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New Trends in Cybersecurity and Privacy Protection

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 15 December 2026 | Viewed by 841

Editors


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Guest Editor
Department of Information and Communication Systems Engineering, University of the Aegean, 83200 Karlovasi, Greece
Interests: computer network security; intrusion detection systems; network security; machine learning; lateral movement; dataset; attacks; hacking

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Guest Editor
Information and Communication Systems Security, at the Laboratory of Systems Security, Department of Digital Systems, University of Piraeus, Piraeus, Greece
Interests: network security; information systems security; security policies and security management and privacy on the internet
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The evolution of computing and communications, coupled with the proliferation of lightweight handheld smart/autonomous Internet of Things (IoT) devices, has led to vast amounts of digital data, including personal information (biometrics, ID credentials, logistics, healthcare and many other domains), corporate records and even national/state classified secrets, being stored and manipulated via institutional offshore server-based cloud facilities.

While informatization and digitization have been fundamentally innovative for the way we live and work during the last decade, they have also exposed security issues for individuals, businesses and large-scale national organizations. This has introduced new vulnerabilities and privacy challenges for cybersecurity experts, making the uninterrupted supply of secure and trustworthy networked services more challenging than ever. Furthermore, although data analysis is being actively studied across academia, corporations and governments, the advent of sophisticated malware and ransomware targeting sensitive digital data highlights the necessity of targeted research to solve this problem.

This Special Issue aims to advance the state of the art by gathering original research in the field of software-intensive systems, exploring the fundamental connections between the theory of information protection and extensive research on security issues for digital assets and various IT systems and devices.

Dr. Christos Smiliotopoulos
Prof. Dr. Stefanos Gritzalis
Guest Editors

Manuscript Submission Information

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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-anonymized 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

  • information security
  • cybersecurity
  • privacy-enhancing technologies
  • blockchain
  • IoT security and privacy
  • AI for cybersecurity
  • cybersecurity for AI
  • cloud security
  • network security monitoring
  • cyber threat intelligence
  • digital forensics
  • steganography

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Published Papers (1 paper)

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Research

23 pages, 10620 KB  
Article
A Novel Coverless Image Steganography Scheme Based on LLM-Guided Image Generation
by Yung-Chen Chou, Jun-Yi Liu, Yuan-Yu Tsai and Chun-Hsiu Yeh
Electronics 2026, 15(15), 3415; https://doi.org/10.3390/electronics15153415 - 2 Aug 2026
Viewed by 234
Abstract
With the advancement of deep learning-based steganalysis and prevalence of lossy compression mechanisms in social network transmission, traditional steganography based on cover modification (such as LSB substitution) faces dual challenges of security and robustness. This study proposes a novel steganographic framework based on [...] Read more.
With the advancement of deep learning-based steganalysis and prevalence of lossy compression mechanisms in social network transmission, traditional steganography based on cover modification (such as LSB substitution) faces dual challenges of security and robustness. This study proposes a novel steganographic framework based on generative artificial intelligence and predefined semantic mapping. Unlike embedding ciphertext in pixel noise, this method utilizes a shared mapping protocol (Codebook) to transform abstract information into concrete visual elements (such as characters, actions, scenes, and styles), and constructs stego-images through generative models. Experimental results show that when both parties share the same key, the system achieves full semantic recovery. Using an explicitly specified pipeline (Gemini 2.5 Flash Image for synthesis and Gemini 2.5 Flash for parsing), we evaluate the scheme on an enlarged, randomly sampled scenario set spanning three to six active semantic dimensions. Across these scenarios, we report per-dimension accuracy, the end-to-end full-recovery rate with 95% confidence intervals, and the partial-recovery rate under controlled JPEG compression, re-scaling, Gaussian noise, and cropping, rather than a single aggregate figure. The results indicate that carrying information at the semantic level yields graceful degradation under common channel distortions together with high visual camouflage, while also revealing that recovery reliability decreases as more semantic dimensions are activated simultaneously. We therefore present these findings as a proof of concept and explicitly separate demonstrated results from hypotheses left to future work. We therefore present these findings as a proof of concept and explicitly separate demonstrated results from hypotheses left to future work. Full article
(This article belongs to the Special Issue New Trends in Cybersecurity and Privacy Protection)
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