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Advances and Challenges in Sensor Security Systems
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Advances and Challenges in Sensor Security Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (31 March 2025) | Viewed by 1246

Special Issue Editor

Dr. Raffaele Pizzolante

E-Mail Website
Guest Editor
Department of Computer Science, University of Salerno, 84084 Fisciano, Italy
Interests: data compression; information hiding; digital forensics; cyber security and digital watermarking
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the field of sensor security systems has seen significant advancements, driven by technological innovations, increasing demand for robust security solutions, enhanced wireless connectivity, improved energy efficiency, and so on. The integration of multiple sensor types enables comprehensive threat detection, enhancing reliability and effectiveness.

Despite these advancements, several challenges remain. Ensuring cybersecurity to protect sensors from attacks is crucial, especially as sensor interconnectivity grows. Continued research is essential to address these issues and further improve sensor security systems.

This Special Issue focuses on novel advances and challenges in this field. Our goal is to bring together researchers, industry, and companies operating in related fields to share recent discoveries, new ideas, and cutting-edge accomplishments.

Topics of interest include, but are not limited to, the following:

  • Emerging Technologies in Sensor Security;
  • Integration of Blockchain in Sensor Security;
  • Advanced Encryption Techniques for Sensor Data;
  • Machine Learning/Artificial Intelligence and Sensor Security;
  • Challenges in Securing IoT Devices;
  • Cybersecurity Issues, Threats, and Vulnerabilities in Sensor Systems;
  • Automotive System Security;
  • Security in Healthcare Sensor Systems;
  • Security in Smart Home.

Dr. Raffaele Pizzolante
Guest Editor

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. Sensors 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 2600 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

  • sensors security systems
  • cybersecurity
  • IoT Devices
  • automotive
  • artificial intelligence

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

19 pages, 1962 KiB  
Article
A Two-Phase Embedding Approach for Secure Distributed Steganography
by Kamil Woźniak, Marek R. Ogiela and Lidia Ogiela
Sensors 2025, 25(5), 1448; https://doi.org/10.3390/s25051448 - 27 Feb 2025
Viewed by 344
Abstract
Steganography serves a crucial role in secure communications by concealing information within non-suspicious media, yet traditional methods often lack resilience and efficiency. Distributed steganography, which involves fragmenting messages across multiple containers using secret sharing schemes, offers improved security but increases complexity. This paper [...] Read more.
Steganography serves a crucial role in secure communications by concealing information within non-suspicious media, yet traditional methods often lack resilience and efficiency. Distributed steganography, which involves fragmenting messages across multiple containers using secret sharing schemes, offers improved security but increases complexity. This paper introduces a novel two-phase embedding algorithm that mitigates these issues, enhancing both security and practicality. Initially, the secret message is divided into shares using Shamir’s Secret Sharing and embedded into distinct media containers via pseudo-random LSB paths determined by a unique internal stego key. Subsequently, this internal key is further divided and embedded using a shared stego key known only to the sender and receiver, adding an additional security layer. The algorithm effectively reduces key management complexity while enhancing resilience against sophisticated steganalytic attacks. Evaluation metrics, including Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM), demonstrate that stego images maintain high quality even when embedding up to 0.95 bits per pixel (bpp). Additionally, robustness tests with StegoExpose and Aletheia confirm the algorithm’s stealthiness, as no detections are made by these advanced steganalysis tools. This research offers a secure and efficient advancement in distributed steganography, facilitating resilient information concealment in sophisticated communication environments. Full article
(This article belongs to the Special Issue Advances and Challenges in Sensor Security Systems)
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16 pages, 15374 KiB  
Article
U-Net-Based Fingerprint Enhancement for 3D Fingerprint Recognition
by Mohammad Mogharen Askarin, Min Wang, Xuefei Yin, Xiuping Jia and Jiankun Hu
Sensors 2025, 25(5), 1384; https://doi.org/10.3390/s25051384 - 24 Feb 2025
Viewed by 501
Abstract
Biometrics-based authentication mechanisms can address the built-in weakness of conventional password or token-based authentication in identifying genuine users. However, 2D-based fingerprint biometrics authentication faces the problem of sensor spoofing attacks. In addition, most 2D fingerprint sensors are contact-based, which can boost the spread [...] Read more.
Biometrics-based authentication mechanisms can address the built-in weakness of conventional password or token-based authentication in identifying genuine users. However, 2D-based fingerprint biometrics authentication faces the problem of sensor spoofing attacks. In addition, most 2D fingerprint sensors are contact-based, which can boost the spread of deadly diseases such as the COVID-19 virus. Three-dimensional fingerprint-based recognition is the emerging technology that can effectively address the above issues. A 3D fingerprint is captured contactlessly and can be represented by a 3D point cloud, which is strong against sensor spoofing attacks. To apply conventional 2D fingerprint recognition methods to 3D fingerprints, the 3D point cloud needs to be converted into a 2D gray-scale image. However, the contrast of the generated image is often not of good quality for direct matching. In this work, we propose an image segmentation approach using the deep learning U-Net to enhance the fingerprint contrast. The enhanced fingerprint images are then used for conventional fingerprint recognition. By applying the proposed method, the fingerprint recognition Equal Error Rate (EER) in experiment A and B improved from 41.32% and 41.97% to 13.96 and 12.49%, respectively, over the public dataset. Full article
(This article belongs to the Special Issue Advances and Challenges in Sensor Security Systems)
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