Advances in Intelligent Cybersecurity Systems

A Special Issue of Future Internet (ISSN 1999-5903) belonging to the section "Cybersecurity".

Deadline for manuscript submissions: 31 May 2027 | Viewed by 1398

Editors


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Guest Editor
Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
Interests: cyberspace security; intelligent network attack and defense; intrusion detection; data fusion
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Cryptography, Information Engineering University, Zhengzhou 450000, China
Interests: cybersecurity game intelligence; moving target defense; cyber deception defense; complex network attack–defense strategy simulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Tthe research scope, technological boundaries, and application scenarios of intelligent cybersecurity systems are continuously expanding. The widespread application of cloud computing, edge computing, the Internet of Things, industrial automation, cyber–physical systems, smart grids, autonomous driving systems, and next-generation communication networks has created increasingly close interactions among digital systems, physical processes, and social services. In this context, cybersecurity is no longer limited to the protection of a single network, device, or information system. Instead, it is gradually evolving into a comprehensive assurance problem involving data circulation, trusted communication, intelligent decision-making, continuous system operation, and the resilience of critical infrastructures. Complex, dynamic, and intelligent threat environments also pose new challenges to traditional security mechanisms that mainly rely on static rules, perimeter defense, and passive response.

In response to these changes, building next-generation intelligent cybersecurity systems with capabilities of intelligent perception, threat identification, adaptive defense, trustworthy decision-making, collaborative response, and resilient recovery has become an important research direction in cybersecurity, artificial intelligence, communication networks, control science, and critical infrastructure protection. Intelligent algorithms provide new technical pathways for attack detection, anomaly identification, and risk assessment. Data-driven methods enhance security situation understanding and dynamic response capabilities, while trustworthy computing, privacy protection, and resilient design provide essential support for secure operation in complex intelligent environments. Therefore, research on intelligent cybersecurity systems needs to strengthen the integration of theoretical foundations, key technologies, system architectures, and practical applications, promoting the transition of security protection from isolated, static, and passive modes toward proactive awareness, collaborative defense, and trustworthy resilience assurance.

This Special Issue aims to provide a broad academic platform for presenting the latest advances in intelligent cybersecurity systems, with a focus on innovative contributions in intelligent security theories, algorithmic methods, system architectures, security mechanisms, and engineering applications. Topics of interest include, but are not limited to, AI-driven threat detection and defense, intrusion detection and response, data security and privacy protection, trustworthy machine learning, cyber–physical system security, industrial control system security, secure communication, blockchain security, digital forensics, risk assessment, resilient system design, and critical infrastructure protection. This Special Issue welcomes high-quality original research articles, review papers, and case studies, and encourages studies on theoretical modeling, algorithm design, experimental validation, real-world deployment, benchmark dataset construction, and interdisciplinary applications, with the aim of further advancing the security, reliability, trustworthiness, privacy, and resilient recovery capabilities of intelligent cybersecurity systems.

We welcome original high-quality submissions on topics including, but not limited to:

  • Intelligent cybersecurity theories, models, and system architectures;
  • AI-driven threat detection, attack identification, and anomaly detection;
  • Intelligent intrusion detection and adaptive response mechanisms;
  • Cybersecurity for cloud computing, edge computing, and distributed systems;
  • Internet of Things security and industrial Internet security;
  • Big data security, data mining security, and privacy-preserving data analysis;
  • Cyber–physical system security and resilient control under cyber-attacks;
  • Industrial control system security and smart grid cybersecurity;
  • Secure communication protocols and trusted network transmission;
  • Trustworthy machine learning and secure artificial intelligence systems;
  • Adversarial attacks, adversarial defense, and robustness evaluation;
  • Privacy protection, access control, authentication, and trusted computing;
  • Blockchain security, smart contract security, and decentralized trust mechanisms;
  • Digital forensics, attack tracing, and cyber threat intelligence;
  • Risk assessment, vulnerability analysis, and security evaluation;
  • Automated security verification, testing, and validation of intelligent systems;
  • Resilient system design, fault tolerance, and recovery mechanisms;
  • Security benchmark datasets, simulation platforms, and experimental validation;
  • Intelligent cybersecurity applications in smart grids, autonomous vehicles, UAVs, healthcare, finance, and next-generation communication networks;
  • Ethical, legal, and trustworthy issues in intelligent cybersecurity systems.

Prof. Dr. Zhihong Tian
Dr. Jinglei Tan
Dr. Xiao Cai
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. Future Internet is an international peer-reviewed open access monthly 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 1800 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

  • intelligent cybersecurity systems
  • artificial intelligence security
  • intrusion detection
  • threat detection and defense
  • data security
  • privacy protection
  • trustworthy machine learning
  • cyber–physical system security
  • industrial control system security
  • secure communication networks
  • critical infrastructure protection
  • cyber-attack defense confrontation
  • intelligent deception defense
  • intelligent industrial control systems
  • intelligent game-theoretic decision-making
  • resilient systems

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Published Papers (1 paper)

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Research

20 pages, 3317 KB  
Article
Adaptive Feature Distillation-Based Continuous Authentication Against RF Fingerprint Drift for Power Equipment
by Fan Luo, Siqin Fan, Xiangjun Li and Weijie Xu
Future Internet 2026, 18(9), 457; https://doi.org/10.3390/fi18090457 - 27 Aug 2026
Viewed by 195
Abstract
RF fingerprints of power equipment drift over time due to environmental changes and device aging, which progressively degrades the performance of authentication systems built on fixed models. Existing incremental learning methods tend to either forget historical devices or fail to adapt to new [...] Read more.
RF fingerprints of power equipment drift over time due to environmental changes and device aging, which progressively degrades the performance of authentication systems built on fixed models. Existing incremental learning methods tend to either forget historical devices or fail to adapt to new distributions when dealing with such drift. We propose an incremental update strategy tailored to this scenario and evaluate it on a self-constructed 64-dimensional simulated drift dataset. At each update, gradient importance per feature channel is computed from the current batch, and three feature-level distillation terms (channel MSE, covariance alignment, and spatial attention) keep the new model’s representations close to the old one. The distillation strength decays exponentially with update steps, enabling strong preservation of old knowledge early and more flexible adaptation later. A small memory buffer mixes old samples into each training batch to further reinforce historical recognition. On the synthetic dataset, our method achieves higher final historical accuracy than static, fine-tuning, EWC, and LwF baselines. Ablation studies confirm that the multi-level distillation, dynamic decay, and momentum-smoothed channel weights each contribute positively. These preliminary simulation-based results indicate that the method shows promise in alleviating forgetting caused by fingerprint drift while maintaining adaptability to new fingerprints within the synthetic evaluation framework. Full article
(This article belongs to the Special Issue Advances in Intelligent Cybersecurity Systems)
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