Systems

Editorial

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4 pages, 268 KiB

Open AccessEditorial

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 1589

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

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26 pages, 5771 KiB

Open AccessArticle

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 2677

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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26 pages, 7643 KiB

Open AccessArticle

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 3292

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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16 pages, 2572 KiB

Open AccessArticle

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 1583

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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14 pages, 1958 KiB

Open AccessArticle

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 4532

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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15 pages, 2465 KiB

Open AccessArticle

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 3970

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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19 pages, 1791 KiB

Open AccessArticle

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 3010

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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Review

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26 pages, 3749 KiB

Open AccessReview

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 3264

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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