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Applied Sciences
Special Issues
Advances in Security, Trust and Privacy in Internet of Things
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Advances in Security, Trust and Privacy in Internet of Things

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 447

Special Issue Editors

Dr. Weidong Fang

E-Mail Website
Guest Editor
Science and Technology on Micro-System Laboratory, Shanghai Institute of Micro-System and Information Technology, Chinese Academy of Sciences, Shanghai 201800, China
Interests: Internet of Things; wireless sensor network; cyber security; trust model; secure routing protocol
Special Issues, Collections and Topics in MDPI journals
Dr. Chunsheng Zhu

E-Mail Website
Guest Editor
SUSTech Institute of Future Networks, Southern University of Science and Technology, Shenzhen 518055, China
Interests: Internet of Things; wireless sensor networks; cloud computing; big data; social networks and security
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Andrew W. H. Ip

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A2, Canada
Interests: radio frequency identification (RFID) and supply chain technologies; wireless sensor networks (WSNs); decision support systems and artificial intelligence (AI); big data analytics; resilience engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The proliferation of Internet of Things (IoT) devices has revolutionized various aspects of modern life, from smart homes and cities to industrial automation and healthcare. However, with this rapid expansion comes a pressing need to address the inherent security, trust, and privacy challenges that accompany the interconnected nature of IoT ecosystems. As IoT devices continue to permeate every facet of society, ensuring robust security measures, establishing trust relationships, and preserving user privacy have become paramount concerns for researchers, practitioners, and policymakers alike.

This Special Issue of Applied Sciences aims to explore recent advancements and emerging trends in addressing the complex challenges associated with securing IoT devices, establishing trust relationships, and safeguarding user privacy in an increasingly interconnected world. By bringing together experts from academia, industry, and government, we seek to foster dialogue and collaboration towards enhancing the security, trustworthiness, and privacy of IoT deployments. By disseminating cutting-edge research and best practices, we hope to empower stakeholders to build more secure, trustworthy, and privacy-respecting IoT systems that enhance the quality of life for individuals and communities worldwide.

We invite submissions addressing a wide range of topics related to security, trust, and privacy in the Internet of Things. Potential areas of interest include, but are not limited to, the following:

  • Network and Cyber Security;
  • Security Policy, Model and Architecture in IoTs;
  • Trust Semantics, Metrics and Models;
  • Trusted Computing Platform;
  • Risk and Reputation Management;
  • Miscellaneous Trust Issues in Cyber Security;
  • Privacy in Mobile and Wireless Communications;
  • Privacy and Anonymity.

Dr. Weidong Fang
Dr. Chunsheng Zhu
Prof. Dr. Andrew W. H. Ip
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. Applied Sciences 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

  • Internet of Things
  • cyber security
  • trust
  • privacy preservation

Published Papers (1 paper)

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Research

38 pages, 14898 KiB  
Article
Audio Steganalysis Estimation with the Goertzel Algorithm
by Blanca E. Carvajal-Gámez, Miguel A. Castillo-Martínez, Luis A. Castañeda-Briones, Francisco J. Gallegos-Funes and Manuel A. Díaz-Casco
Appl. Sci. 2024, 14(14), 6000; https://doi.org/10.3390/app14146000 - 10 Jul 2024
Viewed by 243
Abstract
Audio steganalysis has been little explored due to its complexity and randomness, which complicate the analysis. Audio files generate marks in the frequency domain; these marks are known as fingerprints and make the files unique. This allows us to differentiate between audio vectors. [...] Read more.
Audio steganalysis has been little explored due to its complexity and randomness, which complicate the analysis. Audio files generate marks in the frequency domain; these marks are known as fingerprints and make the files unique. This allows us to differentiate between audio vectors. In this work, the use of the Goertzel algorithm as a steganalyzer in the frequency domain is combined with the proposed sliding window adaptation to allow the analyzed audio vectors to be compared, enabling the differences between the vectors to be identified. We then apply linear prediction to the vectors to detect any modifications in the acoustic signatures. The implemented Goertzel algorithm is computationally less complex than other proposed stegoanalyzers based on convolutional neural networks or other types of classifiers of lower complexity, such as support vector machines (SVD). These methods previously required an extensive audio database to train the network, and thus detect possible stegoaudio through the matches they find. Unlike the proposed Goertzel algorithm, which works individually with the audio vector in question, it locates the difference in tone and generates an alert for the possible stegoaudio. In this work, we apply the classic Goertzel algorithm to detect frequencies that have possibly been modified by insertions or alterations of the audio vectors. The final vectors are plotted to visualize the alteration zones. The obtained results are evaluated qualitatively and quantitatively. To perform a double check of the fingerprint of the audio vectors, we obtain a linear prediction error to establish the percentage of statistical dependence between the processed audio signals. To validate the proposed method, we evaluate the audio quality metrics (AQMs) of the obtained result. Finally, we implement the stegoanalyzer oriented to AQMs to corroborate the obtained results. From the results obtained for the performance of the proposed stegoanalyzer, we demonstrate that we have a success rate of 100%. Full article
(This article belongs to the Special Issue Advances in Security, Trust and Privacy in Internet of Things)
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