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Recent Trends in Cybersecurity, Privacy, and Digital Trust

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

Deadline for manuscript submissions: 15 September 2026 | Viewed by 650

Special Issue Editors


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Guest Editor
Department of Computer Science, University of Hawaii-Hilo, Hilo, HI 96720, USA
Interests: cybersecurity; privacy; digital trust; cryptography
National Renewable Energy Laboratory, Golden, CO 80401, USA
Interests: computational intelligence; machine learning; cyber physical system; cyber security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue looks at the latest trends and breakthroughs in cybersecurity, privacy, and digital trust. It focuses on strengthening digital infrastructure and protecting information systems. The featured research explores key technical areas, including cryptographic protocols, intrusion detection, access control, threat intelligence, and privacy-preserving computing. The volume also examines the connections between technology and social–legal frameworks, such as security policy, ethics, and regulatory compliance. Together, these advancements offer crucial insights into data protection, secure communication, and resilience against sophisticated cyber threats, supporting the creation of safe, trustworthy, and sustainable digital ecosystems.

Prof. Dr. Shawon Rahman
Dr. Shuva Paul
Guest Editors

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Keywords

  • cybersecurity
  • privacy
  • digital trust
  • cryptography
  • intrusion detection
  • threat intelligence
  • access control
  • privacy-preserving computation
  • resilient systems
  • regulatory compliance

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Published Papers (1 paper)

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Research

32 pages, 1742 KB  
Article
Phase-Dynamic Model of User Interactions for Protecting Recommender Systems from Poisoning Attacks
by Serhii Semenov, Volodymyr Mikhav, Yelyzaveta Meleshko, Nataliya Paranyak and Maxim Pochebut
Appl. Sci. 2026, 16(8), 3769; https://doi.org/10.3390/app16083769 - 12 Apr 2026
Viewed by 239
Abstract
Poisoning and shilling attacks remain a serious threat to recommender systems, especially as attackers increasingly mimic plausible profile statistics. This paper proposes an architecture-independent behavioral detection layer that models user interactions as short-window phase-dynamic trajectories rather than static aggregates. Interaction logs are transformed [...] Read more.
Poisoning and shilling attacks remain a serious threat to recommender systems, especially as attackers increasingly mimic plausible profile statistics. This paper proposes an architecture-independent behavioral detection layer that models user interactions as short-window phase-dynamic trajectories rather than static aggregates. Interaction logs are transformed into temporal signals, reconstructed in phase space by delay embedding, and summarized by a compact 15-dimensional portrait combining recurrence-based, entropy-based, spectral, and stabilizing statistical descriptors. In a controlled targeted injection protocol evaluated over 10 independent runs, the statistical baseline achieved PR-AUC = 0.723 ± 0.037 and TPR@1%FPR = 0.029 ± 0.006, the dynamic block achieved PR-AUC = 0.831 ± 0.011 and TPR@1%FPR = 0.220 ± 0.050, and the full portrait achieved PR-AUC = 0.872 ± 0.017 and TPR@1%FPR = 0.291 ± 0.043. Sensitivity analysis showed that recurrence-only descriptors were parameter-sensitive, whereas the extended dynamic block formed a stable high-performance region across a broad range of embedding settings. An IQR-normalized aggregated risk score further demonstrated clear post-window regime separation during injection periods. The results indicate that poisoning attacks primarily deform the temporal organization of behavior rather than only first-order statistics. The proposed phase-dynamic portrait is therefore best interpreted as a complementary behavioral risk-scoring layer for auditing, filtering, and monitoring rather than as a standalone defense. Full article
(This article belongs to the Special Issue Recent Trends in Cybersecurity, Privacy, and Digital Trust)
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