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Systems
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Integration of Cybersecurity, AI, and IoT Technologies
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Integration of Cybersecurity, AI, and IoT Technologies

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Artificial Intelligence and Digital Systems Engineering".

Deadline for manuscript submissions: 15 August 2025 | Viewed by 2403

Special Issue Editor

Dr. Stephane Gagnon

E-Mail Website
Guest Editor
Département des Sciences Administratives, Université du Québec en Outaouais, 101 Saint-Jean-Bosco, Gatineau, QC J8X 3X7, Canada
Interests: information systems; information technology management; business technology management; digital transformation; project management; risk analytics; semantic technologies

Special Issue Information

Dear Colleagues,

Recent literature reviews have pointed out the intersection of Cybersecurity, Artificial Intelligence (AI), and Internet of Things (IoT) as the new “nexus” of our digital experience. The same way “web browsers” and “smart phones” have been our main interfaces, AI coupled with IoT, or AIoT, will soon become our new locus. This Special Issue will showcase contributions that address this intersection; we welcome articles throughout the spectrum of academic research: literature reviews, conceptual frameworks, methodological notes, empirical research, standards development, and implementation reviews. Cross-sectors and sector-specific studies (e.g., energy, healthcare, cities, etc.) are welcomed. Nevertheless, conclusions should attempt to generalize to various or all sectors, addressing at least one or two major gaps identified by recent reviews, including (but not limited to) the following:

  • Enterprise cybersecurity prevention within the AIoT sphere;
  • Cybersecurity incident response automation for AIoT end-users;
  • Humans and ethics in the loop for the regulatory compliance of AIoT systems;
  • Design methods for ensuring explainable AIoT implementations;
  • Small-footprint machine learning algorithms for AIoT applications;
  • Large language model consumption in small AIoT devices;
  • Algorithmic safety and privacy control in AIoT environments;
  • App stores and global AIoT entrepreneurial ecosystems;
  • National development strategies for cybersecurity and AIoT ecosystems.

Dr. Stephane Gagnon
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Systems 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 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

  • cybersecurity
  • Artificial Intelligence (AI)
  • Internet of Things (IoT)
  • AIoT systems
  • enterprise cybersecurity

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Published Papers (2 papers)

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Research

17 pages, 1790 KiB  
Article
Advancing Artificial Intelligence of Things Security: Integrating Feature Selection and Deep Learning for Real-Time Intrusion Detection
by Faisal Albalwy and Muhannad Almohaimeed
Systems 2025, 13(4), 231; https://doi.org/10.3390/systems13040231 - 28 Mar 2025
Viewed by 431
Abstract
The size of data transmitted through various communication systems has recently increased due to technological advancements in the Artificial Intelligence of Things (AIoT) and the industrial Internet of Things (IoT). IoT communications rely on intrusion detection systems (IDS) to ensure secure and reliable [...] Read more.
The size of data transmitted through various communication systems has recently increased due to technological advancements in the Artificial Intelligence of Things (AIoT) and the industrial Internet of Things (IoT). IoT communications rely on intrusion detection systems (IDS) to ensure secure and reliable data transmission, as traditional security mechanisms, such as firewalls and encryption, remain susceptible to attacks. An effective IDS is crucial as evolving threats continue to expose new security vulnerabilities. This study proposes an integrated approach combining feature selection methods and principal component analysis (PCA) with advanced deep learning (DL) models for real-time intrusion detection, significantly improving both computational efficiency and accuracy compared to previous methods. Specifically, five feature selection methods (correlation-based feature subset selection (CFS), Pearson analysis, gain ratio (GR), information gain (IG) and symmetrical uncertainty (SU)) were integrated with PCA to optimise feature dimensionality and enhance predictive performance. Three classifiers—artificial neural networks (ANNs), deep neural networks (DNNs), and TabNet–were evaluated on the RT-IoT2022 dataset. The ANN classifier combined with Pearson analysis and PCA achieved the highest intrusion detection accuracy of 99.7%, demonstrating substantial performance improvements over ANN alone (92%) and TabNet (94%) without feature selection. Key features identified by Pearson analysis included id.resp_p, service, fwd_init_window_size and flow_SYN_flag_count, which significantly contributed to the performance gains. These results indicate that combining Pearson analysis with PCA consistently improves classification performance across multiple models. Furthermore, the deployment of classifiers directly on the original dataset decreased the accuracy, emphasising the importance of feature selection in enhancing AIoT and IoT security. This predictive model strengthens IDS capabilities, enabling early threat detection and proactive mitigation strategies against cyberattacks in real-time AIoT environments. Full article
(This article belongs to the Special Issue Integration of Cybersecurity, AI, and IoT Technologies)
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39 pages, 24264 KiB  
Article
Digital Health Transformation: Leveraging a Knowledge Graph Reasoning Framework and Conversational Agents for Enhanced Knowledge Management
by Abid Ali Fareedi, Muhammad Ismail, Stephane Gagnon, Ahmad Ghazanweh and Zartashia Arooj
Systems 2025, 13(2), 72; https://doi.org/10.3390/systems13020072 - 22 Jan 2025
Viewed by 1081
Abstract
The research focuses on the limitations of traditional systems in optimizing information flow in the healthcare domain. It focuses on integrating knowledge graphs (KGs) and utilizing AI-powered applications, specifically conversational agents (CAs), particularly during peak operational hours in emergency departments (EDs). Leveraging the [...] Read more.
The research focuses on the limitations of traditional systems in optimizing information flow in the healthcare domain. It focuses on integrating knowledge graphs (KGs) and utilizing AI-powered applications, specifically conversational agents (CAs), particularly during peak operational hours in emergency departments (EDs). Leveraging the Cross Industry Standard Process for Data Mining (CRISP-DM) framework, the authors tailored a customized methodology, CRISP-knowledge graph (CRISP-KG), designed to harness KGs for constructing an intelligent knowledge base (KB) for CAs. This KG augmentation empowers CAs with advanced reasoning, knowledge management, and context awareness abilities. We utilized a hybrid method integrating a participatory design collaborative methodology (CM) and Methontology to construct a domain-centric robust formal ontological model depicting and mapping information flow during peak hours in EDs. The ultimate objective is to empower CAs with intelligent KBs, enabling seamless interaction with end users and enhancing the quality of care within EDs. The authors leveraged semantic web rule language (SWRL) to enhance inferencing capabilities within the KG framework further, facilitating efficient information management for assisting healthcare practitioners and patients. This innovative assistive solution helps efficiently manage information flow and information provision during peak hours. It also leads to better care outcomes and streamlined workflows within EDs. Full article
(This article belongs to the Special Issue Integration of Cybersecurity, AI, and IoT Technologies)
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