Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.2
3.5
Journals
Sensors
Special Issues
Advanced Sensing Technologies for Detecting Cybersecurity Attacks in Internet of...
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advanced Sensing Technologies for Detecting Cybersecurity Attacks in Internet of Things Systems

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 7302

Special Issue Editors

Dr. Kamran Siddique

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, University of Alaska Anchorage, Anchorage, AK 99508, USA
Interests: big data processing; cyber security; machine learning; cloud computing; system architectures
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ka Lok Man

E-Mail Website
Guest Editor
School of Advanced Technology, Xi’an Jiaotong Liverpool University, Suzhou 215123, China
Interests: AI; IoT; big data analytics; IC designs; WSN
Special Issues, Collections and Topics in MDPI journals
Dr. Zahid Akhtar

E-Mail Website
Guest Editor
Department of Network and Computer Security, State University of New York Polytechnic Institute, C135, Kunsela Hall, Utica, NY 13502, USA
Interests: machine learning and computer vision with applications to cybersecurity; biometrics; deepfakes; affect recognition; image and video processing; perceptual-based audiovisual multimedia quality assessmentsing; perceptual-based audiovisual multimedia quality assessment; cybersecurity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid proliferation of the Internet of Things (IoT) has revolutionized various domains, including healthcare, smart cities, industrial automation, and critical infrastructure. However, the exponential growth of interconnected devices has also significantly increased the attack surface, making IoT systems prime targets for cyber threats. Traditional security mechanisms often fall short in addressing such evolving landscape of cyber threats due to the heterogeneity, resource constraints, and distributed architecture of IoT ecosystems.

This Special Issue aims to explore innovative sensing methodologies, machine learning techniques, and advanced security frameworks designed to enhance IoT security. We invite high-quality contributions that focus on the design, development, and deployment of advanced sensing technologies for real-time detection, analysis, and mitigation of cybersecurity threats.

Topics of interest include, but are not limited to:

  • Intelligent sensing systems for intrusion detection in IoT networks
  • Machine learning and AI-driven security analytics for IoT
  • Anomaly detection using advanced sensing techniques
  • Lightweight security frameworks for IoT devices
  • Blockchain-based security mechanisms for IoT threat detection
  • Edge and fog computing approaches for real-time threat monitoring
  • Privacy-preserving sensing techniques for IoT environments
  • Secure data fusion and sensor networks for cybersecurity
  • Case studies and real-world implementations of IoT threat detection systems

By bringing together cutting-edge research, this Special Issue aims to stimulate the development of robust security solutions that ensure the resilience and trustworthiness of IoT ecosystems.

Dr. Kamran Siddique
Prof. Dr. Ka Lok Man
Dr. Zahid Akhtar
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
  • cybersecurity attacks
  • anomaly detection
  • intrusion detection
  • machine learning
  • AI-driven security
  • blockchain
  • edge computing
  • threat monitoring
  • sensor networks
  • data fusion
  • privacy-preserving techniques

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Other

38 pages, 6205 KB  
Article
An Adaptive Framework for Intrusion Detection in IoT Security Using MAML (Model-Agnostic Meta-Learning)
by Fatma S. Alrayes, Syed Umar Amin and Nada Hakami
Sensors 2025, 25(8), 2487; https://doi.org/10.3390/s25082487 - 15 Apr 2025
Cited by 13 | Viewed by 4890
Abstract
With the rapid emergence of the Internet of Things (IoT) devices, there were new vectors for attacking cyber, so there was a need for approachable intrusion detection systems (IDSs) with more innovative custom tactics. The traditional IDS models tend to find difficulties in [...] Read more.
With the rapid emergence of the Internet of Things (IoT) devices, there were new vectors for attacking cyber, so there was a need for approachable intrusion detection systems (IDSs) with more innovative custom tactics. The traditional IDS models tend to find difficulties in generalization in the continuously changing and heterogeneous IoT environments. This paper contributes to an adaptive intrusion detection framework using Model-Agnostic Meta-Learning (MAML) and few-shot learning paradigms to quickly adapt to new tasks with little data. The goal of this research is to improve the security of IoT by developing a strong IDS that will perform well across assorted datasets and attack environments. Finally, we apply our proposed framework to two benchmark datasets, UNSW-NB15 and NSL-KDD99, which provide different attack scenarios and network behaviors. The methodology trains a base model with MAML to allow fast adaptation on specific tasks during fine-tuning. Our approach leads to experimental results with 99.98% accuracy, 99.5% precision, 99.0% recall, and 99.4% F1 score on the UNSW-NB15 dataset. The model achieved 99.1% accuracy, 97.3% precision, 98.2% recall, and 98.5% F1 score on the NSL-KDD99 dataset. That shows that MAML can detect many cyber threats in IoT environments. Based on this study, it is concluded that meta-learning-based intrusion detection could help build resilient IoT systems. Future works will move educated meta-learning to a federated setting and deploy it in real time in response to changing threats. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies for Detecting Cybersecurity Attacks in Internet of Things Systems)
►▼ Show Figures

Figure 1

Other

Jump to: Research

46 pages, 2455 KB  
Systematic Review
Performance Analysis of Explainable Deep Learning-Based Intrusion Detection Systems for IoT Networks: A Systematic Review
by Taiwo Blessing Ogunseyi, Gogulakrishan Thiyagarajan, Honggang He, Vinay Bist and Zhengcong Du
Sensors 2026, 26(2), 363; https://doi.org/10.3390/s26020363 - 6 Jan 2026
Cited by 2 | Viewed by 1659
Abstract
The opaque nature of black-box deep learning (DL) models poses significant challenges for intrusion detection systems (IDSs) in Internet of Things (IoT) networks, where transparency, trust, and operational reliability are critical. Although explainable artificial intelligence (XAI) has been increasingly adopted to enhance interpretability, [...] Read more.
The opaque nature of black-box deep learning (DL) models poses significant challenges for intrusion detection systems (IDSs) in Internet of Things (IoT) networks, where transparency, trust, and operational reliability are critical. Although explainable artificial intelligence (XAI) has been increasingly adopted to enhance interpretability, its impact on detection performance and computational efficiency in resource-constrained IoT environments remains insufficiently understood. This systematic review investigates the performance of an explainable deep learning-based IDS for IoT networks by analyzing trade-offs among detection accuracy, computational overhead, and explanation quality. Following the PRISMA methodology, 129 peer-reviewed studies published between 2018 and 2025 are systematically analyzed to address key research questions related to XAI technique trade-offs, deep learning architecture performance, post-deployment XAI evaluation practices, and deployment bottlenecks. The findings reveal a pronounced imbalance in existing approaches, where high detection accuracy is often achieved at the expense of computational efficiency and rigorous explainability evaluation, limiting practical deployment on IoT edge devices. To address these gaps, this review proposes two conceptual contributions: (i) an XAI evaluation framework that standardizes post-deployment evaluation categories for explainability, and (ii) the Unified Explainable IDS Evaluation Framework (UXIEF), which models the fundamental trilemma between detection performance, resource efficiency, and explanation quality in IoT IDSs. By systematically highlighting performance–efficiency gaps, methodological shortcomings, and practical deployment challenges, this review provides a structured foundation and actionable insights for the development of trustworthy, efficient, and deployable explainable IDS solutions in IoT ecosystems. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies for Detecting Cybersecurity Attacks in Internet of Things Systems)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop