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Secure and Intelligent IoT & CPS: AI Driven Attack–Defense, Network...
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Secure and Intelligent IoT & CPS: AI Driven Attack–Defense, Network Analysis and Smart Data Protection

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 15 July 2026 | Viewed by 996

Special Issue Editors

Dr. Hansong Xu

E-Mail Website
Guest Editor
School of Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: internet of things; machine learning and cybersecurity
Special Issues, Collections and Topics in MDPI journals
Dr. Cheng Qian

E-Mail Website
Guest Editor
Department of Computer Science and Information Technology, Hood College, Frederick, MD 21701, USA
Interests: internet of things; cybersecurity; machine learning
Dr. Hengshuo Liang

E-Mail Website
Guest Editor
Engineering & Technology, School of, University of Washington, Tacoma, WA 98402, USA
Interests: cybersecurity; CPS; internet of things; deep learning, and network

Special Issue Information

Dear Colleagues,

The integration of Internet‑of‑Things (IoT) and cyber–physical systems (CPS) with AI is transforming modern infrastructure by enabling cross‑layer communication, intelligent resource allocation and data‑driven decision‑making. At the same time, this convergence creates new vulnerabilities and privacy risks: Industry 4.0 CPS connect sensors, actuators, and cloud services, and this integration exposes networks to emerging cyber threats. Many current security‑by‑design approaches focus only on the design phase and fail to embed mitigation strategies throughout the CPS life cycle. To ensure resilient operation, advanced security technologies, such as machine learning, federated learning, blockchain and digital twins, must be integrated into IoT/CPS, enabling AI‑driven anomaly detection and attack mitigation. Addressing these challenges is essential for advancing optimization, predictive maintenance, intelligent control, and cybersecurity.

This Special Issue invites original research papers, short communications, and review articles that explore how optimization and machine‑learning techniques can safeguard IoT/CPS environments, improve real‑time decision‑making and enhance system intelligence. Topics of interest include the following:

  • Secure & Intelligent IoT/CPS Networks: Optimization of wireless protocols and cross‑layer architectures for high‑reliability, low‑latency communication in IoT/CPS, including 5G/6G and time‑sensitive networking (TSN).
  • AI‑Driven Autonomy & Control: Machine‑learning techniques for autonomous and adaptive control of CPS components (robots, drones, vehicles) and for network intrusion detection and resilience.
  • Smart Data Protection & Privacy: Technologies to protect the integrity and confidentiality of large‑scale data streams. Approaches may include privacy‑preserving analytics (federated learning, differential privacy), cryptographic methods, blockchain‑based integrity assurance, and digital‑twin–assisted resilience to handle the expanded attack surface and safeguard sensitive information.
  • Intelligent Sensing & Vision: Advanced AI methods for real‑time defect detection, object recognition, and anomaly sensing in smart environments; leveraging edge computing and private networks for low‑latency processing.
  • LLMs for CPS: Development and application of large language models tailored for CPS tasks, including predictive analytics, process optimization, and cross‑domain learning, with emphasis on efficient training and deployment.

Dr. Hansong Xu
Dr. Cheng Qian
Dr. Hengshuo Liang
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

  • secure and intelligent IoT/CPS
  • edge AI
  • LLM
  • federated learning
  • 5G/6G networks
  • digital twins
  • smart data protection
  • cross layer security

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Published Papers (2 papers)

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Research

21 pages, 1059 KB  
Article
Lightweight MLP-Based Feature Extraction with Linear Classifier for Intrusion Detection System in Internet of Things
by Jisi Chandroth and Jehad Ali
Electronics 2026, 15(8), 1604; https://doi.org/10.3390/electronics15081604 - 12 Apr 2026
Viewed by 386
Abstract
The Internet of Things (IoT) comprises diverse devices connected through heterogeneous communication protocols to deliver a wide range of services. However, the complexity and scale of IoT networks make them difficult to secure. Network intrusion detection systems (NIDSs) have therefore become essential for [...] Read more.
The Internet of Things (IoT) comprises diverse devices connected through heterogeneous communication protocols to deliver a wide range of services. However, the complexity and scale of IoT networks make them difficult to secure. Network intrusion detection systems (NIDSs) have therefore become essential for identifying malicious activities and protecting IoT environments across many applications. Although recent deep learning (DL)-based IDS approaches achieve strong detection performance, they often require substantial computation and storage, which limits their practicality on resource-constrained IoT devices. To balance detection accuracy with computational efficiency, we propose a lightweight deep learning model for IoT intrusion detection. Specifically, our method learns compact, intrusion-relevant representations from traffic features using a two-layer multi-layer perceptron (MLP) embedding backbone, followed by a linear SoftMax classification head for multi-class attack detection. We evaluate the proposed approach on three benchmark datasets, CICIDS2017, NSL-KDD, and CICIoT2023, and the results show strong performance, achieving 99.85%, 99.21%, and 98.45% accuracy, respectively, while significantly reducing model size and computational overhead. The experimental results demonstrate that the proposed method achieves excellent classification performance while maintaining a lightweight design, with fewer parameters and lower FLOPs than existing approaches. Full article
(This article belongs to the Special Issue Secure and Intelligent IoT & CPS: AI Driven Attack–Defense, Network Analysis and Smart Data Protection)
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23 pages, 6306 KB  
Article
Trustless Federated Reinforcement Learning for VPP Dispatch
by Xin Zhang and Fan Liang
Electronics 2026, 15(6), 1303; https://doi.org/10.3390/electronics15061303 - 20 Mar 2026
Viewed by 327
Abstract
Large-scale Virtual Power Plants (VPPs) are increasingly essential as Distributed Energy Resources (DERs) assume ancillary service duties once supplied by conventional generation, yet scaling a VPP exposes a persistent trilemma among economic efficiency, data privacy, and operational security. Centralized coordination can approach optimal [...] Read more.
Large-scale Virtual Power Plants (VPPs) are increasingly essential as Distributed Energy Resources (DERs) assume ancillary service duties once supplied by conventional generation, yet scaling a VPP exposes a persistent trilemma among economic efficiency, data privacy, and operational security. Centralized coordination can approach optimal revenue but requires collecting fine-grained DER operational data and creates a single point of compromise. Federated Learning (FL) mitigates raw data centralization by keeping measurements and experience local, but it introduces a fragile trust assumption that the aggregator will correctly and fairly combine model updates. This trust gap is acute in reinforcement learning-based VPP control because aggregation deviations, including selectively dropping updates, manipulating weights, replaying stale models, or injecting a replacement model, can silently bias the learned policy and degrade both profit and compliance. We propose a zero-knowledge federated reinforcement learning framework for trustless VPP coordination in which each DER trains a local deep reinforcement learning agent to solve a multi-objective dispatch problem that balances ancillary service revenue against battery degradation under operational and grid constraints, while the global aggregation step is made externally verifiable. In each round, participants bind membership via signed receipts and commit to their updates, and the aggregator produces a zk-SNARK, proving that the published global parameters equal the agreed aggregation rule applied to the receipt-bound set of committed updates under a fixed-point encoding with range constraints. Verification is lightweight and can be performed independently by each DER, removing the need to trust the aggregator for aggregation integrity without centralizing raw DER operational data or trajectories. The proposed design does not aim to hide model updates from the aggregator. Instead, it provides external verifiability of the aggregation computation while keeping raw measurements and local experience. We formalize the threat model and verifiable security properties for aggregation correctness and update inclusion, present a circuit construction with proof complexity characterized by model dimension and fleet size, and evaluate the approach in power and cyber co-simulation on the IEEE 33 bus feeder with ancillary service signals. Results show near-centralized economic performance under benign conditions and improved robustness to aggregator side deviations compared to standard federated reinforcement learning. Full article
(This article belongs to the Special Issue Secure and Intelligent IoT & CPS: AI Driven Attack–Defense, Network Analysis and Smart Data Protection)
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