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Systems
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Intelligent Information Technologies for Quality and Security Assurance
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Intelligent Information Technologies for Quality and Security Assurance

A special issue of Systems (ISSN 2079-8954).

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 25968

Special Issue Editors

Prof. Dr. Tetiana Hovorushchenko

E-Mail Website
Guest Editor
Department of Computer Engineering & Information Systems, Khmelnytskyi National University, Khmelnytskyi 29001, Ukraine
Interests: software quality assessment; intelligent agents based on the ontological approach; intelligent information systems and technologies
Special Issues, Collections and Topics in MDPI journals
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
Dr. Hakan Kutucu

E-Mail Website
Guest Editor
Department of Software Engineering, Karabuk University, Karabuk 78050, Turkey
Interests: network design problems; linear programming; combinatorial optimization; algorithm design; machine learning

Special Issue Information

Dear Colleagues,

At the current stage of development and implementation of information technology in various areas of human activity, decisive changes are taking place, as there are powerful technical resources for the accumulation and processing of large amounts of information. However, the application of known methods and tools to process such arrays of information does not meet the expectations of developers, and leads to the overuse of resources, the loss of significant information and conflicts between customer expectations and the results. At the same time, such areas of intellectualization of information and data processing as machine learning, cognitive computing, big data, deep learning, semantic WEB and others are in active development, which allow solving new classes of problems based on available information resources. All of the above are prerequisites for the transition to a new quality level of information processing, and, accordingly, for the creation and implementation of a new generation of information technologies. However, the specifics and features of the subject areas for which information technology is developed significantly affect the content and methods of information processing, so the expectation of universal approaches to creating effective information technology for different industries today is premature. The approach based on research of characteristics and features of subject branches and the development of new information technologies for concrete branches remains justified. 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 reliable protection of information from cyber threats and malware and quality assurance of software and computer systems. The need for quality and safety is based on the fact that errors and failures in software and computer systems and the impact of malware threaten disasters that lead to human casualties, environmental cataclysms, significant time losses and financial damage, or at least reputational damage to the company. Therefore, special attention in the direction of the development and implementation of effective information technologies is currently needed in the field of quality and security of software and computer systems. Achieving high-quality software and computer systems, as well as cybersecurity, is a key factor in their effective use and one of the main needs of customers. This Special Issue aims to disseminate and discuss artificial intelligence-based information technologies 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 quality and safety issues and offer reliable and accurate solutions.

Original, unpublished studies in different application areas on the following topics are welcome:

  • Intelligent Information Technologies for Software Engineering Domain;
  • Intelligent Information Technologies for Cybersecurity Domain;
  • Intelligent Information Technologies for Software Quality Assurance;
  • Intelligent Information Technologies for Software Security Assurance;
  • Intelligent Information Technologies for Computer Systems Quality Assurance;
  • Intelligent Information Technologies for Computer Systems Security Assurance;
  • Intelligent Information Technologies for Computer Systems Reliability;
  • Сross-Disciplinary Intelligent Information Technologies for Various Subject Areas.

Prof. Dr. Tetiana Hovorushchenko
Dr. Ivan Izonin
Dr. Hakan Kutucu
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. Systems 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 2400 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

  • intelligent information technologies
  • software engineering
  • computer systems
  • cybersecurity

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Published Papers (8 papers)

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Editorial

Jump to: Research, Review

4 pages, 268 KiB  
Editorial
Advancements in AI-Based Information Technologies: Solutions for Quality and Security
by Tetiana Hovorushchenko, Ivan Izonin and Hakan Kutucu
Systems 2024, 12(2), 58; https://doi.org/10.3390/systems12020058 - 9 Feb 2024
Cited by 3 | Viewed by 1604
Abstract
At the current stage of development and implementation of information technology in various areas of human activity, decisive changes are taking place, as there are powerful technical resources for the accumulation and processing of large amounts of information [...] Full article
(This article belongs to the Special Issue Intelligent Information Technologies for Quality and Security Assurance)

Research

Jump to: Editorial, Review

26 pages, 5771 KiB  
Article
Enhancing Smart IoT Malware Detection: A GhostNet-based Hybrid Approach
by Abdulwahab Ali Almazroi and Nasir Ayub
Systems 2023, 11(11), 547; https://doi.org/10.3390/systems11110547 - 11 Nov 2023
Cited by 4 | Viewed by 2738
Abstract
The Internet of Things (IoT) constitutes the foundation of a deeply interconnected society in which objects communicate through the Internet. This innovation, coupled with 5G and artificial intelligence (AI), finds application in diverse sectors like smart cities and advanced manufacturing. With increasing IoT [...] Read more.
The Internet of Things (IoT) constitutes the foundation of a deeply interconnected society in which objects communicate through the Internet. This innovation, coupled with 5G and artificial intelligence (AI), finds application in diverse sectors like smart cities and advanced manufacturing. With increasing IoT adoption comes heightened vulnerabilities, prompting research into identifying IoT malware. While existing models excel at spotting known malicious code, detecting new and modified malware presents challenges. This paper presents a novel six-step framework. It begins with eight malware attack datasets as input, followed by insights from Exploratory Data Analysis (EDA). Feature engineering includes scaling, One-Hot Encoding, target variable analysis, feature importance using MDI and XGBoost, and clustering with K-Means and PCA. Our GhostNet ensemble, combined with the Gated Recurrent Unit Ensembler (GNGRUE), is trained on these datasets and fine-tuned using the Jaya Algorithm (JA) to identify and categorize malware. The tuned GNGRUE-JA is tested on malware datasets. A comprehensive comparison with existing models encompasses performance, evaluation criteria, time complexity, and statistical analysis. Our proposed model demonstrates superior performance through extensive simulations, outperforming existing methods by around 15% across metrics like AUC, accuracy, recall, and hamming loss, with a 10% reduction in time complexity. These results emphasize the significance of our study’s outcomes, particularly in achieving cost-effective solutions for detecting eight malware strains. Full article
(This article belongs to the Special Issue Intelligent Information Technologies for Quality and Security Assurance)
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Figure 1

Figure 1
<p>Detailed Flowchart of the Proposed System Model.</p> Full article ">Figure 2
<p>Preprocessing workflow.</p> Full article ">Figure 3
<p>Optimized ensembler graphical representation.</p> Full article ">Figure 4
<p>Jaya Algorithm in terms of optimizing the GNGRUE weights (flowchart).</p> Full article ">Figure 5
<p>Exploring the Distribution of Malicious Features with Other Dependent Features: (<b>a</b>) Feature orig_byes distribution. (<b>b</b>) Feature duration.</p> Full article ">Figure 6
<p>Analyzing Outliers and Packet Counts Across Various Protocols.</p> Full article ">Figure 7
<p>Analyzing Feature Dependency with a Heat Map Correlation.</p> Full article ">Figure 8
<p>Relative Importance of Each Feature towards Target using MDI and XGBoost.</p> Full article ">Figure 9
<p>Utilizing Silhouette Scores for Distinct Group Identification through Clustering.</p> Full article ">Figure 10
<p>ROC Curve and Mapping True Positive as well as True Negative Values.</p> Full article ">Figure 11
<p>Accuracy Values between the Existing and Proposed Methods.</p> Full article ">Figure 12
<p>Comparison of MCC Values Between Existing and Proposed GNGRUE.</p> Full article ">Figure 13
<p>Time Complexity/Execution time of proposed and existing methods.</p> Full article ">Figure 14
<p>Time Complexity of different modules.</p> Full article ">
26 pages, 7643 KiB  
Article
Iot-Based Privacy-Preserving Anomaly Detection Model for Smart Agriculture
by Keerthi Kethineni and Pradeepini Gera
Systems 2023, 11(6), 304; https://doi.org/10.3390/systems11060304 - 13 Jun 2023
Cited by 13 | Viewed by 3332
Abstract
Internet of Things (IoT) technology has been incorporated into the majority of people’s everyday lives and places of employment due to the quick development in information technology. Modern agricultural techniques increasingly use the well-known and superior approach of managing a farm known as [...] Read more.
Internet of Things (IoT) technology has been incorporated into the majority of people’s everyday lives and places of employment due to the quick development in information technology. Modern agricultural techniques increasingly use the well-known and superior approach of managing a farm known as “smart farming”. Utilizing a variety of information and agricultural technologies, crops are observed for their general health and productivity. This requires monitoring the condition of field crops and looking at many other indicators. The goal of smart agriculture is to reduce the amount of money spent on agricultural inputs while keeping the quality of the final product constant. The Internet of Things (IoT) has made smart agriculture possible through data collection and storage techniques. For example, modern irrigation systems use effective sensor networks to collect field data for the best plant irrigation. Smart agriculture will become more susceptible to cyber-attacks as its reliance on the IoT ecosystem grows, because IoT networks have a large number of nodes but limited resources, which makes security a difficult issue. Hence, it is crucial to have an intrusion detection system (IDS) that can address such challenges. In this manuscript, an IoT-based privacy-preserving anomaly detection model for smart agriculture has been proposed. The motivation behind this work is twofold. Firstly, ensuring data privacy in IoT-based agriculture is of the utmost importance due to the large volumes of sensitive information collected by IoT devices, including on environmental conditions, crop health, and resource utilization data. Secondly, the timely detection of anomalies in smart agriculture systems is critical to enable proactive interventions, such as preventing crop damage, optimizing resource allocation, and ensuring sustainable farming practices. In this paper, we propose a privacy-encoding-based enhanced deep learning framework for the difficulty of data encryption and intrusion detection. In terms of data encoding, a novel method of a sparse capsule-auto encoder (SCAE) is proposed along with feature selection, feature mapping, and feature normalization. An SCAE is used to convert information into a new encrypted format in order to prevent deduction attacks. An attention-based gated recurrent unit neural network model is proposed to detect the intrusion. An AGRU is an advanced version of a GRU which is enhanced by an attention mechanism. In the results section, the proposed model is compared with existing deep learning models using two public datasets. Parameters such as recall, precision, accuracy, and F1-score are considered. The proposed model has accuracy, recall, precision, and F1-score of 99.9%, 99.7%, 99.9%, and 99.8%, respectively. The proposed method is compared using a variety of machine learning techniques such as the deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM). Full article
(This article belongs to the Special Issue Intelligent Information Technologies for Quality and Security Assurance)
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<p>Framework of proposed model.</p> Full article ">Figure 2
<p>The structure of an auto encoder.</p> Full article ">Figure 3
<p>Structure of CAE.</p> Full article ">Figure 4
<p>Gated recurrent unit neural network.</p> Full article ">Figure 5
<p>The loss and accuracy of IoT Botnet.</p> Full article ">Figure 6
<p>The loss and accuracy of ToN-IoT.</p> Full article ">Figure 7
<p>Confusion matrix of the PEDL using IoT Botnet and ToN-IoT datasets.</p> Full article ">Figure 8
<p>ROC curve of PEDL utilizing ToN-IoT and IoT Botnet datasets.</p> Full article ">Figure 9
<p>(<b>a</b>) Accuracy, (<b>b</b>) detection rate, (<b>c</b>) F1-score, and (<b>d</b>) precision of IoT Botnet dataset.</p> Full article ">Figure 10
<p>(<b>a</b>) Accuracy, (<b>b</b>) detection rate, (<b>c</b>) F1-score, and (<b>d</b>) precision of the ToN-IoT dataset.</p> Full article ">Figure 10 Cont.
<p>(<b>a</b>) Accuracy, (<b>b</b>) detection rate, (<b>c</b>) F1-score, and (<b>d</b>) precision of the ToN-IoT dataset.</p> Full article ">Figure 11
<p>(<b>a</b>–<b>d</b>) Performance comparison of different RNNs with the proposed method. (<b>a</b>) Accuracy in IoT Botnet dataset. (<b>b</b>) F-measure in IoT Botnet dataset. (<b>c</b>) Accuracy in ToN-IoT dataset. (<b>d</b>) F-measure in ToN-IoT dataset.</p> Full article ">Figure 11 Cont.
<p>(<b>a</b>–<b>d</b>) Performance comparison of different RNNs with the proposed method. (<b>a</b>) Accuracy in IoT Botnet dataset. (<b>b</b>) F-measure in IoT Botnet dataset. (<b>c</b>) Accuracy in ToN-IoT dataset. (<b>d</b>) F-measure in ToN-IoT dataset.</p> Full article ">Figure 12
<p>Performance analysis of proposed feature encoding model with other methods. (<b>a</b>) Accuracy in IoT Botnet dataset. (<b>b</b>) F-measure in IoT Botnet dataset. (<b>c</b>) Accuracy in ToN-IoT dataset. (<b>d</b>) F-measure in ToN-IoT dataset.</p> Full article ">Figure 12 Cont.
<p>Performance analysis of proposed feature encoding model with other methods. (<b>a</b>) Accuracy in IoT Botnet dataset. (<b>b</b>) F-measure in IoT Botnet dataset. (<b>c</b>) Accuracy in ToN-IoT dataset. (<b>d</b>) F-measure in ToN-IoT dataset.</p> Full article ">Figure 13
<p>Performance analysis of time complexity.</p> Full article ">
16 pages, 2572 KiB  
Article
Identifying the Mutual Correlations and Evaluating the Weights of Factors and Consequences of Mobile Application Insecurity
by Elena Zaitseva, Tetiana Hovorushchenko, Olga Pavlova and Yurii Voichur
Systems 2023, 11(5), 242; https://doi.org/10.3390/systems11050242 - 12 May 2023
Cited by 4 | Viewed by 1602
Abstract
Currently, there is a contradiction between the growing number of mobile applications in use and the responsibility that is placed on them, on the one hand, and the imperfection of the methods and tools for ensuring the security of mobile applications, on the [...] Read more.
Currently, there is a contradiction between the growing number of mobile applications in use and the responsibility that is placed on them, on the one hand, and the imperfection of the methods and tools for ensuring the security of mobile applications, on the other hand. Therefore, ensuring the security of mobile applications by developing effective methods and tools is a challenging task today. This study aims to evaluate the mutual correlations and weights of factors and consequences of mobile application insecurity. We have developed a method of evaluating the weights of factors of mobile application insecurity, which, taking into account the mutual correlations of mobile application insecurity consequences from these factors, determines the weights of the factors and allows us to conclude which factors are necessary to identify and accurately determine (evaluate) to ensure an appropriate level of reliability of forecasting and assess the security of mobile applications. The experimental results of our research are the evaluation of the weights of ten OWASP mobile application insecurity factors the identification of the mutual correlations of the consequences of mobile applications’ insecurity from these factors, and the identification of common factors on which more than one consequence depends. Full article
(This article belongs to the Special Issue Intelligent Information Technologies for Quality and Security Assurance)
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<p>The Frequency of Threat Factors Influences on the Security of Mobile Applications [<a href="#B17-systems-11-00242" class="html-bibr">17</a>,<a href="#B18-systems-11-00242" class="html-bibr">18</a>].</p> Full article ">Figure 2
<p>Diagram of Consequences of Mobile Application Insecurity Dependency on Factors That Affect Mobile Application Security.</p> Full article ">Figure 3
<p>Diagram of the Correlations Between Factors and Consequences.</p> Full article ">Figure 4
<p>Consequences Caused by the Largest Number of Threat Factors: (<b>a</b>) Unauthorized Access to Data is Caused by 4 Threat Factors; (<b>b</b>) Reputation Damage is Caused by 8 Threat Factors.</p> Full article ">
14 pages, 1958 KiB  
Article
High-Performance Artificial Intelligence Recommendation of Quality Research Papers Using Effective Collaborative Approach
by Vinoth Kumar Venkatesan, Mahesh Thyluru Ramakrishna, Anatoliy Batyuk, Andrii Barna and Bohdana Havrysh
Systems 2023, 11(2), 81; https://doi.org/10.3390/systems11020081 - 4 Feb 2023
Cited by 28 | Viewed by 4573
Abstract
The Artificial Intelligence Recommender System has emerged as a significant research interest. It aims at helping users find things online by offering recommendations that closely fit their interests. Recommenders for research papers have appeared over the last decade to make it easier to [...] Read more.
The Artificial Intelligence Recommender System has emerged as a significant research interest. It aims at helping users find things online by offering recommendations that closely fit their interests. Recommenders for research papers have appeared over the last decade to make it easier to find publications associated with the field of researchers’ interests. However, due to several issues, such as copyright constraints, these methodologies assume that the recommended articles’ contents are entirely openly accessible, which is not necessarily the case. This work demonstrates an efficient model, known as RPRSCA: Research Paper Recommendation System Using Effective Collaborative Approach, to address these uncertain systems for the recommendation of quality research papers. We make use of contextual metadata that are publicly available to gather hidden relationships between research papers in order to personalize recommendations by exploiting the advantages of collaborative filtering. The proposed system, RPRSCA, is unique and gives personalized recommendations irrespective of the research subject. Thus, a novel collaborative approach is proposed that provides better performance. Using a publicly available dataset, we found that our proposed method outperformed previous uncertain methods in terms of overall performance and the capacity to return relevant, valuable, and quality publications at the top of the recommendation list. Furthermore, our proposed strategy includes personalized suggestions and customer expertise, in addition to addressing multi-disciplinary concerns. Full article
(This article belongs to the Special Issue Intelligent Information Technologies for Quality and Security Assurance)
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<p>Proposed Scenario of Recommendation.</p> Full article ">Figure 2
<p>Performance of precision.</p> Full article ">Figure 3
<p>Performance of recall.</p> Full article ">Figure 4
<p>Performance of F1-score.</p> Full article ">Figure 5
<p>Performance of MAP (mean average precision).</p> Full article ">Figure 6
<p>Performance of MRR (mean reciprocal rank).</p> Full article ">
15 pages, 2465 KiB  
Article
Encipher GAN: An End-to-End Color Image Encryption System Using a Deep Generative Model
by Kirtee Panwar, Akansha Singh, Sonal Kukreja, Krishna Kant Singh, Nataliya Shakhovska and Andrii Boichuk
Systems 2023, 11(1), 36; https://doi.org/10.3390/systems11010036 - 7 Jan 2023
Cited by 13 | Viewed by 4021
Abstract
Chaos-based image encryption schemes are applied widely for their cryptographic properties. However, chaos and cryptographic relations remain a challenge. The chaotic systems are defined on the set of real numbers and then normalized to a small group of integers in the range 0–255, [...] Read more.
Chaos-based image encryption schemes are applied widely for their cryptographic properties. However, chaos and cryptographic relations remain a challenge. The chaotic systems are defined on the set of real numbers and then normalized to a small group of integers in the range 0–255, which affects the security of such cryptosystems. This paper proposes an image encryption system developed using deep learning to realize the secure and efficient transmission of medical images over an insecure network. The non-linearity introduced with deep learning makes the encryption system secure against plaintext attacks. Another limiting factor for applying deep learning in this area is the quality of the recovered image. The application of an appropriate loss function further improves the quality of the recovered image. The loss function employs the structure similarity index metric (SSIM) to train the encryption/decryption network to achieve the desired output. This loss function helped to generate cipher images similar to the target cipher images and recovered images similar to the originals concerning structure, luminance and contrast. The images recovered through the proposed decryption scheme were high-quality, which was further justified by their PSNR values. Security analysis and its results explain that the proposed model provides security against statistical and differential attacks. Comparative analysis justified the robustness of the proposed encryption system. Full article
(This article belongs to the Special Issue Intelligent Information Technologies for Quality and Security Assurance)
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<p>Image cryptography and cryptanalysis.</p> Full article ">Figure 2
<p>Deep-learning-based image encryption.</p> Full article ">Figure 3
<p>Flow diagram of the encryption process.</p> Full article ">Figure 4
<p>Encryption/decryption network.</p> Full article ">Figure 5
<p>Discriminator model.</p> Full article ">Figure 6
<p>Plain image. (<b>a</b>) Image 1. (<b>b</b>) Cipher Image 1; (<b>c</b>) Cipher Image 2—generated with different sets of keys.</p> Full article ">Figure 7
<p>Plain image. (<b>a</b>) Image 1, (<b>b</b>) Image 2, (<b>c</b>) Image 3. (<b>d</b>) Cipher Image 1. (<b>e</b>) Cipher Image 2. (<b>f</b>) Cipher Image 3.</p> Full article ">Figure 8
<p>Histogram of plain images (<b>a</b>–<b>c</b>) and corresponding cipher images (<b>d</b>–<b>f</b>).</p> Full article ">Figure 9
<p>Adjacent pixel correlations (horizontal, vertical and diagonal directions) of plain images (<b>a</b>–<b>c</b>) and corresponding cipher images (<b>d</b>–<b>f</b>).</p> Full article ">Figure 10
<p>Comparison of the quality of encrypted images as training proceeded: (<b>a</b>) Original Image; (<b>b</b>) Cipher Image (epoch 1); (<b>c</b>) Cipher Image (epoch 10); (<b>d</b>) Cipher Image (epoch 40); (<b>e</b>) Cipher Image (epoch 100).</p> Full article ">Figure 11
<p>Quality of recovered images generated with the decryption network: (<b>a</b>) Original Image; (<b>b</b>) Encrypted Image of (<b>a</b>); (<b>c</b>) Recovered Image from (<b>b</b>); (<b>d</b>) Original Image; (<b>e</b>) Encrypted Image of (<b>d</b>); (<b>f</b>) Recovered Image from (<b>e</b>); (<b>g</b>) Original Image; (<b>h</b>) Encrypted Image of (<b>g</b>); (<b>i</b>) Recovered Image from (<b>h</b>); (Column 1 displays original images. Column 2 displays encrypted images generated with the encryption network. Column 3 shows recovered images generated with the decryption network.)</p> Full article ">
19 pages, 1791 KiB  
Article
Adaptive Artificial Bee Colony Algorithm for Nature-Inspired Cyber Defense
by Chirag Ganguli, Shishir Kumar Shandilya, Maryna Nehrey and Myroslav Havryliuk
Systems 2023, 11(1), 27; https://doi.org/10.3390/systems11010027 - 5 Jan 2023
Cited by 13 | Viewed by 3047
Abstract
With the significant growth of the cyber environment over recent years, defensive mechanisms against adversaries have become an important step in maintaining online safety. The adaptive defense mechanism is an evolving approach that, when combined with nature-inspired algorithms, allows users to effectively run [...] Read more.
With the significant growth of the cyber environment over recent years, defensive mechanisms against adversaries have become an important step in maintaining online safety. The adaptive defense mechanism is an evolving approach that, when combined with nature-inspired algorithms, allows users to effectively run a series of artificial intelligence-driven tests on their customized networks to detect normal and under attack behavior of the nodes or machines attached to the network. This includes a detailed analysis of the difference in the throughput, end-to-end delay, and packet delivery ratio of the nodes before and after an attack. In this paper, we compare the behavior and fitness of the nodes when nodes under a simulated attack are altered, aiding several nature-inspired cyber security-based adaptive defense mechanism approaches and achieving clear experimental results. The simulation results show the effectiveness of the fitness of the nodes and their differences through a specially crafted metric value defined using the network performance statistics and the actual throughput difference of the attacked node before and after the attack. Full article
(This article belongs to the Special Issue Intelligent Information Technologies for Quality and Security Assurance)
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<p>Network architecture [<a href="#B8-systems-11-00027" class="html-bibr">8</a>].</p> Full article ">Figure 2
<p>Case 1: metric composition.</p> Full article ">Figure 3
<p>Case 1: fitness.</p> Full article ">Figure 4
<p>Case 2: metric composition.</p> Full article ">Figure 5
<p>Case 2: fitness.</p> Full article ">Figure 6
<p>Case 3: metric composition.</p> Full article ">Figure 7
<p>Case 3: fitness.</p> Full article ">Figure 8
<p>Case 4: metric composition.</p> Full article ">Figure 9
<p>Case 4: fitness.</p> Full article ">Figure 10
<p>Case 5: metric composition.</p> Full article ">Figure 11
<p>Case 5: fitness.</p> Full article ">Figure 12
<p>Case 1: average throughput.</p> Full article ">Figure 13
<p>Case 1: intrusion chart and fitness.</p> Full article ">Figure 14
<p>Case 2: average throughput.</p> Full article ">Figure 15
<p>Case 2: intrusion chart and fitness.</p> Full article ">Figure 16
<p>Case 3: average throughput.</p> Full article ">Figure 17
<p>Case 3: intrusion chart and fitness.</p> Full article ">

Review

Jump to: Editorial, Research

26 pages, 3749 KiB  
Review
Video Synopsis Algorithms and Framework: A Survey and Comparative Evaluation
by Palash Yuvraj Ingle and Young-Gab Kim
Systems 2023, 11(2), 108; https://doi.org/10.3390/systems11020108 - 17 Feb 2023
Cited by 5 | Viewed by 3295
Abstract
With the increase in video surveillance data, techniques such as video synopsis are being used to construct small videos for analysis, thereby saving storage resources. The video synopsis framework applies in real-time environments, allowing for the creation of synopsis between multiple and single-view [...] Read more.
With the increase in video surveillance data, techniques such as video synopsis are being used to construct small videos for analysis, thereby saving storage resources. The video synopsis framework applies in real-time environments, allowing for the creation of synopsis between multiple and single-view cameras; the same framework encompasses optimization, extraction, and object detection algorithms. Contemporary state-of-the-art synopsis frameworks are suitable only for particular scenarios. This paper aims to review the traditional state-of-the-art video synopsis techniques and understand the different methods incorporated in the methodology. A comprehensive review provides analysis of varying video synopsis frameworks and their components, along with insightful evidence for classifying these techniques. We primarily investigate studies based on single-view and multiview cameras, providing a synopsis and taxonomy based on their characteristics, then identifying and briefly discussing the most commonly used datasets and evaluation metrics. At each stage of the synopsis framework, we present new trends and open challenges based on the obtained insights. Finally, we evaluate the different components such as object detection, tracking, optimization, and stitching techniques on a publicly available dataset and identify the lacuna among the different algorithms based on experimental results. Full article
(This article belongs to the Special Issue Intelligent Information Technologies for Quality and Security Assurance)
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<p>A taxonomy of video synopsis techniques and their properties.</p> Full article ">Figure 2
<p>Chronological overview of the most relevant video synopsis studies. The chronology represents the names of the author and the respective timeline of their study.</p> Full article ">Figure 3
<p>An illustration of different synopsis methodologies and their components: (<b>a</b>) single-camera video synopsis framework; (<b>b</b>) multi-camera video synopsis framework; (<b>c</b>) abnormal content video synopsis framework.</p> Full article ">Figure 4
<p>Illustration of different synopsis methodologies for generating foreground segmentation.</p> Full article ">
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