Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
2.9
Journals
Electronics
Special Issues
Digital Trustworthiness: Cybersecurity, Privacy and Resilience
share announcement

Digital Trustworthiness: Cybersecurity, Privacy and Resilience

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

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 15241

Special Issue Editors

Dr. Muath Obaidat

E-Mail Website
Guest Editor
Computer Science Department, City University of New York, New York, NY 10019, USA
Interests: wireless and mobile security; network security and forensics; IoT security and privacy; cybersecurity and machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Thaier Hayajneh

E-Mail Website
Guest Editor
Fordham Center for Cybersecurity, Fordham University, New York, NY 10023, USA
Interests: security; information assurance and privacy; crypto-resilient attacks; applied cryptography; blockchain and cryptocurrency; IoT security and privacy; cyberphysical systems and WBAN security; steganography; lightweight cryptographic algorithms and protocols; cloud-computing security; ad hoc and WSNs; ecure remote patient monitoring systems; computer networks protocols and QoS; wireless networks coexistence
Special Issues, Collections and Topics in MDPI journals
Dr. Eman Hammad

E-Mail Website
Guest Editor
Computer Science and Information Systems, Texas A&M University, College Station, TX 77843, USA
Interests: Cyber-Physical Systems; Cyber-Physical Security & Resilience; Smart Grid; 5G; Interdependent Critical Infrastru

Special Issue Information

Dear Colleagues,

The continued advancements in technology, connectivity and intelligence are put at risk by an alarming rise in adversarial cyber-attacks targeting a wide range of systems and applications from online services, banking, and IoT devices to critical infrastructures (energy, telecom). It is evident that more innovation will be critical to improve the current state of cybersecurity across multiple domains, technologies and industries to ensure the resilience and trustworthiness of our digital societies.

To that purpose, this Special Issue invites contributions presenting new original research and development in the domain of security and privacy that pertain to the domains of systems, networks, communications, devices and data. We particularly encourage submissions with high-calibre empirical research and scholarly work proposing practical, scalable and usable approaches to cybersecurity and privacy in its broad definition as well as comprehensive surveys.

This Special Issue on “Digital trustworthiness: cybersecurity, privacy and resilience” solicits articles in multiple domains and topics including but not limited to the following:

- Cybersecurity: formal methods;

- Cryptography and cryptanalysis: quantum-safe

- Cybersecurity and cybercrime

- Wireless and mobile networks security

- Critical infrastructures security

- Privacy and anonymity

- IoT and mobile devices security

- Digital forensics

- Cybersecurity and privacy laws and ethics

- Cloud, fog and edge computing security

- Trust/blockchain for systems security

- Machine learning for security

Dr. Muath Obaidat
Prof. Dr. Thaier Hayajneh
Dr. Eman Hammad
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

  • cybersecurity
  • security
  • privacy
  • blockchain
  • cryptography
  • forensics
  • computing
  • cloud
  • wireless
  • IoT
  • sensors
  • smart cities

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

Jump to: Review, Other

19 pages, 3940 KiB  
Article
Criminal Behavior Identification Using Social Media Forensics
by Noorulain Ashraf, Danish Mahmood, Muath A. Obaidat, Ghufran Ahmed and Adnan Akhunzada
Electronics 2022, 11(19), 3162; https://doi.org/10.3390/electronics11193162 - 01 Oct 2022
Cited by 5 | Viewed by 2714
Abstract
Human needs consist of five levels, which are: physiological needs, safety needs, love needs, esteem needs and self-actualization. All these needs lead to human behavior. If the environment of a person is positive, healthy behavior is developed. However, if the environment of the [...] Read more.
Human needs consist of five levels, which are: physiological needs, safety needs, love needs, esteem needs and self-actualization. All these needs lead to human behavior. If the environment of a person is positive, healthy behavior is developed. However, if the environment of the person is not healthy, it can be reflected in his/her behavior. Machines are intelligent enough to mimic human intelligence by using machine learning and artificial intelligence techniques. In the modern era, people tend to post their everyday life events on social media in the form of comments, pictures, videos, etc. Therefore, social media is a significant way of knowing certain behaviors of people such as abusive, aggressive, frustrated and offensive behaviors. Behavior detection by crawling the social media profile of a person is a crucial and important idea. The challenge of behavior detection can be sorted out by applying social media forensics on social media profiles, which involves NLP and deep learning techniques. This paper is based on the study of state of the art work on behavior detection, and based on the research, a model is proposed for behavior detection. The proposed model outperformed with an F1 score of 87% in the unigram + bigram class, and in the bigram + trigram class, it gave an F1 score of 88% when compared with models applied on state of the art work. This study is a great benefit to cybercrime and cyber-security agencies in shortlisting the profiles containing certain behaviors to prevent crimes in the future. Full article
(This article belongs to the Special Issue Digital Trustworthiness: Cybersecurity, Privacy and Resilience)
►▼ Show Figures

Figure 1

32 pages, 2136 KiB  
Article
Privacy-Preserving Top-k Query Processing Algorithms Using Efficient Secure Protocols over Encrypted Database in Cloud Computing Environment
by Hyeong-Jin Kim, Yong-Ki Kim, Hyun-Jo Lee and Jae-Woo Chang
Electronics 2022, 11(18), 2870; https://doi.org/10.3390/electronics11182870 - 11 Sep 2022
Viewed by 1576
Abstract
Recently, studies on secure database outsourcing have been highlighted for the cloud computing environment. A few secure Top-k query processing algorithms have been proposed in the encrypted database. However, the previous algorithms can support either security or efficiency. Therefore, we propose a [...] Read more.
Recently, studies on secure database outsourcing have been highlighted for the cloud computing environment. A few secure Top-k query processing algorithms have been proposed in the encrypted database. However, the previous algorithms can support either security or efficiency. Therefore, we propose a new Top-k query processing algorithm using a homomorphic cryptosystem, which can support both security and efficiency. For security, we propose new secure and efficient protocols based on arithmetic operations. To obtain a high level of efficiency, we also propose a parallel Top-k query processing algorithm using an encrypted random value pool. Through our performance analysis, the proposed Top-k algorithms present about 1.5∼7.1 times better performance with regard to a query processing time, compared with the existing algorithms. Full article
(This article belongs to the Special Issue Digital Trustworthiness: Cybersecurity, Privacy and Resilience)
►▼ Show Figures

Figure 1

20 pages, 1384 KiB  
Article
A Hybrid Method for Keystroke Biometric User Identification
by Md L. Ali, Kutub Thakur and Muath A. Obaidat
Electronics 2022, 11(17), 2782; https://doi.org/10.3390/electronics11172782 - 03 Sep 2022
Cited by 15 | Viewed by 2277
Abstract
The generative model and discriminative model are the two categories of statistical models used in keystroke biometric areas. Generative models have the trait of handling missing or irregular data, and perform well for limited training data. Discriminative models are fast in making predictions [...] Read more.
The generative model and discriminative model are the two categories of statistical models used in keystroke biometric areas. Generative models have the trait of handling missing or irregular data, and perform well for limited training data. Discriminative models are fast in making predictions for new data, resulting in faster classification of new data compared to the generative models. In an attempt to build an efficient model for keystroke biometric user identification, this study proposes a hybrid POHMM/SVM method taking advantage of both generative and discriminative models. The partially observable hidden Markov model (POHMM) is an extension of the hidden Markov model (HMM), which has shown promising performance in user verification and handling missing or infrequent data. On the other hand, the support vector machine (SVM) has been a widely used discriminative model in keystroke biometric systems for the last decade and achieved a higher accuracy rate for large data sets. In the proposed model, features are extracted using the POHMM model, and a one-class support vector machine is used as the anomaly detector. For user identification, the study examines POHMM parameters using five different discriminative classifiers: support vector machines, k-nearest neighbor, random forest, multilayer perceptron (MLP) neural network, and logistic regression. The best accuracy of 91.3% (mean 0.868, SD 0.132) is achieved by the proposed hybrid POHMM/SVM approach among all generative and discriminative models. Full article
(This article belongs to the Special Issue Digital Trustworthiness: Cybersecurity, Privacy and Resilience)
►▼ Show Figures

Figure 1

16 pages, 3892 KiB  
Article
A Novel Feature-Selection Method for Human Activity Recognition in Videos
by Nadia Tweit, Muath A. Obaidat, Majdi Rawashdeh, Abdalraoof K. Bsoul and Mohammed GH. Al Zamil
Electronics 2022, 11(5), 732; https://doi.org/10.3390/electronics11050732 - 26 Feb 2022
Cited by 9 | Viewed by 1569
Abstract
Human Activity Recognition (HAR) is the process of identifying human actions in a specific environment. Recognizing human activities from video streams is a challenging task due to problems such as background noise, partial occlusion, changes in scale, orientation, lighting, and the unstable capturing [...] Read more.
Human Activity Recognition (HAR) is the process of identifying human actions in a specific environment. Recognizing human activities from video streams is a challenging task due to problems such as background noise, partial occlusion, changes in scale, orientation, lighting, and the unstable capturing process. Such multi-dimensional and none-linear process increases the complexity, making traditional solutions inefficient in terms of several performance indicators such as accuracy, time, and memory. This paper proposes a technique to select a set of representative features that can accurately recognize human activities from video streams, while minimizing the recognition time and memory. The extracted features are projected on a canvas, which keeps the synchronization property of the spatiotemporal information. The proposed technique is developed to select the features that refer only to progression of changes. The original RGB frames are preprocessed using background subtraction to extract the subject. Then the activity pattern is extracted through the proposed Growth method. Three experiments were conducted; the first experiment was a baseline to compare the classification task using the original RGB features. The second experiment relied on classifying activities using the proposed feature-selection method. Finally, the third experiment provided a sensitivity analysis that compares between the effect of both techniques on time and memory resources. The results indicated that the proposed method outperformed original RBG feature-selection method in terms of accuracy, time, and memory requirements. Full article
(This article belongs to the Special Issue Digital Trustworthiness: Cybersecurity, Privacy and Resilience)
►▼ Show Figures

Graphical abstract

Review

Jump to: Research, Other

16 pages, 1827 KiB  
Review
Contemporary Study on Deep Neural Networks to Diagnose COVID-19 Using Digital Posteroanterior X-ray Images
by Saad Akbar, Humera Tariq, Muhammad Fahad, Ghufran Ahmed and Hassan Jamil Syed
Electronics 2022, 11(19), 3113; https://doi.org/10.3390/electronics11193113 - 29 Sep 2022
Cited by 4 | Viewed by 1728
Abstract
COVID-19 is a transferable disease inherited from the SARS-CoV-2 virus. A total of 594 million people have been infected, and 6.4 million human beings have died due to COVID-19. The fastest way to diagnose the disease is by radiography. Deep learning has been [...] Read more.
COVID-19 is a transferable disease inherited from the SARS-CoV-2 virus. A total of 594 million people have been infected, and 6.4 million human beings have died due to COVID-19. The fastest way to diagnose the disease is by radiography. Deep learning has been the most popular technique for image classification during the last decade. This paper aims to examine the contributions of machine learning for the detection of COVID-19 using Deep Learning and explores the overall application of convolutional neural networks of some famous state-of-the-art deep learning pre-trained models. In this research, our objective is to explore the various image classification strategies for CXIs and the application of deep learning models for optimization and feature selection. The study presented in this article shows that the accuracy of deep learning models when detecting COVID-19 on the basis of chest X-ray images ranges from 93 percent to above 99 percent. Full article
(This article belongs to the Special Issue Digital Trustworthiness: Cybersecurity, Privacy and Resilience)
►▼ Show Figures

Graphical abstract

Other

Jump to: Research, Review

37 pages, 1425 KiB  
Systematic Review
A Comprehensive Analysis of Privacy-Preserving Solutions Developed for Online Social Networks
by Abdul Majeed, Safiullah Khan and Seong Oun Hwang
Electronics 2022, 11(13), 1931; https://doi.org/10.3390/electronics11131931 - 21 Jun 2022
Cited by 7 | Viewed by 4069
Abstract
Owning to the massive growth in internet connectivity, smartphone technology, and digital tools, the use of various online social networks (OSNs) has significantly increased. On the one hand, the use of OSNs enables people to share their experiences and information. On the other [...] Read more.
Owning to the massive growth in internet connectivity, smartphone technology, and digital tools, the use of various online social networks (OSNs) has significantly increased. On the one hand, the use of OSNs enables people to share their experiences and information. On the other hand, this ever-growing use of OSNs enables adversaries to launch various privacy attacks to compromise users’ accounts as well as to steal other sensitive information via statistical matching. In general, a privacy attack is carried out by the exercise of linking personal data available on the OSN site and social graphs (or statistics) published by the OSN service providers. The problem of securing user personal information for mitigating privacy attacks in OSNs environments is a challenging research problem. Recently, many privacy-preserving solutions have been proposed to secure users’ data available over OSNs from prying eyes. However, a systematic overview of the research dynamics of OSN privacy, and findings of the latest privacy-preserving approaches from a broader perspective, remain unexplored in the current literature. Furthermore, the significance of artificial intelligence (AI) techniques in the OSN privacy area has not been highlighted by previous research. To cover this gap, we present a comprehensive analysis of the state-of-the-art solutions that have been proposed to address privacy issues in OSNs. Specifically, we classify the existing privacy-preserving solutions into two main categories: privacy-preserving graph publishing (PPGP) and privacy preservation in application-specific scenarios of the OSNs. Then, we introduce a high-level taxonomy that encompasses common as well as AI-based privacy-preserving approaches that have proposed ways to combat the privacy issues in PPGP. In line with these works, we discuss many state-of-the-art privacy-preserving solutions that have been proposed for application-specific scenarios (e.g., information diffusion, community clustering, influence analysis, friend recommendation, etc.) of OSNs. In addition, we discuss the various latest de-anonymization methods (common and AI-based) that have been developed to infer either identity or sensitive information of OSN users from the published graph. Finally, some challenges of preserving the privacy of OSNs (i.e., social graph data) from malevolent adversaries are presented, and promising avenues for future research are suggested. Full article
(This article belongs to the Special Issue Digital Trustworthiness: Cybersecurity, Privacy and Resilience)
►▼ 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