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Future Internet
Special Issues
AI-Driven Security, Privacy, and Trust for the Internet of Things
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AI-Driven Security, Privacy, and Trust for the Internet of Things

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

Deadline for manuscript submissions: 31 March 2027 | Viewed by 1740

Editors

Prof. Dr. Francesco Buccafurri

E-Mail Website
Guest Editor
Department of Information Engineering, Infrastructures and Sustainable Energy (DIIES), University Mediterranea of Reggio Calabria, 89122 Reggio Calabria, Italy
Interests: cybersecurity; privacy; trust; blockchain; e-government; Internet of Things; distributed and networked systems
Special Issues, Collections and Topics in MDPI journals
Dr. Sara Lazzaro

E-Mail Website
Guest Editor
Department of Information Engineering, Infrastructures and Sustainable Energy (DIIES), University Mediterranea of Reggio Calabria, 89122 Reggio Calabria, Italy
Interests: cybersecurity; anonymity; Internet of Things; blockchain; artificial intelligence

Special Issue Information

Dear Colleagues,

The Internet of Things is increasingly embedded in everyday and critical environments, including smart homes, industrial systems, healthcare, transportation, and smart cities. By connecting heterogeneous devices and enabling continuous data exchange, IoT ecosystems support intelligent and context-aware services. However, their large scale, dynamic connectivity, and resource constraints also create significant challenges in terms of security, privacy, and trust.

Traditional protection mechanisms are often difficult to apply directly in such environments, as they may not adapt effectively to evolving threats, distributed architectures, and real-time requirements. Artificial intelligence offers new opportunities to address these limitations by enabling security solutions that can learn from data, recognize abnormal behaviours, support automated decision-making, and improve the protection of sensitive information. Nevertheless, the adoption of AI also introduces new concerns related to robustness, explainability, privacy, and trustworthiness.

This Special Issue welcomes original research, reviews, and practical case studies on AI-driven approaches for strengthening security, privacy, and trust in IoT ecosystems. Topics of interest include intelligent security architectures, privacy-preserving and trustworthy AI methods, secure edge intelligence, resilient IoT applications, and experimental or real-world evaluations.

Prof. Dr. Francesco Buccafurri
Dr. Sara Lazzaro
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

  • Internet of Things
  • IoT security
  • artificial intelligence
  • privacy-preserving technologies
  • trust management
  • edge intelligence
  • intrusion detection
  • federated learning
  • cyber-resilience
  • trustworthy AI

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Published Papers (3 papers)

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Research

33 pages, 1802 KB  
Article
TDT-Pipe: A Security-Aware Architecture for Trustworthy IoT Digital Twins
by Vincenzo De Angelis
Future Internet 2026, 18(9), 458; https://doi.org/10.3390/fi18090458 - 27 Aug 2026
Viewed by 237
Abstract
Digital Twins rely on continuous data exchanges between physical assets, Internet of Things devices, communication infrastructures, and software representations. In MQTT-based deployments, however, successful message delivery does not guarantee that a telemetry value is authentic, authorized, fresh, structurally valid, or appropriate for updating [...] Read more.
Digital Twins rely on continuous data exchanges between physical assets, Internet of Things devices, communication infrastructures, and software representations. In MQTT-based deployments, however, successful message delivery does not guarantee that a telemetry value is authentic, authorized, fresh, structurally valid, or appropriate for updating the active twin state. This paper presents TDT-Pipe, a security-aware telemetry-to-Digital-Twin pipeline that separates message transport from update admission. The architecture integrates secure publication, registry-based device control, edge validation, provenance generation, controlled state management, quarantine, auditing, and policy-based views. Telemetry messages are protected through payload digests and device-specific HMACs, while validation predicates classify them as verified, suspicious, or untrusted and map each class to apply, quarantine, or reject actions. The formal model links active values to the events that produced them and supports security properties covering authentication, integrity, freshness, replay protection, controlled state modification, audit traceability, and view confinement. A lightweight prototype is used to assess the feasibility of the approach. The evaluation indicates that the proposed validation, provenance, and controlled-admission mechanisms can be integrated into IoT Digital Twin pipelines while keeping the processing overhead limited. Full article
(This article belongs to the Special Issue AI-Driven Security, Privacy, and Trust for the Internet of Things)
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31 pages, 5508 KB  
Article
AERO-GUARD: A Post-Quantum Mutual Authentication Drone Protocol with Homomorphic Encryption for Secure Road Surveillance in Smart Cities
by Albandari Alsumayt, Arwa Almalki, Reema Almassary, Hotoon Alghamdi, Reemas Alqahtani, Ryouf Alzuabie, Reham Alharthi and Naya Nagy
Future Internet 2026, 18(8), 412; https://doi.org/10.3390/fi18080412 - 4 Aug 2026
Viewed by 683
Abstract
This paper presents AERO-GUARD, a formally verified drone authentication and road surveillance system that integrates Kyber post-quantum key encapsulation, physical unclonable functions (PUFs), decentralized IPFS-based identity storage, and blockchain-anchored audit logging. AERO-GUARD operates across three phases, key provisioning, enrollment, and authentication, enforcing mutual [...] Read more.
This paper presents AERO-GUARD, a formally verified drone authentication and road surveillance system that integrates Kyber post-quantum key encapsulation, physical unclonable functions (PUFs), decentralized IPFS-based identity storage, and blockchain-anchored audit logging. AERO-GUARD operates across three phases, key provisioning, enrollment, and authentication, enforcing mutual authentication, replay resistance, and privacy-preserving comparison through an off-chain evaluator (OCE) that performs homomorphic subtraction on encrypted PUF responses without accessing plaintext secrets. The protocol is modeled and verified using ProVerif 2.05 under the Dolev–Yao adversary model. To evaluate the system beyond theoretical verification, a simulation environment was developed to replicate realistic road conditions, incorporating a simulated road network and a virtual drone traversing monitored routes. An AI model is deployed to perform real-time detection of suspicious and anomalous activities along the road. All detection events are surfaced through a centralized monitoring dashboard that provides authorized personnel with live alerts, a drone camera livestream with detection annotations, and contextual drone telemetry, enabling timely and informed incident response. Formal verification results demonstrate that AERO-GUARD satisfies the targeted security properties, including mutual authentication, secrecy preservation, and replay resistance, confirming the protocol’s resilience against common authentication attacks. Full article
(This article belongs to the Special Issue AI-Driven Security, Privacy, and Trust for the Internet of Things)
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25 pages, 1006 KB  
Article
Network Orchestration Framework Design Using AI-Driven Automation and Cybersecurity
by Tasneem Annahdi, Albandari Alsumayt and Majid Alshammari
Future Internet 2026, 18(8), 394; https://doi.org/10.3390/fi18080394 - 27 Jul 2026
Viewed by 447
Abstract
This paper addresses human error in network orchestration systems and the high cost and resource requirements of integrating artificial intelligence (AI) for network orchestration. It proposes a framework for implementing an AI decision-maker and automation. Data are fed into the AI decision-maker to [...] Read more.
This paper addresses human error in network orchestration systems and the high cost and resource requirements of integrating artificial intelligence (AI) for network orchestration. It proposes a framework for implementing an AI decision-maker and automation. Data are fed into the AI decision-maker to trigger designated automation robots’ tasks or notify IT specialists to gradually implement automated robots, ensuring efficient resource use, reducing costs, and enhancing productivity. We evaluated the proposed method in a simulation with genuinely uncertain outcomes, across 20 independent runs: the AI decision-maker reached 78.3% accuracy against an estimated 79.1% achievable ceiling, and the proposed framework reduced operational cost by 61.4 ± 0.7% relative to fully manual operation—the best of six operating policies in the training environment—while an explicit sensitivity guard, rather than the learned model, accounts for the absence of security incidents; under distribution shift, the framework retains 43.2 ± 0.8% savings, second only to a hand-tuned rule-based router that requires environment-specific threshold calibration. However, the proposed method requires an IT specialist to implement it properly, and the AI model’s accuracy depends on the amount of input data. In the end, we recommend that future work conduct a study focused on AI decision-makers, test the proposed method on real-world companies, and implement AI decision-makers across various departments to cover a broader range of the company’s systems. Full article
(This article belongs to the Special Issue AI-Driven Security, Privacy, and Trust for the Internet of Things)
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