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Innovative Sensors and Processes for Intrusion and Anomaly Detection in IoT

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 1174

Special Issue Editors

Prof. Dr. Vasos Vassiliou

E-Mail Website
Guest Editor
Department of Computer Science, University of Cyprus, Nicosia, Cyprus
Interests: networks; IOT
Special Issues, Collections and Topics in MDPI journals
Dr. Iacovos I. Ioannou

E-Mail Website
Guest Editor
Department of Computer Science, Philips University, 2001 Nicosia, Cyprus
Interests: intrusion detection system; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The proliferation of IoT devices and the increasing integration of cyber–physical systems (CPS) across diverse domains—from smart homes and industrial automation to healthcare and critical infrastructure—have created unprecedented vulnerabilities. Traditional security mechanisms often fall short in detecting subtle anomalies or sophisticated intrusions in these dynamic environments. This Special Issue aims to showcase cutting-edge research on sensors, methodologies, and processes that enhance intrusion and anomaly detection within IoT and CPS ecosystems.

We welcome original contributions focusing on sensor design, data fusion, edge AI for threat detection, anomaly detection using machine learning, privacy-preserving sensing systems, behavior-based anomaly detection, hybrid threat detection, context-aware sensing, secure sensor fusion, and data-driven predictive models for real-time protection. Contributions may include both theoretical developments and real-world applications. This Special Issue also encourages interdisciplinary work intersecting networking, cybersecurity, and embedded sensing.

This topic is highly aligned with the scope of Sensors, focusing on the role of innovative sensing technologies and methodologies in enhancing system security.

Prof. Dr. Vasos Vassiliou
Dr. Iacovos I. Ioannou
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

  • IoT security
  • intrusion detection
  • anomaly detection
  • edge computing
  • cyber–physical systems
  • machine learning
  • sensor networks
  • privacy
  • network monitoring
  • embedded security
  • smart environments
  • AI for security
  • resilient sensing
  • hybrid threat detection

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Published Papers (1 paper)

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Research

51 pages, 12791 KB  
Article
Generative Adversarial Networks for Energy-Aware IoT Intrusion Detection: Comprehensive Benchmark Analysis of GAN Architectures with Accuracy-per-Joule Evaluation
by Iacovos Ioannou and Vasos Vassiliou
Sensors 2026, 26(3), 757; https://doi.org/10.3390/s26030757 - 23 Jan 2026
Cited by 1 | Viewed by 558
Abstract
The proliferation of Internet of Things (IoT) devices has created unprecedented security challenges characterized by resource constraints, heterogeneous network architectures, and severe class imbalance in attack detection datasets. This paper presents a comprehensive benchmark evaluation of five Generative Adversarial Network (GAN) architectures for [...] Read more.
The proliferation of Internet of Things (IoT) devices has created unprecedented security challenges characterized by resource constraints, heterogeneous network architectures, and severe class imbalance in attack detection datasets. This paper presents a comprehensive benchmark evaluation of five Generative Adversarial Network (GAN) architectures for energy-aware intrusion detection: Standard GAN, Progressive GAN (PGAN), Conditional GAN (cGAN), Graph-based GAN (GraphGAN), and Wasserstein GAN with Gradient Penalty (WGAN-GP). Our evaluation framework introduces novel energy-normalized performance metrics, including Accuracy-per-Joule (APJ) and F1-per-Joule (F1PJ), that enable principled architecture selection for energy-constrained deployments. We propose an optimized WGAN-GP architecture incorporating diversity loss, feature matching, and noise injection mechanisms specifically designed for classification-oriented data augmentation. Experimental results on a stratified subset of the BoT-IoT dataset (approximately 1.83 million records) demonstrate that our optimized WGAN-GP achieves state-of-the-art performance, with 99.99% classification accuracy, a 0.99 macro-F1 score, and superior generation quality (MSE 0.01). While traditional classifiers augmented with SMOTE (i.e., Logistic Regression and CNN1D-TCN) also achieve 99.99% accuracy, they suffer from poor minority class detection (77.78–80.00%); our WGAN-GP improves minority class detection to 100.00% on the reported test split (45 of 45 attack instances correctly identified). Furthermore, WGAN-GP provides substantial efficiency advantages under our energy-normalized metrics, achieving superior accuracy-per-joule performance compared to Standard GAN. Also, a cross-dataset validation across five benchmarks (BoT-IoT, CICIoT2023, ToN-IoT, UNSW-NB15, CIC-IDS2017) was implemented using 250 pooled test attacks to confirm generalizability, with WGAN-GP achieving 98.40% minority class accuracy (246/250 attacks detected) compared to 76.80% for Classical + SMOTE methods, a statistically significant 21.60 percentage point improvement (p<0.0001). Finally, our analysis reveals that incorporating diversity-promoting mechanisms in GAN training simultaneously achieves best generation quality AND best classification performance, demonstrating that these objectives are complementary rather than competing. Full article
(This article belongs to the Special Issue Innovative Sensors and Processes for Intrusion and Anomaly Detection in IoT)
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