org/10.3390/s22082886 - 9 Apr 2022

Cited by 24 | Viewed by 4749

Abstract

Heterogeneous cyberattacks against industrial control systems (ICSs) have had a strong impact on the physical world in recent decades. Connecting devices to the internet enables new attack surfaces for attackers. The intrusion of ICSs, such as the manipulation of industrial sensory or actuator data, can be the cause for anomalous ICS behaviors. This poses a threat to the infrastructure that is critical for the operation of a modern city. Nowadays, the best techniques for detecting anomalies in ICSs are based on machine learning and, more recently, deep learning. Cybersecurity in ICSs is still an emerging field, and industrial datasets that can be used to develop anomaly detection techniques are rare. In this paper, we propose an unsupervised deep learning methodology for anomaly detection in ICSs, specifically, a lightweight long short-term memory variational auto-encoder (LW-LSTM-VAE) architecture. We successfully demonstrate our solution under two ICS applications, namely, water purification and water distribution plants. Our proposed method proves to be efficient in detecting anomalies in these applications and improves upon reconstruction-based anomaly detection methods presented in previous work. For example, we successfully detected 82.16% of the anomalies in the scenario of the widely used Secure Water Treatment (SWaT) benchmark. The deep learning architecture we propose has the added advantage of being extremely lightweight. Full article

(This article belongs to the Special Issue Sensors and Pattern Recognition Methods for Security and Industrial Applications (SPR-SIA))

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18 pages, 1130 KiB

Open AccessArticle

Inter-Domain Fusion for Enhanced Intrusion Detection in Power Systems: An Evidence Theoretic and Meta-Heuristic Approach

by Abhijeet Sahu and Katherine Davis

Sensors 2022, 22(6), 2100; https://doi.org/10.3390/s22062100 - 9 Mar 2022

Cited by 6 | Viewed by 2693

Abstract

False alerts due to misconfigured or compromised intrusion detection systems (IDS) in industrial control system (ICS) networks can lead to severe economic and operational damage. However, research using deep learning to reduce false alerts often requires the physical and cyber sensor data to be trustworthy. Implicit trust is a major problem for artificial intelligence or machine learning (AI/ML) in cyber-physical system (CPS) security, because when these solutions are most urgently needed is also when they are most at risk (e.g., during an attack). To address this, the Inter-Domain Evidence theoretic Approach for Inference (IDEA-I) is proposed that reframes the detection problem as how to make good decisions given uncertainty. Specifically, an evidence theoretic approach leveraging Dempster–Shafer (DS) combination rules and their variants is proposed for reducing false alerts. A multi-hypothesis mass function model is designed that leverages probability scores obtained from supervised-learning classifiers. Using this model, a location-cum-domain-based fusion framework is proposed to evaluate the detector’s performance using disjunctive, conjunctive, and cautious conjunctive rules. The approach is demonstrated in a cyber-physical power system testbed, and the classifiers are trained with datasets from Man-In-The-Middle attack emulation in a large-scale synthetic electric grid. For evaluating the performance, we consider plausibility, belief, pignistic, and general Bayesian theorem-based metrics as decision functions. To improve the performance, a multi-objective-based genetic algorithm is proposed for feature selection considering the decision metrics as the fitness function. Finally, we present a software application to evaluate the DS fusion approaches with different parameters and architectures. Full article

(This article belongs to the Special Issue Sensors and Pattern Recognition Methods for Security and Industrial Applications (SPR-SIA))

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25 pages, 10639 KiB

Open AccessArticle

A Novel, Low Computational Complexity, Parallel Swarm Algorithm for Application in Low-Energy Devices

by Zofia Długosz, Michał Rajewski, Rafał Długosz and Tomasz Talaśka

Sensors 2021, 21(24), 8449; https://doi.org/10.3390/s21248449 - 17 Dec 2021

Cited by 7 | Viewed by 2683

Abstract

In this work, we propose a novel metaheuristic algorithm that evolved from a conventional particle swarm optimization (PSO) algorithm for application in miniaturized devices and systems that require low energy consumption. The modifications allowed us to substantially reduce the computational complexity of the PSO algorithm, translating to reduced energy consumption in hardware implementation. This is a paramount feature in the devices used, for example, in wireless sensor networks (WSNs) or wireless body area sensors (WBANs), in which particular devices have limited access to a power source. Various swarm algorithms are widely used in solving problems that require searching for an optimal solution, with simultaneous occurrence of a different number of sub-optimal solutions. This makes the hardware implementation worthy of consideration. However, hardware implementation of the conventional PSO algorithm is challenging task. One of the issues is an efficient implementation of the randomization function. In this work, we propose novel methods to work around this problem. In the proposed approach, we replaced the block responsible for generating random values using deterministic methods, which differentiate the trajectories of particular particles in the swarm. Comprehensive investigations in the software model of the modified algorithm have shown that its performance is comparable with or even surpasses the conventional PSO algorithm in a multitude of scenarios. The proposed algorithm was tested with numerous fitness functions to verify its flexibility and adaptiveness to different problems. The paper also presents the hardware implementation of the selected blocks that modify the algorithm. In particular, we focused on reducing the hardware complexity, achieving high-speed operation, while reducing energy consumption. Full article

(This article belongs to the Special Issue Sensors and Pattern Recognition Methods for Security and Industrial Applications (SPR-SIA))

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17 pages, 4494 KiB

Open AccessArticle

Presentation Attack Detection on Limited-Resource Devices Using Deep Neural Classifiers Trained on Consistent Spectrogram Fragments

by Kacper Kubicki, Paweł Kapusta and Krzysztof Ślot

Sensors 2021, 21(22), 7728; https://doi.org/10.3390/s21227728 - 20 Nov 2021

Cited by 1 | Viewed by 1855

Abstract

The presented paper is concerned with detection of presentation attacks against unsupervised remote biometric speaker verification, using a well-known challenge–response scheme. We propose a novel approach to convolutional phoneme classifier training, which ensures high phoneme recognition accuracy even for significantly simplified network architectures, thus enabling efficient utterance verification on resource-limited hardware, such as mobile phones or embedded devices. We consider Deep Convolutional Neural Networks operating on windows of speech Mel-Spectrograms as a means for phoneme recognition, and we show that one can boost the performance of highly simplified neural architectures by modifying the principle underlying training set construction. Instead of generating training examples by slicing spectrograms using a sliding window, as it is commonly done, we propose to maximize the consistency of phoneme-related spectrogram structures that are to be learned, by choosing only spectrogram chunks from the central regions of phoneme articulation intervals. This approach enables better utilization of the limited capacity of the considered simplified networks, as it significantly reduces a within-class data scatter. We show that neural architectures comprising as few as dozens of thousands parameters can successfully—with accuracy of up to 76%, solve the 39-phoneme recognition task (we use the English language TIMIT database for experimental verification of the method). We also show that ensembling of simple classifiers, using a basic bagging method, boosts the recognition accuracy by another 2–3%, offering Phoneme Error Rates at the level of 23%, which approaches the accuracy of the state-of-the-art deep neural architectures that are one to two orders of magnitude more complex than the proposed solution. This, in turn, enables executing reliable presentation attack detection, based on just few-syllable long challenges on highly resource-limited computing hardware. Full article

(This article belongs to the Special Issue Sensors and Pattern Recognition Methods for Security and Industrial Applications (SPR-SIA))

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18 pages, 1826 KiB

Open AccessArticle

Evaluation of Keypoint Descriptors for Flight Simulator Cockpit Elements: WrightBroS Database

by Karolina Nurzynska, Przemysław Skurowski, Magdalena Pawlyta and Krzysztof Cyran

Sensors 2021, 21(22), 7687; https://doi.org/10.3390/s21227687 - 19 Nov 2021

Cited by 4 | Viewed by 2604

Abstract

The goal of the WrightBroS project is to design a system supporting the training of pilots in a flight simulator. The desired software should work on smart glasses supplementing the visual information with augmented reality data, displaying, for instance, additional training information or descriptions of visible devices in real time. Therefore, the rapid recognition of observed objects and their exact positioning is crucial for successful deployment. The keypoint descriptor approach is a natural framework that is used for this purpose. For this to be applied, the thorough examination of specific keypoint location methods and types of keypoint descriptors is required first, as these are essential factors that affect the overall accuracy of the approach. In the presented research, we prepared a dedicated database presenting 27 various devices of flight simulator. Then, we used it to compare existing state-of-the-art techniques and verify their applicability. We investigated the time necessary for the computation of a keypoint position, the time needed for the preparation of a descriptor, and the classification accuracy of the considered approaches. In total, we compared the outcomes of 12 keypoint location methods and 10 keypoint descriptors. The best scores recorded for our database were almost 96% for a combination of the ORB method for keypoint localization followed by the BRISK approach as a descriptor. Full article

(This article belongs to the Special Issue Sensors and Pattern Recognition Methods for Security and Industrial Applications (SPR-SIA))

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13 pages, 1863 KiB

Open AccessArticle

Differential Diagnosis of Cysts and Granulomas Supported by Texture Analysis of Intraoral Radiographs

by Elżbieta Pociask, Karolina Nurzynska, Rafał Obuchowicz, Paulina Bałon, Daniel Uryga, Michał Strzelecki, Andrzej Izworski and Adam Piórkowski

Sensors 2021, 21(22), 7481; https://doi.org/10.3390/s21227481 - 10 Nov 2021

Cited by 11 | Viewed by 9106

Abstract

The aim of this study was to evaluate whether textural analysis could differentiate between the two common types of lytic lesions imaged with use of radiography. Sixty-two patients were enrolled in the study with intraoral radiograph images and a histological reference study. Full textural analysis was performed using MaZda software. For over 10,000 features, logistic regression models were applied. Fragments containing lesion edges were characterized by significant correlation of structural information. Although the input images were stored using lossy compression and their scale was not preserved, the obtained results confirmed the possibility of distinguishing between cysts and granulomas with use of textural analysis of intraoral radiographs. It was shown that the important information distinguishing the aforementioned types of lesions is located at the edges and not within the lesion. Full article

(This article belongs to the Special Issue Sensors and Pattern Recognition Methods for Security and Industrial Applications (SPR-SIA))

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11 pages, 45674 KiB

Open AccessArticle

Detection of Safe Passage for Trains at Rail Level Crossings Using Deep Learning

by Teresa Pamuła and Wiesław Pamuła

Sensors 2021, 21(18), 6281; https://doi.org/10.3390/s21186281 - 18 Sep 2021

Cited by 8 | Viewed by 3469

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 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

(This article belongs to the Special Issue Sensors and Pattern Recognition Methods for Security and Industrial Applications (SPR-SIA))

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20 pages, 3914 KiB

Open AccessArticle

Application of Feature Extraction Methods for Chemical Risk Classification in the Pharmaceutical Industry

by Mariusz Topolski

Sensors 2021, 21(17), 5753; https://doi.org/10.3390/s21175753 - 26 Aug 2021

Cited by 4 | Viewed by 2456

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 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

(This article belongs to the Special Issue Sensors and Pattern Recognition Methods for Security and Industrial Applications (SPR-SIA))

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20 pages, 1143 KiB

Open AccessArticle

The Proposition and Evaluation of the RoEduNet-SIMARGL2021 Network Intrusion Detection Dataset

by Maria-Elena Mihailescu, Darius Mihai, Mihai Carabas, Mikołaj Komisarek, Marek Pawlicki, Witold Hołubowicz and Rafał Kozik

Sensors 2021, 21(13), 4319; https://doi.org/10.3390/s21134319 - 24 Jun 2021

Cited by 29 | Viewed by 4399

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 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

(This article belongs to the Special Issue Sensors and Pattern Recognition Methods for Security and Industrial Applications (SPR-SIA))

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Review

Jump to: Editorial, Research

25 pages, 696 KiB

Open AccessReview

A Systematic Review of Recommender Systems and Their Applications in Cybersecurity

by Aleksandra Pawlicka, Marek Pawlicki, Rafał Kozik and Ryszard S. Choraś

Sensors 2021, 21(15), 5248; https://doi.org/10.3390/s21155248 - 3 Aug 2021

Cited by 30 | Viewed by 7614

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 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

(This article belongs to the Special Issue Sensors and Pattern Recognition Methods for Security and Industrial Applications (SPR-SIA))

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