Human-Centric AI for Cyber Security in Critical Infrastructures

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 August 2025) | Viewed by 1568

Special Issue Editors


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Guest Editor
Department of Risk and Security, Institute for Energy Technology, 1777 Halden, Norway
Interests: artificial intelligence; cyber security; risk management; safety; serious games

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Guest Editor
Department of Applied Data Science, Institute for Energy Technology, 1777 Halden, Norway
Interests: applied informatics; intelligent systems; sustainable development goals; digital sovereignty; software metrics
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Special Issue Information

Dear Colleagues,

We are pleased to invite you to submit your research papers to our Special Issue on “Human-Centric AI for Cyber Security in Critical Infrastructures”.

This special issue aims to explore the intersection of Artificial Intelligence (AI), cyber security, and human-centered design, particularly within the context of Critical Infrastructures (CIs). The objective is to explore how AI can be designed to collaborate effectively with human operators, enhancing both the detection and mitigation of cyber threats in CIs such as energy grids, transportation networks, financial systems, and healthcare services. The key emphasis is on developing AI systems that prioritize human-AI teaming, ensuring that AI augments rather than replaces human decision-making in the context of cyber security.

The purpose of this Special Issue is to advance our understanding and application of human-centric AI in securing CIs. In a landscape where cyber threats are becoming increasingly sophisticated, it is crucial to develop AI systems that can effectively collaborate with human operators, providing them with enhanced tools for prevention, detection, response, and recovery.

This Special Issue aims to gather high-quality empirical, experimental, and theoretical research that presents original and unpublished findings.

Topics of interest for this Special Issue, particularly within the context of CIs, include but are not limited to the following:

  • AI-Augmented Threat Detection and Response;
  • AI-driven Anomaly Detection with Human Oversight;
  • Collaborative AI Systems for Real-Time Cyber Threat Mitigation;
  • Enhancing Cyber Resilience through Human-AI Teaming;
  • Human–AI Collaboration/Teaming in Prevention;
  • Human–AI Collaboration/Teaming in Detection;
  • Human–AI Collaboration/Teaming in Incident Response;
  • Human–AI Collaboration/Teaming in Recovery;
  • Human-Centered Design Principles for AI in Cyber Security;
  • Integration of AI and Human Expertise in Cyber Security;
  • Trust, Accountability, and Ethical Considerations in Human–AI Cyber Security Teams;
  • User-centric AI Interfaces for Security Operations Centre (SOC).

This Special Issue will significantly contribute to the existing literature by addressing a critical gap at the intersection of AI, cyber security, and human-centered design. While much of the current research in AI-driven cyber security focuses on purely technical solutions, this Special Issue will highlight the importance of incorporating human factors into these systems. By emphasizing human–AI collaboration/teaming, this Special Issue will offer new perspectives on how AI can be designed and implemented to work alongside human operators, rather than in isolation.

Papers submitted for consideration should be original and unpublished, focusing on one of the topics covered by the Special Issue. Each submission will be assessed for its relevance, significance of contribution, technical quality, scholarly merit, and clarity of presentation. The journal maintains a strict policy that no submission, or any submission with significant overlap, should be published or under review at another journal or conference during the review process.

We look forward to receiving your contributions.

Dr. Sabarathinam Chockalingam
Dr. Sanjay Misra
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics 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

  • artificial intelligence
  • AI ethics
  • AI governance
  • anomaly detection
  • cooperative AI
  • critical infrastructures
  • cyber–physical systems
  • cyber security
  • human–AI collaboration
  • human–AI partnership
  • human–AI synergy
  • human–AI teaming
  • human-centric AI
  • human–machine interaction
  • incident response
  • interactive AI
  • infrastructure resilience
  • machine learning
  • privacy
  • security operations
  • trustworthy AI

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Published Papers (1 paper)

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Research

29 pages, 966 KB  
Article
You Got Phished! Analyzing How to Provide Useful Feedback in Anti-Phishing Training with LLM Teacher Models
by Tailia Malloy, Laura Bernardy, Omar El Bachyr, Fred Philippy, Jordan Samhi, Jacques Klein and Tegawendé F. Bissyandé
Electronics 2025, 14(19), 3872; https://doi.org/10.3390/electronics14193872 - 29 Sep 2025
Viewed by 785
Abstract
Training users to correctly identify potential security threats like social engineering attacks such as phishing emails is a crucial aspect of cybersecurity. One challenge in this training is providing useful educational feedback to maximize student learning outcomes. Large Language Models (LLMs) have recently [...] Read more.
Training users to correctly identify potential security threats like social engineering attacks such as phishing emails is a crucial aspect of cybersecurity. One challenge in this training is providing useful educational feedback to maximize student learning outcomes. Large Language Models (LLMs) have recently been applied to wider and wider applications, including domain-specific education and training. These applications of LLMs have many benefits, such as cost and ease of access, but there are important potential biases and constraints within LLMs. These may make LLMs worse teachers for important and vulnerable subpopulations including the elderly and those with less technical knowledge. In this work we present a dataset of LLM embeddings of conversations between human students and LLM teachers in an anti-phishing setting. We apply these embeddings onto an analysis of human–LLM educational conversations to develop specific and actionable targets for LLM training, fine-tuning, and evaluation that can potentially improve the educational quality of LLM teachers and ameliorate potential biases that may disproportionally impact specific subpopulations. Specifically, we suggest that LLM teaching platforms either speak generally or mention specific quotations of emails depending on user demographics and behaviors, and to steer conversations away from an over focus on the current example. Full article
(This article belongs to the Special Issue Human-Centric AI for Cyber Security in Critical Infrastructures)
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