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Intrusion Detection and Trust Provisioning in Edge-of-Things Environment
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Intrusion Detection and Trust Provisioning in Edge-of-Things Environment

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "ICT Infrastructures for Cybersecurity".

Deadline for manuscript submissions: 20 May 2026 | Viewed by 22896

Special Issue Editors

Dr. Hooman Alavizadeh

E-Mail Website
Guest Editor
Computer Science and Information Technology, La Trobe University, Melbourne, VIC 3086, Australia
Interests: cybersecurity; cloud security; security modelling and analysis; AI in cyber defence
Dr. Ahmad Salehi Shahraki

E-Mail Website
Guest Editor
Department of Computer Science and Information Technology, La Trobe University, Bundoora, VIC 3086, Australia
Interests: different aspects of cybersecurity and blockchain: access control; applied cryptography; blockchain; distributed systems; edge and cloud computing; Internet of Things; digital health
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Edge of Things (EoT) is a new computing paradigm adopted for Internet-of-Things (IoT) applications to improve responsiveness and conserve communication resources. With the growing number of IoT applications leveraging edge computing, there is a rising demand to shift more computations to edge servers. However, this shift may introduce a significant range of security and privacy challenges. As the EoT system brings services typically provided by cloud computing and IoT closer to the end user, many of its security and privacy issues are inherited directly from cloud and IoT environments. These concerns are now distributed across the various layers of the edge architecture. However, ensuring robust security at different layers of an EoT environment such as infrastructure layers is crucial. Intrusion detection techniques have demonstrated their effectiveness in analysing and capturing cyber threats across different contexts. In the context of EoT computing, more robust and resilience intrusion detection should be designed to effectively evaluate and capture cyber threats across various layers of the edge architecture. This Special Issue focuses on the development of intrusion detection systems tailored for EoT environments. Given the limitations of data availability and the dynamic nature of edge networks, distributed IDS models with real-time data collection, refinement and evaluation are crucial. This Special Issue invites papers covering security, privacy and trust challenges in EoT, offering novel strategies to improve the detection of intrusions and provisioning of trust in this cutting-edge paradigm.

Dr. Hooman Alavizadeh
Dr. Ahmad Salehi Shahraki
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Computers is an international peer-reviewed open access monthly 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 1800 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

  • AI-based intrusion detection
  • Edge-of-Things computing
  • Internet of Things (IoT)
  • cloud computing
  • real-time monitoring
  • security modelling and analysis
  • trust provisioning
  • threat modelling and situation awareness
  • privacy-preserving techniques
  • deep learning-based IDS for EoT

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Published Papers (4 papers)

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Research

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37 pages, 2286 KB  
Article
Parameterised Quantum SVM with Data-Driven Entanglement for Zero-Day Exploit Detection
by Steven Jabulani Nhlapo, Elodie Ngoie Mutombo and Mike Nkongolo Wa Nkongolo
Computers 2025, 14(8), 331; https://doi.org/10.3390/computers14080331 - 15 Aug 2025
Viewed by 1232
Abstract
Zero-day attacks pose a persistent threat to computing infrastructure by exploiting previously unknown software vulnerabilities that evade traditional signature-based network intrusion detection systems (NIDSs). To address this limitation, machine learning (ML) techniques offer a promising approach for enhancing anomaly detection in network traffic. [...] Read more.
Zero-day attacks pose a persistent threat to computing infrastructure by exploiting previously unknown software vulnerabilities that evade traditional signature-based network intrusion detection systems (NIDSs). To address this limitation, machine learning (ML) techniques offer a promising approach for enhancing anomaly detection in network traffic. This study evaluates several ML models on a labeled network traffic dataset, with a focus on zero-day attack detection. Ensemble learning methods, particularly eXtreme gradient boosting (XGBoost), achieved perfect classification, identifying all 6231 zero-day instances without false positives and maintaining efficient training and prediction times. While classical support vector machines (SVMs) performed modestly at 64% accuracy, their performance improved to 98% with the use of the borderline synthetic minority oversampling technique (SMOTE) and SMOTE + edited nearest neighbours (SMOTEENN). To explore quantum-enhanced alternatives, a quantum SVM (QSVM) is implemented using three-qubit and four-qubit quantum circuits simulated on the aer_simulator_statevector. The QSVM achieved high accuracy (99.89%) and strong F1-scores (98.95%), indicating that nonlinear quantum feature maps (QFMs) can increase sensitivity to zero-day exploit patterns. Unlike prior work that applies standard quantum kernels, this study introduces a parameterised quantum feature encoding scheme, where each classical feature is mapped using a nonlinear function tuned by a set of learnable parameters. Additionally, a sparse entanglement topology is derived from mutual information between features, ensuring a compact and data-adaptive quantum circuit that aligns with the resource constraints of noisy intermediate-scale quantum (NISQ) devices. Our contribution lies in formalising a quantum circuit design that enables scalable, expressive, and generalisable quantum architectures tailored for zero-day attack detection. This extends beyond conventional usage of QSVMs by offering a principled approach to quantum circuit construction for cybersecurity. While these findings are obtained via noiseless simulation, they provide a theoretical proof of concept for the viability of quantum ML (QML) in network security. Future work should target real quantum hardware execution and adaptive sampling techniques to assess robustness under decoherence, gate errors, and dynamic threat environments. Full article
(This article belongs to the Special Issue Intrusion Detection and Trust Provisioning in Edge-of-Things Environment)
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46 pages, 8887 KB  
Article
One-Class Anomaly Detection for Industrial Applications: A Comparative Survey and Experimental Study
by Davide Paolini, Pierpaolo Dini, Ettore Soldaini and Sergio Saponara
Computers 2025, 14(7), 281; https://doi.org/10.3390/computers14070281 - 16 Jul 2025
Viewed by 2165
Abstract
This article aims to evaluate the runtime effectiveness of various one-class classification (OCC) techniques for anomaly detection in an industrial scenario reproduced in a laboratory setting. To address the limitations posed by restricted access to proprietary data, the study explores OCC methods that [...] Read more.
This article aims to evaluate the runtime effectiveness of various one-class classification (OCC) techniques for anomaly detection in an industrial scenario reproduced in a laboratory setting. To address the limitations posed by restricted access to proprietary data, the study explores OCC methods that learn solely from legitimate network traffic, without requiring labeled malicious samples. After analyzing major publicly available datasets, such as KDD Cup 1999 and TON-IoT, as well as the most widely used OCC techniques, a lightweight and modular intrusion detection system (IDS) was developed in Python. The system was tested in real time on an experimental platform based on Raspberry Pi, within a simulated client–server environment using the NFSv4 protocol over TCP/UDP. Several OCC models were compared, including One-Class SVM, Autoencoder, VAE, and Isolation Forest. The results showed strong performance in terms of detection accuracy and low latency, with the best outcomes achieved using the UNSW-NB15 dataset. The article concludes with a discussion of additional strategies to enhance the runtime analysis of these algorithms, offering insights into potential future applications and improvement directions. Full article
(This article belongs to the Special Issue Intrusion Detection and Trust Provisioning in Edge-of-Things Environment)
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21 pages, 542 KB  
Article
WGAN-DL-IDS: An Efficient Framework for Intrusion Detection System Using WGAN, Random Forest, and Deep Learning Approaches
by Shehla Gul, Sobia Arshad, Sanay Muhammad Umar Saeed, Adeel Akram and Muhammad Awais Azam
Computers 2025, 14(1), 4; https://doi.org/10.3390/computers14010004 - 27 Dec 2024
Cited by 2 | Viewed by 2237
Abstract
The rise in cyber security issues has caused significant harm to tech world and thus society in recent years. Intrusion detection systems (IDSs) are crucial for the detection and the mitigation of the increasing risk of cyber attacks. False and disregarded alarms are [...] Read more.
The rise in cyber security issues has caused significant harm to tech world and thus society in recent years. Intrusion detection systems (IDSs) are crucial for the detection and the mitigation of the increasing risk of cyber attacks. False and disregarded alarms are a common problem for traditional IDSs in high-bandwidth and large-scale network systems. While applying learning techniques to intrusion detection, researchers are facing challenges mainly due to the imbalanced training sets and the high dimensionality of datasets, resulting from the scarcity of attack data and longer training periods, respectively. Thus, this leads to reduced efficiency. In this research study, we propose a strategy for dealing with the problems of imbalanced datasets and high dimensionality in IDSs. In our efficient and novel framework, we integrate an oversampling strategy that uses Generative Adversarial Networks (GANs) to overcome the difficulties introduced by imbalanced datasets, and we use the Random Forest (RF) importance algorithm to select a subset of features that best represent the dataset to reduce the dimensionality of a training dataset. Then, we use three deep learning techniques, Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), to classify the attacks. We implement and evaluate this proposed framework on the CICIDS2017 dataset. Experimental results show that our proposed framework outperforms state-of-the-art approaches, vastly improving DL model detection accuracy by 98% using CNN. Full article
(This article belongs to the Special Issue Intrusion Detection and Trust Provisioning in Edge-of-Things Environment)
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Review

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44 pages, 642 KB  
Review
Overview on Intrusion Detection Systems for Computers Networking Security
by Lorenzo Diana, Pierpaolo Dini and Davide Paolini
Computers 2025, 14(3), 87; https://doi.org/10.3390/computers14030087 - 3 Mar 2025
Cited by 20 | Viewed by 16640
Abstract
The rapid growth of digital communications and extensive data exchange have made computer networks integral to organizational operations. However, this increased connectivity has also expanded the attack surface, introducing significant security risks. This paper provides a comprehensive review of Intrusion Detection System (IDS) [...] Read more.
The rapid growth of digital communications and extensive data exchange have made computer networks integral to organizational operations. However, this increased connectivity has also expanded the attack surface, introducing significant security risks. This paper provides a comprehensive review of Intrusion Detection System (IDS) technologies for network security, examining both traditional methods and recent advancements. The review covers IDS architectures and types, key detection techniques, datasets and test environments, and implementations in modern network environments such as cloud computing, virtualized networks, Internet of Things (IoT), and industrial control systems. It also addresses current challenges, including scalability, performance, and the reduction of false positives and negatives. Special attention is given to the integration of advanced technologies like Artificial Intelligence (AI) and Machine Learning (ML), and the potential of distributed technologies such as blockchain. By maintaining a broad-spectrum analysis, this review aims to offer a holistic view of the state-of-the-art in IDSs, support a diverse audience, and identify future research and development directions in this critical area of cybersecurity. Full article
(This article belongs to the Special Issue Intrusion Detection and Trust Provisioning in Edge-of-Things Environment)
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