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Recent Advances in Secure Software Engineering

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

Deadline for manuscript submissions: 20 May 2027 | Viewed by 1036

Editors


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Guest Editor
The Department of Computational, Engineering, and Mathematical Sciences, College of Arts and Sciences, Texas A&M University-San Antonio, 71007, San Antonio, TX 78224, USA
Interests: computer science; software engineering; software visualization; source; code analysis; software security
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
The Department of Computational, Engineering, and Mathematical Sciences, College of Arts and Sciences, Texas A&M University-San Antonio, 71007, San Antonio, TX 78224, USA
Interests: object-oriented programming; Java programming; software development; cyber security; computer security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modern software, especially applications handling private and sensitive information, faces inevitable security threats. This Special Issue will address these challenges by showcasing recent findings and innovative perspectives on integrating security measures throughout the software development lifecycle.

We are particularly interested in papers that create secure software applications across diverse environments and implement advanced strategies to prevent malicious activities in cyber systems.

We invite researchers, professionals, and experts to share their insights and findings. With this Special Issue, we aim to enrich current knowledge, inspire new research directions, and contribute to a safer, more secure digital world.

Dr. Young Lee
Dr. Jeong Yang
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 250 words) can be sent to the Editorial Office for assessment.

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. 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

  • secure software developement
  • mobile application security
  • cloud security
  • security vulnerabilities
  • privacy and data security
  • artificial intelligence and machine learning in cybersecurity
  • IoT security
  • data protection
  • internet safety
  • encryption
  • authentication
  • cyber threats
  • intrusion detection
  • security architecture

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Published Papers (2 papers)

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Research

19 pages, 1709 KB  
Article
Advanced Vector Extensions 512 Acceleration of LSH and LEA-GCM
by Seung-Won Lee, Min-Ho Song, Ha-Gyeong Kim, Ui-Jae Kim, Si-Woo Eum and Hwa-Jeong Seo
Appl. Sci. 2026, 16(14), 6846; https://doi.org/10.3390/app16146846 - 8 Jul 2026
Viewed by 162
Abstract
This paper presents high-performance Advanced Vector Extensions 512 (AVX-512) implementations of two Korean standard cryptographic algorithms: the Lightweight Secure Hash (LSH) function and Lightweight Encryption Algorithm–Galois/Counter Mode (LEA-GCM) authenticated encryption. For LSH, we apply three optimization strategies: single-message processing using AVX-512 512-bit vector [...] Read more.
This paper presents high-performance Advanced Vector Extensions 512 (AVX-512) implementations of two Korean standard cryptographic algorithms: the Lightweight Secure Hash (LSH) function and Lightweight Encryption Algorithm–Galois/Counter Mode (LEA-GCM) authenticated encryption. For LSH, we apply three optimization strategies: single-message processing using AVX-512 512-bit vector registers, dual-message parallel processing through register interleaving, and multi-core parallelization using a dynamic queue-based pthread thread pool. For LEA-GCM, we propose an end-to-end optimization that replaces scalar counter-mode (CTR) encryption with 16-block AVX-512 parallel processing and Streaming SIMD Extensions(SSE)-based Galois Hash (GHASH) authentication with VPCLMULQDQ-based four-block parallel processing. Performance evaluation on an Intel Core i7-1165G7 (Tiger Lake) processor shows that LSH-256 achieves an average of 1.16× throughput improvement and LSH-512 achieves an average of 1.61× improvement over the Korea Internet and Security Agency (KISA) AVX2 reference implementation. Dual-message interleaving achieves an average superlinear speedup of 2.28×, driven by instruction-level parallelism (ILP), and the thread pool delivers speedups of 3.73× to 5.13× across eight logical cores (four physical cores with hyperthreading). The optimized LEA-GCM implementation achieves a 3.26× throughput improvement over the KISA SSE-based reference and a 12.1× improvement over the pure software implementation for 4096-byte inputs, with correctness verified against KISA official test vectors. Full article
(This article belongs to the Special Issue Recent Advances in Secure Software Engineering)
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25 pages, 537 KB  
Article
IP Composition Analysis as a Prerequisite for IDS Dataset Evaluation: Correcting File-Level Label Artifacts in SDN-MG25
by Khaled Chahine and Hassan N. Noura
Appl. Sci. 2026, 16(10), 5064; https://doi.org/10.3390/app16105064 - 19 May 2026
Viewed by 359
Abstract
Intrusion detection system (IDS) research relies on accurately labeled network traffic datasets; however, label quality in IDS datasets is seldom audited prior to modeling. Many publicly available IDS datasets assign ground-truth labels based on capture filenames or temporal session windows rather than per-flow [...] Read more.
Intrusion detection system (IDS) research relies on accurately labeled network traffic datasets; however, label quality in IDS datasets is seldom audited prior to modeling. Many publicly available IDS datasets assign ground-truth labels based on capture filenames or temporal session windows rather than per-flow inspection, a practice referred to as file-level labeling. This study identifies and corrects a systematic mislabeling instance in SDN-MG25, a CICFlowMeter-based dataset for software-defined networking (SDN)-enabled microgrid intrusion detection. IP composition analysis, which cross-references each attack-labeled flow with the documented attacker IP address, reveals that the BackgroundAttackTraffic (BAT) class, comprising 3167 flows (79.5% of all attack labels), contains no attacker-originated traffic. All BAT flows involve legitimate microgrid hosts communicating with external services during the attack capture window. Correcting this labeling error increases binary detection F1 from 0.578 to 0.956±0.005, an improvement of +0.378 that is 4.2 times greater than the best single modeling improvement (threshold tuning, +0.090). Furthermore, Confident Learning, a state-of-the-art automated label-noise detector, recovers only 8.4% of mislabeled BAT flows (recall =0.084, precision =0.247), indicating that domain-knowledge audits are essential for detecting systematic, class-level mislabeling that statistical methods cannot identify. The end-to-end pipeline Macro F1 improves from 0.749 to 0.862 after label correction. IP composition analysis is proposed as a mandatory prerequisite for IDS dataset evaluation, and a reproducible two-stage pipeline with feature-tier ablation for session confound diagnosis is provided. Full article
(This article belongs to the Special Issue Recent Advances in Secure Software Engineering)
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