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Electronics
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Recent Advanced Technologies and Applications of Smart Computing and Cyber...
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Recent Advanced Technologies and Applications of Smart Computing and Cyber Security

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 52862

Special Issue Editors

Dr. Satyabrata Aich

E-Mail Website
Guest Editor
Department of Computer Engineering, Inje University, Gimhae 197, Korea
Interests: artificial intelligence; blockchain; biomedical signal processing; machine learning; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Madhusanka Liyanage

E-Mail Website
Guest Editor
School of computer science, University College Dublin, Belfield, Dublin 4, Ireland
Interests: 5G; blockchain; network security; virtual networks; security protocols; software-defined networking (SDN); Internet of Things (IoT); multi-access edge computing (MEC)
Special Issues, Collections and Topics in MDPI journals
Dr. Bruce Ndibanje

E-Mail Website
Guest Editor
Vulnerability Management Team, Department of Information Technology, World Food Programme Rome, 77835 Latium, Italy
Interests: computer networking; cyber security; information security; cryptography; IT security; computer network security; cybersecurity; deep learning

Special Issue Information

Dear Colleagues,

Today, smart computing has become a part of our daily life, where advanced computational methodologies and technologies both become too complex and sophisticated. Smart computing, combined with a technical approach, creates systems, applications, and new service implementations that meet the needs of societies and cover many different application areas, including big data and cloud services, artificial intelligence and machine learning, the Internet of Things, health, energy, transport systems, environment, industrial systems, information retrieval, and creativity. The explosive growth of smart computing and the various technological advancements create many scientific and engineering challenges that call for ingenious research efforts from both academia and industry. In all these areas, innovation requires both conceiving of new applications and services and improving the efficiency, reliability, and sustainability of existing ones. In the digital era, cybersecurity is a part of our day-to-day life to protect systems, networks, and programs from digital attacks. Innumerable organizations and companies frequently experience damage due to data breaches and the inability to execute their operations due to cyberattacks. Public services and research institutions are also constantly affected by the same issues. We are inviting submissions that explore the defensive tools and mechanisms that deal with cybersecurity and at the same time assess exposure to cyberattacks and that promote a tradeoff between security, costs, and usability that is more aligned with the public interest. Both theoretical and experimental studies are welcome, as well as case studies, papers dealing with the systematization of knowledge, and survey papers. This Special Issue is to bring together scholars, professors, researchers, engineers, and administrators resorting to state-of-the-art technologies and ideas to significantly improve the field of smart computing and cybersecurity, including but not limited to the following: 

  • Smart computing;
  • IoT applications in healthcare, wearables, smart cities, agriculture;
  • Big data and cloud computing;
  • Artificial intelligence and machine learning;
  • Computational intelligence methodologies;
  • Mobile and wireless security;
  • Network security;
  • cybersecurity data analytics;
  • cybersecurity applications;
  • Blockchain applications in cybersecurity.

Dr. Satyabrata Aich
Prof. Dr. Madhusanka Liyanage
Dr. Bruce Ndibanje
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. Electronics 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 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

  • Smart computing
  • IoT
  • AI and ML
  • 5G/6G
  • Blockchain
  • Cybersecurity
  • Big data
  • Cloud computing
  • Wearables
  • Data analytics

Published Papers (7 papers)

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Research

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15 pages, 2217 KiB  
Article
Application of Model-Based Software Testing in the Health Care Domain
by Pragya Jha, Madhusmita Sahu, Sukant Kishoro Bisoy and Mangal Sain
Electronics 2022, 11(13), 2062; https://doi.org/10.3390/electronics11132062 - 30 Jun 2022
Cited by 1 | Viewed by 3333
Abstract
The human body’s reaction to various therapeutic medications is critical to comprehend since it aids in the appropriate construction of automated decision support systems for healthcare. Healthcare Internet of Things (IoT) solutions are becoming more accessible and trusted, necessitating more testing before they [...] Read more.
The human body’s reaction to various therapeutic medications is critical to comprehend since it aids in the appropriate construction of automated decision support systems for healthcare. Healthcare Internet of Things (IoT) solutions are becoming more accessible and trusted, necessitating more testing before they are standardized for commercial usage. We have developed an activity diagram based on the Unified Modeling Language (UML) to represent acceptability testing in IoT systems. The activity flow graph is used to extract all of the necessary information by traversing the activity flow diagram from start to finish, displaying all its properties. In this paper, a test case is generated to compute the type of diabetes using blood sugar test results, estimate the kind of diabetes, and the probability that a person would get diabetes in the future. We have demonstrated how these test cases can function using a telehealth care case study. First, we offer a high-level overview of the topic as well as a design model working diagram. The test case creation method is then outlined using the activity diagram as a guide. Full article
(This article belongs to the Special Issue Recent Advanced Technologies and Applications of Smart Computing and Cyber Security)
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15 pages, 3738 KiB  
Article
A Comprehensive Cost Analysis of Intra-Domain Handoff with Authentication Cost in PMIPv6 for Vehicular Ad Hoc Networks (VANETs)
by Amit Kumar Goyal, Gaurav Agarwal, Arun Kumar Tripathi, Vikas Goel, Girish Sharma, Kueh Lee Hui and Mangal Sain
Electronics 2022, 11(10), 1625; https://doi.org/10.3390/electronics11101625 - 19 May 2022
Cited by 2 | Viewed by 1609
Abstract
In metro cities, the effective and efficient management of traffic is one of the most demanding and time taking tasks. Vehicular ad hoc networks (VANET) provide unfailing, low-cost solutions for transportation systems with intelligence. VANET, a subclass of mobile ad hoc networks (MANET), [...] Read more.
In metro cities, the effective and efficient management of traffic is one of the most demanding and time taking tasks. Vehicular ad hoc networks (VANET) provide unfailing, low-cost solutions for transportation systems with intelligence. VANET, a subclass of mobile ad hoc networks (MANET), allows exchange of information among vehicles and/or roadside devices. VANET can be implemented in numerous application areas such as effective traffic management, safety, and user comfort for drivers as well as passengers. It provides phenomenal growth to both in industries and research communities. Secure mobility and handoff management are the most promising and challenging research issues in VANET. In this paper, we have introduced the security in intra domain mobility handoff in PMIPv6 for VANET. Existing intra handover schemes does not include the authentication cost while evaluating the total packet delivery cost for intra domain handoff. Our proposed scheme includes the authentication cost of an intra-domain handover for evaluating the total packet delivery cost of handover for next-generation mobility management protocols, which is PMIPv6 for the vehicular network. We have considered the vital parameters such as the number of MAGs, setup cost, binding update cost, unit transmission cost for analyzing the total packet delivery cost. Furthermore, a comparative study with authentication and without authentication cost for the considered parameters shows that our proposed scheme secures the handover process with slight variation in cost. Full article
(This article belongs to the Special Issue Recent Advanced Technologies and Applications of Smart Computing and Cyber Security)
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38 pages, 5310 KiB  
Article
IoDM: A Study on a IoT-Based Organizational Deception Modeling with Adaptive General-Sum Game Competition
by Sang Seo and Dohoon Kim
Electronics 2022, 11(10), 1623; https://doi.org/10.3390/electronics11101623 - 19 May 2022
Cited by 3 | Viewed by 1876
Abstract
Moving target defense (MTD) and decoy strategies, measures of active defense, were introduced to secure both the proactive security and reactive adaptability of internet-of-things (IoT) networks that have been explosively applied to various industries without any strong security measures and to mitigate the [...] Read more.
Moving target defense (MTD) and decoy strategies, measures of active defense, were introduced to secure both the proactive security and reactive adaptability of internet-of-things (IoT) networks that have been explosively applied to various industries without any strong security measures and to mitigate the side effects of threats. However, the existing MTD and decoy strategies are limited to avoiding the attacker’s reconnaissance and initial intrusion attempts through simple structural mutations or inducing the attackers to a static trap based on the deceptive path and lack approaches to adaptively optimize IoT in consideration of the unique characteristic information by the domain of IoT. Game theory-based and decoy strategies are other options; however, they do not consider the dynamicity and uncertainty of the decision-making stages by the organizational agent related to the IoT domains. Therefore, in this paper, we present a type of organizational deception modeling, namely IoT-based organizational deception modeling (IoDM), which considers both the dynamic topologies and organizational business fingerprints customized in the IoT domain and operational purpose. For this model, we considered the practical scalability of the existing IoT-enabled MTD and decoy concepts and formulated the partially incomplete deceptive decision-making modeling for the cyber-attack and defense competition for IoT in real-time based on the general-sum game. According to our experimental results, the efficiency of the deceptive defense of the IoT defender could be improved by 70% on average while deriving the optimal defense cost compared to the increased defense performance. The findings of this study will improve the deception performances of MTD and decoy strategies by IoT scenarios related to various operational domains such as smart home networks, industrial networks, and medical networks. To the best of our knowledge, this study has employed social-engineering IoT knowledge and general-sum game theory for the first time. Full article
(This article belongs to the Special Issue Recent Advanced Technologies and Applications of Smart Computing and Cyber Security)
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26 pages, 4884 KiB  
Article
AEI-DNET: A Novel DenseNet Model with an Autoencoder for the Stock Market Predictions Using Stock Technical Indicators
by Saleh Albahli, Tahira Nazir, Awais Mehmood, Aun Irtaza, Ali Alkhalifah and Waleed Albattah
Electronics 2022, 11(4), 611; https://doi.org/10.3390/electronics11040611 - 16 Feb 2022
Cited by 17 | Viewed by 3652
Abstract
Predicting stock market prices is an important and interesting task in academic and financial research. The volatile nature of the stock market means that predicting stock market prices is a challenging task. However, recent advancements in machine learning, especially in deep learning techniques, [...] Read more.
Predicting stock market prices is an important and interesting task in academic and financial research. The volatile nature of the stock market means that predicting stock market prices is a challenging task. However, recent advancements in machine learning, especially in deep learning techniques, have made it possible for researchers to use such techniques to predict future stock trends based on historical financial data, social media news, financial news, and stock technical indicators (STIs). This work focused on the prediction of closing stock prices based on using ten years of Yahoo Finance data of ten renowned stocks and STIs by using 1D DenseNet and an autoencoder. The calculated STIs were first used as the input for the autoencoder for dimensionality reduction, resulting in less correlation between the STIs. These STIs, along with the Yahoo finance data, were then fed into the 1D DenseNet. The resultant features obtained from the 1D DenseNet were then used as input for the softmax layer residing inside the 1D DenseNet framework for the prediction of closing stock prices for short-, medium-, and long-term perspectives. Based on the predicted trends of the stock prices, our model presented the user with one of three suggested signals, i.e., buy, sell, or hold. The experimental results showed that the proposed approach outperformed the state-of-the-art techniques by obtaining a minimum MAPE value of 0.41. Full article
(This article belongs to the Special Issue Recent Advanced Technologies and Applications of Smart Computing and Cyber Security)
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21 pages, 3475 KiB  
Article
Bytecode Similarity Detection of Smart Contract across Optimization Options and Compiler Versions Based on Triplet Network
by Di Zhu, Feng Yue, Jianmin Pang, Xin Zhou, Wenjie Han and Fudong Liu
Electronics 2022, 11(4), 597; https://doi.org/10.3390/electronics11040597 - 15 Feb 2022
Cited by 5 | Viewed by 3634
Abstract
In recent years, the number of smart contracts running in the blockchain has increased rapidly, accompanied by many security problems, such as vulnerability propagation caused by code reuse or vicious transaction caused by malicious contract deployment, for example. Most smart contracts do not [...] Read more.
In recent years, the number of smart contracts running in the blockchain has increased rapidly, accompanied by many security problems, such as vulnerability propagation caused by code reuse or vicious transaction caused by malicious contract deployment, for example. Most smart contracts do not publish the source code, but only the bytecode. Based on the research of bytecode similarity of smart contract, smart contract upgrade, vulnerability search and malicious contract analysis can be carried out. The difficulty of bytecode similarity research is that different compilation versions and optimization options lead to the diversification of bytecode of the same source code. This paper presents a solution, including a series of methods to measure the similarity of smart contract bytecode. Starting from the opcode of smart contract, a method of pre-training the basic block sequence of smart contract is proposed, which can embed the basic block vector. Positive samples were obtained by basic block marking, and the negative sampling method is improved. After these works, we put the obtained positive samples, negative samples and basic blocks themselves into the triplet network composed of transformers. Our solution can obtain evaluation results with an accuracy of 97.8%, so that the basic block sequence of optimized and unoptimized options can be transformed into each other. At the same time, the instructions are normalized, and the order of compiled version instructions is normalized. Experiments show that our solution can effectively reduce the bytecode difference caused by optimization options and compiler version, and improve the accuracy by 1.4% compared with the existing work. We provide a data set covering 64 currently used Solidity compilers, including one million basic block pairs extracted from them. Full article
(This article belongs to the Special Issue Recent Advanced Technologies and Applications of Smart Computing and Cyber Security)
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22 pages, 3690 KiB  
Article
A Machine Learning Method for Detection of Surface Defects on Ceramic Tiles Using Convolutional Neural Networks
by Okeke Stephen, Uchenna Joseph Maduh and Mangal Sain
Electronics 2022, 11(1), 55; https://doi.org/10.3390/electronics11010055 - 24 Dec 2021
Cited by 19 | Viewed by 4578
Abstract
We propose a simple but effective convolutional neural network to learn the similarities between closely related raw pixel images for feature representation extraction and classification through the initialization of convolutional kernels from learned filter kernels of the network. The binary-class classification of sigmoid [...] Read more.
We propose a simple but effective convolutional neural network to learn the similarities between closely related raw pixel images for feature representation extraction and classification through the initialization of convolutional kernels from learned filter kernels of the network. The binary-class classification of sigmoid and discriminative feature vectors are simultaneously learned together contrasting the handcrafted traditional method of feature extractions, which split feature-extraction and classification tasks into two different processes during training. Relying on the high-quality feature representation learned by the network, the classification tasks can be efficiently conducted. We evaluated the classification performance of our proposed method using a collection of tile surface images consisting of cracked surfaces and no-cracked surfaces. We tried to classify the tiny-cracked surfaces from non-crack normal tile demarcations, which could be useful for automated visual inspections that are labor intensive, risky in high altitudes, and time consuming with manual inspection methods. We performed a series of comparisons on the results obtained by varying the optimization, activation functions, and deployment of different data augmentation methods in our network architecture. By doing this, the effectiveness of the presented model for smooth surface defect classification was explored and determined. Through extensive experimentation, we obtained a promising validation accuracy and minimal loss. Full article
(This article belongs to the Special Issue Recent Advanced Technologies and Applications of Smart Computing and Cyber Security)
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25 pages, 2015 KiB  
Review
Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions
by Nusrat Rouf, Majid Bashir Malik, Tasleem Arif, Sparsh Sharma, Saurabh Singh, Satyabrata Aich and Hee-Cheol Kim
Electronics 2021, 10(21), 2717; https://doi.org/10.3390/electronics10212717 - 08 Nov 2021
Cited by 52 | Viewed by 32628
Abstract
With the advent of technological marvels like global digitization, the prediction of the stock market has entered a technologically advanced era, revamping the old model of trading. With the ceaseless increase in market capitalization, stock trading has become a center of investment for [...] Read more.
With the advent of technological marvels like global digitization, the prediction of the stock market has entered a technologically advanced era, revamping the old model of trading. With the ceaseless increase in market capitalization, stock trading has become a center of investment for many financial investors. Many analysts and researchers have developed tools and techniques that predict stock price movements and help investors in proper decision-making. Advanced trading models enable researchers to predict the market using non-traditional textual data from social platforms. The application of advanced machine learning approaches such as text data analytics and ensemble methods have greatly increased the prediction accuracies. Meanwhile, the analysis and prediction of stock markets continue to be one of the most challenging research areas due to dynamic, erratic, and chaotic data. This study explains the systematics of machine learning-based approaches for stock market prediction based on the deployment of a generic framework. Findings from the last decade (2011–2021) were critically analyzed, having been retrieved from online digital libraries and databases like ACM digital library and Scopus. Furthermore, an extensive comparative analysis was carried out to identify the direction of significance. The study would be helpful for emerging researchers to understand the basics and advancements of this emerging area, and thus carry-on further research in promising directions. Full article
(This article belongs to the Special Issue Recent Advanced Technologies and Applications of Smart Computing and Cyber Security)
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