AI-Driven Cybersecurity, Resilience, and Trust Frameworks for Future Urban IoT Systems

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

Deadline for manuscript submissions: 15 October 2026 | Viewed by 3138

Special Issue Editors


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Guest Editor
1. Security Science Laboratory, Lewis University, Romeoville, IL 60446, USA
2. Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
Interests: anomaly detection; artificial narrow intelligence; intrusion detection evaluation
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Guest Editor
Engineering, Computing and Mathematical Science, Lewis University, Romeoville, IL 60446, USA
Interests: FPGA-based AI acceleration; low-power hardware optimization; hardware–software co-design

Special Issue Information

Dear Colleagues,

Urban Internet of Things (IoT) systems — spanning smart transportation networks, connected and autonomous electric vehicles, intelligent energy grids, environmental monitoring infrastructures, and large-scale cloud and edge computing platforms — are becoming essential to the daily operation and sustainability of modern cities. These systems enable real-time decision-making, automation, and service optimization, yet they face an expanding spectrum of sophisticated cyber threats, ranging from large-scale distributed attacks to stealthy, AI-generated intrusions.

Ensuring the cybersecurity, resilience, and trustworthiness of these infrastructures requires innovative strategies that safeguard both the digital and physical layers of urban services. Traditional security mechanisms are no longer sufficient in the face of emerging attack vectors, such as adversarial AI, supply chain compromises, and quantum-era cryptographic threats. The shift toward zero-trust architectures, privacy-preserving computation, and autonomous threat response is now critical for sustaining uninterrupted and secure service delivery.

Artificial Intelligence (AI) and Machine Learning (ML) are central to this transformation, powering intrusion detection, anomaly detection, and predictive threat intelligence. However, these solutions often face challenges in resource-constrained environments — including edge devices, embedded IoT platforms, and in-vehicle systems — where energy efficiency, low latency, and real-time adaptability are paramount. Addressing these demands calls for hardware–software co-design, including FPGA and ASIC accelerators, power-aware AI model deployment, and digital twin-based security testing for proactive vulnerability assessment.

This Special Issue of Electronics invites original research and reviews on advanced methods, architectures, and frameworks that enhance the cybersecurity, resilience, and trust of urban IoT ecosystems. Contributions may address secure communication protocols, post-quantum cryptographic solutions, resilient and fault-tolerant system design, federated and distributed learning for IoT security, and real-time autonomous response systems.

Topics of interest include, but are not limited to:

  • AI/ML-based intrusion detection, anomaly detection, and predictive threat intelligence for urban IoT;
  • Zero-trust architectures and continuous authentication mechanisms for urban infrastructures;
  • Post-quantum cryptography and lightweight encryption for IoT and IIoT devices;
  • Hardware–software co-design for secure, energy-efficient, and real-time IoT systems;
  • FPGA and ASIC accelerator designs for AI-based security and privacy applications;
  • Adversarial machine learning defenses and robust AI for IoT cybersecurity;
  • Digital twin frameworks for urban IoT security testing, simulation, and resilience analysis;
  • Privacy-preserving and federated learning methods for multi-domain IoT security;
  • Resilient architectures for connected and autonomous vehicle systems;
  • Low-power, real-time edge computing platforms for critical urban services;
  • Secure and sustainable cloud/edge data centers for large-scale IoT workloads;
  • Dataset creation, benchmarking, and evaluation for next-generation IoT security research.

By integrating cutting-edge AI-driven security, energy-efficient hardware acceleration, and resilience-focused architectures, this Special Issue aims to advance the creation of scalable, sustainable, and secure urban IoT systems capable of withstanding evolving cyber threats while maintaining public trust and operational continuity.

Dr. Jake Cho
Dr. Victoria Kim
Guest Editors

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Keywords

  • urban Internet of Things (IoT)
  • cybersecurity
  • resilience
  • zero-trust architecture
  • post-quantum cryptography
  • adversarial machine learning
  • hardware–software co-design
  • FPGA accelerators
  • ASIC accelerators
  • energy-efficient AI
  • intrusion detection
  • anomaly detection
  • federated learning
  • digital twin
  • connected vehicles
  • smart transportation
  • sustainable computing
  • privacy-preserving AI

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Published Papers (4 papers)

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Research

26 pages, 894 KB  
Article
Differential and Linear Cryptanalysis of the IoT-Friendly MGFN Block Cipher
by Namil Kim, Wonwoo Song, Seungjun Baek, Yongjin Jeon, Giyoon Kim, Changhoon Lee and Jongsung Kim
Electronics 2026, 15(5), 1126; https://doi.org/10.3390/electronics15051126 - 9 Mar 2026
Viewed by 399
Abstract
Developed in 2023, the Modified Generalized Feistel Network (MGFN) is a block cipher that complies with Malaysia’s national cryptographic and cybersecurity policies. MGFN is a 64-bit block cipher with a 128-bit master key, specifically designed to deliver lightweight cybersecurity in resource-constrained Internet of [...] Read more.
Developed in 2023, the Modified Generalized Feistel Network (MGFN) is a block cipher that complies with Malaysia’s national cryptographic and cybersecurity policies. MGFN is a 64-bit block cipher with a 128-bit master key, specifically designed to deliver lightweight cybersecurity in resource-constrained Internet of Things (IoT) environments. In this paper, we analyze the security of the full-round MGFN against differential and linear cryptanalysis. We present concrete key recovery strategies for both attacks by employing multiple peeling-off steps. As a result, for the first time, we demonstrate a practical differential cryptanalysis of the full-round MGFN within a realistic time bound. In addition, we propose a practical linear cryptanalysis of the round-reduced MGFN. Our results provide the first practical security assessment of MGFN and offer concrete insights into its resistance against differential and linear cryptanalysis, thereby supporting the design and evaluation of lightweight block ciphers for IoT environments. Full article
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17 pages, 615 KB  
Article
Hybrid Time–Position Embedding for Provenance-Based Intrusion Detection
by Seonghyeon Gong, Jake Cho and Kyuwon Ken Choi
Electronics 2026, 15(5), 1004; https://doi.org/10.3390/electronics15051004 - 28 Feb 2026
Viewed by 477
Abstract
Provenance-based Intrusion Detection Systems (IDSs) model the causal relationships between security events through a provenance graph and learn contextual information to detect Advanced Persistent Threats (APTs) effectively. However, existing provenance graph representation methods fail to fully reflect the characteristics of security domain data [...] Read more.
Provenance-based Intrusion Detection Systems (IDSs) model the causal relationships between security events through a provenance graph and learn contextual information to detect Advanced Persistent Threats (APTs) effectively. However, existing provenance graph representation methods fail to fully reflect the characteristics of security domain data and the semantic information embedded in system logs, resulting in limited learning efficiency and detection accuracy. This paper proposes a provenance representation method that effectively captures security context from system log data. The proposed method improves the performance of provenance-based IDSs by combining (1) a provenance graph construction technique that transforms meaningful string attributes—such as command lines, process names, and file paths—into vector representations to extract semantic information in the security context, (2) a hybrid time–position embedding technique for capturing causal relationships between events, and (3) an iterative refinement learning strategy tailored to the characteristics of system log data. Experimental results using the DARPA Transparent Computing Engagement 3 (E3) benchmark dataset for APT detection demonstrate that our method achieves improved accuracy compared to existing approaches while significantly accelerating convergence during iterative training. These results suggest that the proposed embedding technique can more effectively capture abnormal temporal patterns, such as the long dwell times characteristic of APT attacks. Full article
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34 pages, 780 KB  
Article
Rethinking Ransomware Protection Targets for AI Systems
by Cheon-Ho Min and Jin Kwak
Electronics 2026, 15(4), 770; https://doi.org/10.3390/electronics15040770 - 11 Feb 2026
Viewed by 750
Abstract
Artificial intelligence (AI) systems have become operational infrastructure whose value is increasingly dominated by trained models, behavioral configurations, and decision-making logic rather than by software binaries alone. As a result, ransomware threats against AI systems cannot be adequately addressed by conventional recovery strategies [...] Read more.
Artificial intelligence (AI) systems have become operational infrastructure whose value is increasingly dominated by trained models, behavioral configurations, and decision-making logic rather than by software binaries alone. As a result, ransomware threats against AI systems cannot be adequately addressed by conventional recovery strategies that assume service availability can be restored through file and code recovery. In AI environments, assets such as model parameters, training data, inference pipelines, and safety policies constitute primary attack targets, and their compromise can invalidate system behavior even when files are successfully restored. This study re-examines ransomware threats against AI systems from an asset-based protection perspective and demonstrates why traditional recovery assumptions structurally fail in AI-centric environments. Based on this analysis, we show that protection mechanisms limited to file integrity are insufficient and must be extended to include behavioral consistency and decision-making reliability. To address this gap, we propose a behavior-aware ransomware protection methodology, implemented as the Behavior-Aware Integrity Protection System (BIPS). BIPS augments existing ransomware response processes by redefining protection targets, establishing behavioral baselines, verifying post-recovery behavioral integrity, and supporting risk-based operational decisions. This work contributes by reframing ransomware threats against AI systems as an issue rooted in protection scope and recovery assumptions rather than isolated attack techniques, thereby extending ransomware response for AI systems toward a reliability- and risk-oriented protection framework. Full article
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11 pages, 428 KB  
Article
RMF-A: An Availability Assurance Framework for Quantitative Evaluation of Operational Resilience
by Cheon-Ho Min and Jin Kwak
Electronics 2025, 14(23), 4644; https://doi.org/10.3390/electronics14234644 - 26 Nov 2025
Viewed by 831
Abstract
Recent data center incidents have revealed that certification under ISO 22301 and ISO/IEC 27001 does not guarantee real operational resilience. This study presents the Availability Assurance Framework (RMF-A), an extension of the NIST Risk Management Framework that introduces an Availability Assurance Phase. RMF-A [...] Read more.
Recent data center incidents have revealed that certification under ISO 22301 and ISO/IEC 27001 does not guarantee real operational resilience. This study presents the Availability Assurance Framework (RMF-A), an extension of the NIST Risk Management Framework that introduces an Availability Assurance Phase. RMF-A combines ISO-based management controls with NIST’s evidence-driven assessment using the Availability Evidence Model (AEM) and the Availability Assurance Index (AAI). AEM defines measurable indicators—recovery rate (RR), recovery time (RTO), and Detection Effectiveness (DET)—and AAI aggregates them into a quantitative assurance score. Validation using three open datasets—Google Cluster Trace, Azure Cloud Trace, and LANL HPC Logs—showed consistent assurance results: Google (AAI = 0.758, ATO-Conditional), Azure (AAI = 0.720, ATO-Conditional), and LANL HPC (AAI = 0.744, ATO-Conditional). The results confirm that RMF-A provides a reproducible, evidence-based approach to quantify operational resilience and ensure continuous availability. Full article
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