Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.9
Journals
Electronics
Special Issues
Security and Privacy in IoT Enabled Modern Applications Using Deep/Machine...
share announcement

Security and Privacy in IoT Enabled Modern Applications Using Deep/Machine Learning and Blockchain Technology

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

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 34433

Special Issue Editors

Dr. Piyush Kumar Shukla

E-Mail Website
Guest Editor
Department of Computer Science, University Institute of Technology, RGPV Bhopal, Bhopal 462033, India
Interests: white box cryptography; information security; privacy; cyber security; dynamic wireless networks; blockchain; machine learning; IoT; image processing; medical imaging; FANET
Special Issues, Collections and Topics in MDPI journals
Dr. Manoj Kumar

E-Mail Website
Guest Editor
School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India
Interests: image processing; digital forensics; IOT; WSN; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaochun Cheng

E-Mail Website
Guest Editor
Department of Computer Science, Middlesex University, London NW4 4BT, UK
Interests: information security; artificial intelligence; cognitive computing; intelligent internet systems; communications and information security
Special Issues, Collections and Topics in MDPI journals
Dr. Prashant Kumar Shukla

E-Mail Website
Guest Editor
Department of Computer Science & Engineering, K L University, Vijaywada, Andhra Pradesh, India
Interests: machine learning; IoT; CPS; blockchain; wireless network security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The security and privacy of users have become significant concerns due to the involvement of the Internet of Things (IoT) devices in numerous applications. Cyber threats are growing at an explosive pace, making the existing security and privacy measures inadequate. Hence, everyone on the Internet is a potential victim of hackers. Consequently, Machine Learning (ML) algorithms are used to produce accurate outputs from large complex databases. The generated results can be used to predict and detect vulnerabilities in IoT-based systems.

Furthermore, Blockchain (BC) techniques are becoming popular in modern IoT applications to solve security and privacy issues. Several studies have been conducted on either ML algorithms or BC techniques. However, these studies target either security or privacy issues using ML algorithms or BC techniques, thus posing a need for a combined survey on recent years' efforts to address both security and privacy issues in vulnerable IoT devices using ML algorithms and BC techniques.

In this Special Issue proposal, we have summarized the research areas to focus on and the title related to cutting-edge topics, addressing security and privacy issues using ML algorithms and BC techniques in the IoT domain. The Special Issue emphasizes the operational elements of the IoT using machine learning techniques that facilitate intelligence in security and privacy challenges in the real world and in cyberspace. Additionally, we will invite authors to submit case studies based on categorizing various security and privacy threats reported in the past few years (especially during the COVID-19 pandemic) in the IoT domain, along with original research papers as well as survey papers.

Dr. Piyush Kumar Shukla
Dr. Manoj Kumar
Dr. Xiaochun Cheng
Dr. Prashant Kumar Shukla
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

  • Formal or Interdisciplinary Architecture of IoT Comprising Machine Learning & Blockchain
  • Machine Learning-Based Prototype Security and Privacy Models for IoT Environment
  • Machine Learning-Based Blockchain-Enabled Techniques Adoption to Protect IoT Applications
  • Interdisciplinary Architecture of IoT using Machine Learning
  • Intelligent IoT Enabled Applications Using Blockchain-Based Machine Learning: Challenges and a Path Towards Success
  • Accomplishing Blockchain Architectures for Futuristic Internet of Things Security
  • Preserving the Internet of Things Against Attacks and Vulnerabilities
  • Blockchain-Based Secure Access Control in IoT Enabled Sustainable Systems
  • Identity management schemes on the block chain
  • Machine Learning & Blockchain-Enabled Data Integrity Approaches for Cyber-Physical Systems (CPS)
  • Privacy-Preservation for Collaborative IoT-Enabled Intrusion Detection Systems
  • Sustaining Data Integrity Against MITM & DoS Attacks With Distributed Mobile Management (DMM) in IoT & Blockchain-Based Intelligent Systems
  • Security Investment Assessment Methods using Primitive Cognitive Network for IoT & Blockchain
  • Anomaly Detection for Deep Learning Approach
  • CrowdSensing-Based IoT Solutions with Deep Learning for Smart Environment
  • Spoofing Attack Detection in IoT Environment With Reinforcement Learning, Deep Learning Along with Blockchain
  • Secure DeepChain-IoT: Deep Learning with Blockchain-based IoT Applications
  • Blockchain & Internet of Things for Machine Learning-based Malicious Application Detection Techniques
  • IoT-Enabled Devises Physical-Layer Security Applying Machine Learning & Blockchain Based Approaches
  • IoT-Cloud Authorization and Delegation for Smart & Intelligent Devices Using Blockchain

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 6717 KiB  
Article
Detecting the Presence of Malware and Identifying the Type of Cyber Attack Using Deep Learning and VGG-16 Techniques
by Abdullah I. A. Alzahrani, Manel Ayadi, Mashael M. Asiri, Amal Al-Rasheed and Amel Ksibi
Electronics 2022, 11(22), 3665; https://doi.org/10.3390/electronics11223665 - 9 Nov 2022
Cited by 6 | Viewed by 2737
Abstract
malware is malicious software (harmful program files) that targets and damage computers, devices, networks, and servers. Many types of malware exist, including worms, viruses, trojan horses, etc. With the increase in technology and devices every day, malware is significantly propagating more and more [...] Read more.
malware is malicious software (harmful program files) that targets and damage computers, devices, networks, and servers. Many types of malware exist, including worms, viruses, trojan horses, etc. With the increase in technology and devices every day, malware is significantly propagating more and more on a daily basis. The rapid growth in the number of devices and computers and the rise in technology is directly proportional to the number of malicious attacks—most of these attacks target organizations, customers, companies, etc. The main goal of these attacks is to steal critical data and passwords, blackmail, etc. The propagation of this malware may be performed through emails, infected files, connected peripherals such as flash drives and external disks, and malicious websites. Many types of research in artificial intelligence and machine learning fields have recently been released for malware detection. In this research work, we will focus on detecting malware using deep learning. We worked on a dataset that consisted of 8970 malware and 1000 non-malware (benign) executable files. The malware files were divided into five types in the dataset: Locker, Mediyes, Winwebsec, Zeroaccess, and Zbot. Those executable files were pre-processed and converted from raw data into images of size 224 * 224 * 3. This paper proposes a multi-stage architecture consisting of two modified VGG-19 models. The first model objective is to identify whether the input file is malicious or not, while the second model objective is to identify the type of malware if the file is detected as malware by the first model. The two models were trained on 80% of the data and tested on the remaining 20%. The first stage of the VGG-19 model achieved 99% accuracy on the testing set. The second stage using the VGG-19 model was responsible for detecting the type of malware (five different types in our dataset) and achieved an accuracy of 98.2% on the testing set. Full article
(This article belongs to the Special Issue Security and Privacy in IoT Enabled Modern Applications Using Deep/Machine Learning and Blockchain Technology)
►▼ Show Figures

Figure 1

16 pages, 2271 KiB  
Article
Deep Learning-Based Intrusion Detection Methods in Cyber-Physical Systems: Challenges and Future Trends
by Muhammad Umer, Saima Sadiq, Hanen Karamti, Reemah M. Alhebshi, Khaled Alnowaiser, Ala’ Abdulmajid Eshmawi, Houbing Song and Imran Ashraf
Electronics 2022, 11(20), 3326; https://doi.org/10.3390/electronics11203326 - 15 Oct 2022
Cited by 10 | Viewed by 2379
Abstract
A cyber-physical system (CPS) integrates various interconnected physical processes, computing resources, and networking units, as well as monitors the process and applications of the computing systems. Interconnection of the physical and cyber world initiates threatening security challenges, especially with the increasing complexity of [...] Read more.
A cyber-physical system (CPS) integrates various interconnected physical processes, computing resources, and networking units, as well as monitors the process and applications of the computing systems. Interconnection of the physical and cyber world initiates threatening security challenges, especially with the increasing complexity of communication networks. Despite efforts to combat these challenges, it is difficult to detect and analyze cyber-physical attacks in a complex CPS. Machine learning-based models have been adopted by researchers to analyze cyber-physical security systems. This paper discusses the security threats, vulnerabilities, challenges, and attacks of CPS. Initially, the CPS architecture is presented as a layered approach including the physical layer, network layer, and application layer in terms of functionality. Then, different cyber-physical attacks regarding each layer are elaborated, in addition to challenges and key issues associated with each layer. Afterward, deep learning models are analyzed for malicious URLs and intrusion detection in cyber-physical systems. A multilayer perceptron architecture is utilized for experiments using the malicious URL detection dataset and KDD Cup99 dataset, and its performance is compared with existing works. Lastly, we provide a roadmap of future research directions for cyber-physical security to investigate attacks concerning their source, complexity, and impact. Full article
(This article belongs to the Special Issue Security and Privacy in IoT Enabled Modern Applications Using Deep/Machine Learning and Blockchain Technology)
►▼ Show Figures

Figure 1

14 pages, 20278 KiB  
Article
Classification and Analysis of Pistachio Species with Pre-Trained Deep Learning Models
by Dilbag Singh, Yavuz Selim Taspinar, Ramazan Kursun, Ilkay Cinar, Murat Koklu, Ilker Ali Ozkan and Heung-No Lee
Electronics 2022, 11(7), 981; https://doi.org/10.3390/electronics11070981 - 22 Mar 2022
Cited by 45 | Viewed by 9622
Abstract
Pistachio is a shelled fruit from the anacardiaceae family. The homeland of pistachio is the Middle East. The Kirmizi pistachios and Siirt pistachios are the major types grown and exported in Turkey. Since the prices, tastes, and nutritional values of these types differs, [...] Read more.
Pistachio is a shelled fruit from the anacardiaceae family. The homeland of pistachio is the Middle East. The Kirmizi pistachios and Siirt pistachios are the major types grown and exported in Turkey. Since the prices, tastes, and nutritional values of these types differs, the type of pistachio becomes important when it comes to trade. This study aims to identify these two types of pistachios, which are frequently grown in Turkey, by classifying them via convolutional neural networks. Within the scope of the study, images of Kirmizi and Siirt pistachio types were obtained through the computer vision system. The pre-trained dataset includes a total of 2148 images, 1232 of Kirmizi type and 916 of Siirt type. Three different convolutional neural network models were used to classify these images. Models were trained by using the transfer learning method, with AlexNet and the pre-trained models VGG16 and VGG19. The dataset is divided as 80% training and 20% test. As a result of the performed classifications, the success rates obtained from the AlexNet, VGG16, and VGG19 models are 94.42%, 98.84%, and 98.14%, respectively. Models’ performances were evaluated through sensitivity, specificity, precision, and F-1 score metrics. In addition, ROC curves and AUC values were used in the performance evaluation. The highest classification success was achieved with the VGG16 model. The obtained results reveal that these methods can be used successfully in the determination of pistachio types. Full article
(This article belongs to the Special Issue Security and Privacy in IoT Enabled Modern Applications Using Deep/Machine Learning and Blockchain Technology)
►▼ Show Figures

Figure 1

15 pages, 1645 KiB  
Article
An Experimental Analysis of Various Machine Learning Algorithms for Hand Gesture Recognition
by Shashi Bhushan, Mohammed Alshehri, Ismail Keshta, Ashish Kumar Chakraverti, Jitendra Rajpurohit and Ahed Abugabah
Electronics 2022, 11(6), 968; https://doi.org/10.3390/electronics11060968 - 21 Mar 2022
Cited by 35 | Viewed by 10260
Abstract
Nowadays, hand gestures have become a booming area for researchers to work on. In communication, hand gestures play an important role so that humans can communicate through this. So, for accurate communication, it is necessary to capture the real meaning behind any hand [...] Read more.
Nowadays, hand gestures have become a booming area for researchers to work on. In communication, hand gestures play an important role so that humans can communicate through this. So, for accurate communication, it is necessary to capture the real meaning behind any hand gesture so that an appropriate response can be sent back. The correct prediction of gestures is a priority for meaningful communication, which will also enhance human–computer interactions. So, there are several techniques, classifiers, and methods available to improve this gesture recognition. In this research, analysis was conducted on some of the most popular classification techniques such as Naïve Bayes, K-Nearest Neighbor (KNN), random forest, XGBoost, Support vector classifier (SVC), logistic regression, Stochastic Gradient Descent Classifier (SGDC), and Convolution Neural Networks (CNN). By performing an analysis and comparative study on classifiers for gesture recognition, we found that the sign language MNIST dataset and random forest outperform traditional machine-learning classifiers, such as SVC, SGDC, KNN, Naïve Bayes, XG Boost, and logistic regression, predicting more accurate results. Still, the best results were obtained by the CNN algorithm. Full article
(This article belongs to the Special Issue Security and Privacy in IoT Enabled Modern Applications Using Deep/Machine Learning and Blockchain Technology)
►▼ Show Figures

Figure 1

18 pages, 4259 KiB  
Article
SUKRY: Suricata IDS with Enhanced kNN Algorithm on Raspberry Pi for Classifying IoT Botnet Attacks
by Irfan Syamsuddin and Omar Mohammed Barukab
Electronics 2022, 11(5), 737; https://doi.org/10.3390/electronics11050737 - 27 Feb 2022
Cited by 12 | Viewed by 4520
Abstract
The focus of this research is the application of the k-Nearest Neighbor algorithm in terms of classifying botnet attacks in the IoT environment. The kNN algorithm has several advantages in classification tasks, such as simplicity, effectiveness, and robustness. However, it does not perform [...] Read more.
The focus of this research is the application of the k-Nearest Neighbor algorithm in terms of classifying botnet attacks in the IoT environment. The kNN algorithm has several advantages in classification tasks, such as simplicity, effectiveness, and robustness. However, it does not perform well in handling large datasets such as the Bot-IoT dataset, which represents a huge amount of data about botnet attacks on IoT networks. Therefore, improving the kNN performance in classifying IoT botnet attacks is the main concern in this study by applying several feature selection techniques. The whole research process was conducted in the Rapidminer environment using three prebuilt feature selection techniques, namely, Information Gain, Forward Selection, and Backward Elimination. After comparing accuracy, precision, recall, F1 score and processing time, the combination of the kNN algorithm and the Forward Selection technique (kNN-FS) achieves the best results among others, with the highest level of accuracy and the fastest execution time among others. Finally, kNN-FS is used in developing SUKRY, which stands for Suricata IDS with Enhanced kNN Algorithm on Raspberry Pi. Full article
(This article belongs to the Special Issue Security and Privacy in IoT Enabled Modern Applications Using Deep/Machine Learning and Blockchain Technology)
►▼ Show Figures

Graphical abstract

14 pages, 4133 KiB  
Article
A Novel Approach to Face Pattern Analysis
by Shashi Bhushan, Mohammed Alshehri, Neha Agarwal, Ismail Keshta, Jitendra Rajpurohit and Ahed Abugabah
Electronics 2022, 11(3), 444; https://doi.org/10.3390/electronics11030444 - 1 Feb 2022
Cited by 8 | Viewed by 2654
Abstract
Recognizing facial expressions is a major challenge and will be required in the latest fields of research such as the industrial Internet of Things. Currently, the available methods are useful for detecting singular facial images, but they are very hard to extract. The [...] Read more.
Recognizing facial expressions is a major challenge and will be required in the latest fields of research such as the industrial Internet of Things. Currently, the available methods are useful for detecting singular facial images, but they are very hard to extract. The main aim of face detection is to capture an image in real-time and search for the image in the available dataset. So, by using this biometric feature, one can recognize and verify the person’s image by their facial features. Many researchers have used Principal Component Analysis (PCA), Support Vector Machine (SVM), a combination of PCA and SVM, PCA with an Artificial Neural Network, and even the traditional PCA-SVM to improve face recognition. PCA-SVM is better than PCA-ANN as PCA-ANN has the limitation of a small dataset. As far as classification and generalization are concerned, SVM requires fewer parameters and generates less generalization errors than an ANN. In this paper, we propose a new framework, called FRS-DCT-SVM, that uses GA-RBF for face detection and optimization and the discrete cosine transform (DCT) to extract features. FRS-DCT-SVM using GA-RBF gives better results in terms of clustering time. The average accuracy received by FRS-DCT-SVM using GA-RBF is 98.346, which is better than that of PCA-SVM and SVM-DCT (86.668 and 96.098, respectively). In addition, a comparison is made based on the training, testing, and classification times. Full article
(This article belongs to the Special Issue Security and Privacy in IoT Enabled Modern Applications Using Deep/Machine Learning and Blockchain Technology)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop