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Applied Sciences
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Trends and Prospects in Software Security
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Trends and Prospects in Software Security

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 June 2025 | Viewed by 482

Special Issue Editor

Prof. Dr. Zhanyong Tang

E-Mail Website
Guest Editor
School of Information Science and Technology, Northwest University, Xi 'an 710069, China
Interests: system and software security; wireless sensing identification and authentication; artificial intelligence security

Special Issue Information

Dear Colleagues,

In the digital age, software is the lifeblood of our technological ecosystem. As we stand on the cusp of a new era dominated by artificial intelligence (AI), the importance of software security has grown exponentially. The convergence of AI and software has opened a Pandora's box of opportunities and challenges. AI has enhanced the capabilities of software, enabling automation, intelligent decision making, and unprecedented data processing. However, its evolution also means that the security stakes are higher than ever. Malicious actors are constantly evolving their tactics and exploiting the complex interdependencies and vulnerabilities in software systems. This Special Issue aims to delve into the trends and perspectives of software security.

We aim to provide a platform for researchers, practitioners, and industry experts from around the world to share their latest findings and insights on software security in the context of AI. By bringing together different perspectives, we hope to stimulate discussions and collaborations that can lead to innovative solutions and policies. We invite researchers to submit their contributions to this Special Issue. Potential topics include, but are not limited to, the following：

  • Software security in the convergence of emerging technologies;
  • Software security encryption mechanisms;
  • The application of artificial intelligence algorithms in software vulnerability detection and repair;
  • New malware propagation patterns;
  • Common attack types and preventive measures against IoT software;
  • Software supply chain attack case analysis and defense system construction;
  • Software security assessment and standards;
  • New methods of software fuzzy testing;
  • Software and code protection.

Prof. Dr. Zhanyong Tang
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. 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

  • system and software security
  • artificial intelligence
  • information security
  • privacy

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Published Papers (1 paper)

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Research

28 pages, 425 KiB  
Article
SecureLLM: A Unified Framework for Privacy-Focused Large Language Models
by Konstantinos Kalodanis, Sotirios Papadopoulos, Georgios Feretzakis, Panagiotis Rizomiliotis and Dimosthenis Anagnostopoulos
Appl. Sci. 2025, 15(8), 4180; https://doi.org/10.3390/app15084180 - 10 Apr 2025
Viewed by 279
Abstract
Large language models (LLMs) have shown remarkable skills across various activities, including text generation and code synthesis. Their widespread applicability, however, raises substantial concerns about security, privacy, and possibly misuse. Of recent legislative efforts, the most notable is the proposed EU AI Act, [...] Read more.
Large language models (LLMs) have shown remarkable skills across various activities, including text generation and code synthesis. Their widespread applicability, however, raises substantial concerns about security, privacy, and possibly misuse. Of recent legislative efforts, the most notable is the proposed EU AI Act, which classifies specific AI applications as high-risk. For detailed regulatory guidance, also refer to the GDPR and HIPAA privacy rules. This paper introduces SecureLLM, a novel framework that integrates lightweight cryptographic protocols, decentralized fine-tuning strategies, and differential privacy to mitigate data leakage and adversarial attacks in LLM ecosystems. We propose SecureLLM as a conceptual security architecture for LLMs, offering a unified approach that can be adapted and tested in real-world deployments. While extensive empirical benchmarks are deferred to future studies, we include a small-scale demonstration illustrating how differential privacy can reduce membership inference risks with a manageable overhead. The SecureLLM framework underscores the potential of cryptography, differential privacy, and decentralized fine-tuning for creating safer and more compliant AI systems. Full article
(This article belongs to the Special Issue Trends and Prospects in Software Security)
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