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Advanced Technologies for Information Security
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Advanced Technologies for Information Security

A Special Issue of Electronics (ISSN 2079-9292) belonging to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 April 2027 | Viewed by 798

Editor

Dr. Sharmin Jahan

E-Mail Website
Guest Editor
Department of Computer Science, Oklahoma State University, Stillwater, OK 74078, USA
Interests: explainable artificial intelligence applications in cyber security; information security; self-protecting system; IoT security; security in micro-service based application

Special Issue Information

Dear Colleagues,

Information security is a critical requirement in today’s AI-driven and technologically advanced world, where massive volumes of sensitive data are continuously generated, processed, and analyzed to support highly interconnected digital infrastructures. Robust information security mechanisms are essential not only for protecting data and AI models, but also for ensuring the reliability, safety, and ethical deployment of advanced technologies. As information has become a valuable digital asset, it has also emerged as a prime target for increasingly sophisticated cyberattacks aimed at data theft, manipulation, privacy violations, and erosion of trust in digital systems. This challenge is further intensified by the misuse of advanced AI techniques to automate and scale cyberattacks. In response to this evolving threat landscape, this Special Issue of Electronics on “Advanced Technologies for Information Security” seeks high-quality research that explores emerging technologies used for both compromising and defending information security. Topics of interest include, but are not limited to, AI-powered cyberattacks, defensive AI techniques, blockchain-based security solutions, secure autonomous systems, secure cloud infrastructures, and approaches for security assurance and awareness.

Dr. Sharmin Jahan
Guest Editor

Manuscript Submission Information

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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

  • AI-driven cybersecurity
  • emerging cyber threats
  • cyber threat intelligence
  • data privacy and protection
  • secure autonomous systems
  • blockchain for security
  • secure cloud infrastructure
  • security assurance and awareness

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Published Papers (1 paper)

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Research

22 pages, 785 KB  
Article
Investigating the Impact of Supervision Format on Reasoning Performance in Large Language Models
by Nhat Thanh Vu, Md Mamunur Rashid and Fariza Sabrina
Electronics 2026, 15(16), 3683; https://doi.org/10.3390/electronics15163683 - 18 Aug 2026
Viewed by 415
Abstract
Supervised fine-tuning is often discussed in terms of data volume and target quality, but the format of the supervision itself can change the reasoning strategy a model expresses in its outputs. We study this effect on a six-category reasoning benchmark spanning symbol transformation, [...] Read more.
Supervised fine-tuning is often discussed in terms of data volume and target quality, but the format of the supervision itself can change the reasoning strategy a model expresses in its outputs. We study this effect on a six-category reasoning benchmark spanning symbol transformation, text decryption, bit manipulation, gravitational constant estimation, numeral conversion, and unit conversion (drawn from the NVIDIA Nemotron Model Reasoning Challenge). Using NVIDIA Nemotron-3-Nano-30B-A3B with matched LoRA training settings, we compare three symbol-supervision formats: verbose English rule descriptions, compact family tags, and compact formula notation. We hypothesize that supervision renderings bias token-level reasoning priors, and that these priors transfer across task boundaries in multi-task SFT. In the canonical strict-rescore inventory, the best compact tag and formula checkpoints are statistically equivalent in aggregate within a pre-specified ±4-point margin: K8A-800 reaches 72.3% strict-scored overall accuracy and K8B-700 reaches 71.2% (TOST p = 0.003). Compact tags nevertheless provide a cleaner behavioral profile: an earlier K8A-400 checkpoint reaches 66.4% overall, 98.7% gravity accuracy, and 36.9% bit accuracy without the same contamination signatures. In contrast, verbose English rule descriptions are associated with heuristic parroting, with up to 57% of symbol failures at audited verbose checkpoints collapsing to a single remove-operator template, while formula notation is associated with cross-category contamination: numeric-looking predictions appear more often in text decryption (higher at five of six matched training steps under the canonical seed; matched-step means 15.8 vs. 11.7 numeric predictions per 157 text rows), and gravity failures at a representative K8B formula checkpoint shift toward shortcut stubs and explicit g = 9.8/9.81 fallbacks. We further show that checkpoint selection and strict evaluation auditing materially change branch decisions. Across three training seeds, neither compact format shows a consistent aggregate advantage, while the contamination signatures are partly seed-specific: the gravity-shortcut severity difference persists but is not exclusive to the formula branch, and the numeric–text signature does not reproduce under reseeding. These results support treating supervision format as a first-class hyperparameter for multi-task reasoning SFT in large language models—at least in this benchmark-and-model setting—rather than a mere rendering detail. Because such symbolic and procedural reasoning tasks recur in domains including cybersecurity, mathematics, and code generation, the same formatting choices plausibly shape the policy that any later reinforcement-learning stage would inherit, which we flag as future work. Full article
(This article belongs to the Special Issue Advanced Technologies for Information Security)
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