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Recent Progress in Embedded and Internet of Things (IoT) Systems Security

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

Deadline for manuscript submissions: 20 November 2026 | Viewed by 817

Editors


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Guest Editor
School of Computing, Southern Illinois University, 1230 Lincoln Drive, Carbondale, IL 62901, USA
Interests: next generation networks; Internet-of-Things; cognitive radio networks; software defined networks; green communication and wireless network security

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Guest Editor
Department of Computer Science, Southeast Missouri State University, Cape Girardeau, MO 63701, USA
Interests: peer-to-peer (P2P) networks; federated learning (FL); fog and edge computing; blockchain and applied cryptography; post-quantum and quantum cryptography; AI & machine learning in cybersecurity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Embedded and IoT systems are increasingly vital in healthcare, energy, transportation, and manufacturing. Their rapid expansion, however, has created urgent security and privacy challenges. This Special Issue focuses on recent progress in protecting these systems, highlighting practical and well-defined approaches.

The scope is organized around three themes: 

  • Secure Architectures and Cryptography—lightweight cryptography, resilient communication protocols, and secure hardware/system models.
  • Threat Detection and Data Protection—AI/ML-based threat detection, privacy-preserving computation, and advanced access control mechanisms.
  • Security in Critical Applications—industrial IoT, critical infrastructure protection, zero-trust approaches, and real-world deployments.

We welcome original research and reviews that can contribute to advancing the security and resilience of embedded and IoT ecosystems.

Dr. Ansuman Bhattacharya
Dr. Indranil Roy
Guest Editors

Manuscript Submission Information

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Keywords

  • IoT security
  • cyber-physical systems
  • embedded systems
  • industrial IoT
  • data privacy
  • secure architectures
  • edge and fog computing
  • critical infrastructure protection
  • zero-trust security
  • lightweight protocols

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Published Papers (2 papers)

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Research

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29 pages, 1899 KB  
Article
Automated Acoustic Side-Channel Attack on Keyboard Inputs via Combined Video–Audio Analysis
by Dario Vranješ, Ivo Stančić, Marin Bugarić and Toni Perković
Electronics 2026, 15(16), 3509; https://doi.org/10.3390/electronics15163509 - 7 Aug 2026
Viewed by 107
Abstract
Acoustic side-channel attacks (ASCAs) exploit unintended sound emitted by keyboards to infer typed input, but existing methods generally assume manually labelled training data and controlled environments, limiting their applicability to realistic scenarios such as online lectures. We develop a pipeline that automatically labels [...] Read more.
Acoustic side-channel attacks (ASCAs) exploit unintended sound emitted by keyboards to infer typed input, but existing methods generally assume manually labelled training data and controlled environments, limiting their applicability to realistic scenarios such as online lectures. We develop a pipeline that automatically labels keystroke-sound samples captured from online coding tutorials: video frames are processed with optical character recognition (OCR) to extract the ground-truth character sequence, audio is segmented into clips centred on detected click events, and the two streams are aligned. A convolutional neural network (CNN) is trained on mel-spectrogram features, with transfer learning used to adapt the pretrained model to a target user with minimal samples. The classifier is trained on all 68 physical keys present in the recordings; of these, 50 produce a character or whitespace and the remaining 18 are control, navigation, and modifier keys. On a held-out test set, the CNN achieves 98.1% top-1, 99.4% top-2, and 100% top-3 accuracy. Transfer learning retains strong performance with as few as 13 samples per key. Pairing OCR-derived ground truth with acoustic CNN classification removes the labelling bottleneck that has limited previous ASCAs, and the transfer-learning stage makes the attack viable with minimal per-victim data. All code, trained models, and labelled datasets are released to support reproducible research. Full article
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Review

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17 pages, 896 KB  
Review
Proactive Defence in IoT Networks for Digital Health Systems: A Scoping Review
by Gihan Gunasekara, Patricia A. H. Williams and Ginger Mudd
Electronics 2026, 15(14), 3091; https://doi.org/10.3390/electronics15143091 - 14 Jul 2026
Viewed by 250
Abstract
The Internet of Things (IoT) plays a significant role in digital health systems, supporting applications such as implantable devices, wearables, activity trackers and ingestibles. However, the rapid expansion of IoT networks has outpaced security measures, introducing significant security challenges. This scoping review examines [...] Read more.
The Internet of Things (IoT) plays a significant role in digital health systems, supporting applications such as implantable devices, wearables, activity trackers and ingestibles. However, the rapid expansion of IoT networks has outpaced security measures, introducing significant security challenges. This scoping review examines the extent to which existing IoT security frameworks provide comprehensive coverage and identify key security elements that contribute to proactive defence in digital health environments. The specific research questions addressed are as follows: (1) Are there any IoT network security frameworks to provide comprehensive protection for IoT networks and digital health systems specifically? (2) What key security elements are currently used to develop a robust, proactive IoT security framework? Following the PRISMA-ScR guidelines, a systematic search of four databases was conducted to identify peer-reviewed studies from 2015 to 2024. This review included 255 studies, comprising 50 framework papers and 205 non-framework papers. The results reveal that some frameworks address discrete security areas but generally provide limited integrated security coverage. Key security elements identified include vulnerability assessments, threat modelling, feedback loops and the integration of artificial intelligence (AI) for proactive security measures in IoT networks. This review provides a structured synthesis, highlights research gaps and may inform the future development of more integrated and proactive security frameworks for IoT-enabled digital health systems. Full article
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