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Internet of Things Cybersecurity
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Internet of Things Cybersecurity

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 20 February 2026 | Viewed by 5774

Special Issue Editors

Dr. Vasileios Memos

E-Mail Website
Guest Editor
Department of Applied Informatics, University of Macedonia, 54636 Thessaloniki, Greece
Interests: computer networks; wireless and mobile communications; multimedia transmission over 5G networks; cloud computing (CC); big data analytics (BDA); wireless sensor network (WSN); Internet of Things (IoT); artificial intelligence (AI); machine learning (ML); cyber security; cryptography; privacy and security software testing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Konstantinos E. Psannis

E-Mail Website
Guest Editor
Department of Applied Informatics, University of Macedonia, 54636 Thessaloniki, Greece
Interests: digital signals and systems; 6G-enabled-ubiquitous big data; AI-IoT; clouds and communications; digital media communications; media coding; media synchronization; transport over a variety of networks
Special Issues, Collections and Topics in MDPI journals
Dr. Christos L. Stergiou

E-Mail Website
Guest Editor
Department of Applied Informatics, University of Macedonia, 54636 Thessaloniki, Greece
Interests: algorithms for cloud computing; big data; wireless communication
Special Issues, Collections and Topics in MDPI journals
Dr. Andreas P. Plageras

E-Mail Website
Guest Editor
Department of Applied Informatics, University of Macedonia, 54636 Thessaloniki, Greece
Interests: web design and development; application development; networks and security; systems programming; cloud and edge computing; big data technologies; big data analytics; artificial intelligence; automation; robotics; 3D graphics design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Every day, numerous Internet of Things (IoT) devices, such as computers, mobile phones, smartwatches, webcams, and various other smart devices, are hijacked and integrated into massive malicious networks, known as botnets. IoT botnets are considered one of the main types of network attacks, posing significant risks to user privacy, device security, and wider network infrastructure like industrial networks. Malicious users are increasingly leveraging Artificial Intelligence (AI) to develop advanced techniques and methods to exploit vulnerabilities in IoT networks, aiming to transform them into a Botnet of Things (BoT) network (also referred to as an IoT botnet). 

This Special Issue aims to explore novel trends, techniques, and methodologies that enhance the security and resilience of IoT devices and networks. We welcome high-quality original research, technical papers, and review articles that address the challenges of real-time protection against botnets and other types of attacks that target IoT environments, such as Distributed Denial of Service (DDoS), Man in the Middle (MitM), phishing, advanced persistent threats (APTs), ransomware, large-scale malware infections, and zero-day exploits. 

Contributions that bridge the gap between IoT and BoT, such as intelligent and scalable security frameworks, lightweight detection mechanisms, and privacy-preserving protocols, are particularly encouraged. 

Dr. Vasileios Memos
Dr. Konstantinos E. Psannis
Dr. Christos L. Stergiou
Dr. Andreas P. Plageras
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. Sensors 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 2600 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
  • Botnet of Things (BoT) network
  • network attacks

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Published Papers (4 papers)

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Research

25 pages, 1862 KB  
Article
A Novel Architecture for Mitigating Botnet Threats in AI-Powered IoT Environments
by Vasileios A. Memos, Christos L. Stergiou, Alexandros I. Bermperis, Andreas P. Plageras and Konstantinos E. Psannis
Sensors 2026, 26(2), 572; https://doi.org/10.3390/s26020572 - 14 Jan 2026
Viewed by 350
Abstract
The rapid growth of Artificial Intelligence of Things (AIoT) environments in various sectors has introduced major security challenges, as these smart devices can be exploited by malicious users to form Botnets of Things (BoT). Limited computational resources and weak encryption mechanisms in such [...] Read more.
The rapid growth of Artificial Intelligence of Things (AIoT) environments in various sectors has introduced major security challenges, as these smart devices can be exploited by malicious users to form Botnets of Things (BoT). Limited computational resources and weak encryption mechanisms in such devices make them attractive targets for attacks like Distributed Denial of Service (DDoS), Man-in-the-Middle (MitM), and malware distribution. In this paper, we propose a novel multi-layered architecture to mitigate BoT threats in AIoT environments. The system leverages edge traffic inspection, sandboxing, and machine learning techniques to analyze, detect, and prevent suspicious behavior, while uses centralized monitoring and response automation to ensure rapid mitigation. Experimental results demonstrate the necessity and superiority over or parallel to existing models, providing an early detection of botnet activity, reduced false positives, improved forensic capabilities, and scalable protection for large-scale AIoT areas. Overall, this solution delivers a comprehensive, resilient, and proactive framework to protect AIoT assets from evolving cyber threats. Full article
(This article belongs to the Special Issue Internet of Things Cybersecurity)
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39 pages, 94444 KB  
Article
From Capture–Recapture to No Recapture: Efficient SCAD Even After Software Updates
by Kurt A. Vedros, Aleksandar Vakanski, Domenic J. Forte and Constantinos Kolias
Sensors 2026, 26(1), 118; https://doi.org/10.3390/s26010118 - 24 Dec 2025
Viewed by 403
Abstract
Side-Channel-based Anomaly Detection (SCAD) offers a powerful and non-intrusive means of detecting unauthorized behavior in IoT and cyber–physical systems. It leverages signals that emerge from physical activity—such as electromagnetic (EM) emissions or power consumption traces—as passive indicators of software execution integrity. This capability [...] Read more.
Side-Channel-based Anomaly Detection (SCAD) offers a powerful and non-intrusive means of detecting unauthorized behavior in IoT and cyber–physical systems. It leverages signals that emerge from physical activity—such as electromagnetic (EM) emissions or power consumption traces—as passive indicators of software execution integrity. This capability is particularly critical in IoT/IIoT environments, where large fleets of deployed devices are at heightened risk of firmware tampering, malicious code injection, and stealthy post-deployment compromise. However, its deployment remains constrained by the costly and time-consuming need to re-fingerprint whenever a program is updated or modified, as fingerprinting involves a precision-intensive manual capturing process for each execution path. To address this challenge, we propose a generative modeling framework that synthesizes realistic EM signals for newly introduced or updated execution paths. Our approach utilizes a Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (CWGAN-GP) framework trained on real EM traces that are conditioned on Execution State Descriptors (ESDs) that encode instruction sequences, operands, and register values. Comprehensive evaluations at instruction-level granularity demonstrate that our approach generates synthetic signals that faithfully reproduce the distinctive features of real EM emissions—achieving 85–92% similarity to real emanations. The inclusion of ESD conditioning further improves fidelity, reducing the similarity distance by ∼13%. To gauge SCAD utility, we train a basic semi-supervised detector on the synthetic signals and find ROC-AUC results within ±1% of detectors trained on real EM data across varying noise conditions. Furthermore, the proposed 1DCNNGAN model (a CWGAN-GP variant) achieves faster training and reduced memory requirements compared with the previously leading ResGAN. Full article
(This article belongs to the Special Issue Internet of Things Cybersecurity)
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22 pages, 2549 KB  
Article
Lightweight Signal Processing and Edge AI for Real-Time Anomaly Detection in IoT Sensor Networks
by Manuel J. C. S. Reis
Sensors 2025, 25(21), 6629; https://doi.org/10.3390/s25216629 - 28 Oct 2025
Cited by 1 | Viewed by 3238
Abstract
The proliferation of IoT devices has created vast sensor networks that generate continuous time-series data. Efficient and real-time processing of these signals is crucial for applications such as predictive maintenance, healthcare monitoring, and environmental sensing. This paper proposes a lightweight framework that combines [...] Read more.
The proliferation of IoT devices has created vast sensor networks that generate continuous time-series data. Efficient and real-time processing of these signals is crucial for applications such as predictive maintenance, healthcare monitoring, and environmental sensing. This paper proposes a lightweight framework that combines classical signal processing techniques (Fourier and Wavelet-based feature extraction) with edge-deployed machine learning models for anomaly detection. By performing feature extraction and classification locally, the approach reduces communication overhead, minimizes latency, and improves energy efficiency in IoT nodes. Experiments with synthetic vibration, acoustic, and environmental datasets showed that the proposed Shallow Neural Network achieved the highest detection performance (F1-score ≈ 0.94), while a Quantized TinyML model offered a favorable trade-off (F1-score ≈ 0.92) with a 3× reduction in memory footprint and 60% lower energy consumption. Decision Trees remained competitive for ultra-constrained devices, providing sub-millisecond latency with limited recall. Additional analyses confirmed robustness against noise, missing data, and variations in anomaly characteristics, while ablation studies highlighted the contributions of each pipeline component. These results demonstrate the feasibility of accurate, resource-efficient anomaly detection at the edge, paving the way for practical deployment in large-scale IoT sensor networks. Full article
(This article belongs to the Special Issue Internet of Things Cybersecurity)
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23 pages, 360 KB  
Article
In-Memory Shellcode Runner Detection in Internet of Things (IoT) Networks: A Lightweight Behavioral and Semantic Analysis Framework
by Jean Rosemond Dora, Ladislav Hluchý and Michal Staňo
Sensors 2025, 25(17), 5425; https://doi.org/10.3390/s25175425 - 2 Sep 2025
Cited by 1 | Viewed by 1296
Abstract
The widespread expansion of Internet of Things devices has ushered in an era of unprecedented connectivity. However, it has simultaneously exposed these resource-constrained systems to novel and advanced cyber threats. Among the most impressive and complex attacks are those leveraging in-memory shellcode runners [...] Read more.
The widespread expansion of Internet of Things devices has ushered in an era of unprecedented connectivity. However, it has simultaneously exposed these resource-constrained systems to novel and advanced cyber threats. Among the most impressive and complex attacks are those leveraging in-memory shellcode runners (malware), which perform malicious payloads directly in memory, circumventing conventional disk-based detection security mechanisms. This paper presents a comprehensive framework, both academic and technical, for detecting in-memory shellcode runners, particularly tailored to the unique characteristics of these networks. We analyze and review the limitations of existing security parameters in this area, highlight the different challenges posed by those constraints, and propose a multi-layered approach that combines entropy-based anomaly scoring, lightweight behavioral monitoring, and novel Graph Neural Network methods for System Call Semantic Graph Analysis. Our proposal focuses on runtime analysis of process memory, system call patterns (e.g., Syscall ID, Process ID, Hooking, Win32 application programming interface), and network behavior to identify the subtle indicators of compromise that portray in-memory attacks, even in the absence of conventional file-system artifacts. Through meticulous empirical evaluation against simulated and real-world Internet of Things attacks (red team engagements, penetration testing), we demonstrate the efficiency and a few challenges of our approach, providing a crucial step towards enhancing the security posture of these critical environments. Full article
(This article belongs to the Special Issue Internet of Things Cybersecurity)
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