IoT Networks Security

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Cybersecurity".

Deadline for manuscript submissions: 10 September 2026 | Viewed by 2347

Editors


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Guest Editor
Electrical and Computer Engineering Department, The William States Lee College of Engineering, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
Interests: AI; wireless communications; cyber security; machine learning
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Artificial Intelligence Research Center, University of North Dakota, Grand Forks, ND 58202, USA
Interests: artificial intelligence; wireless communications & networking; autonomous cybersecurity; sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

IoT network security continues to face several research challenges due to the extensive scale, heterogeneity, and resource constraints of connected devices. Traditional security mechanisms are often too heavy for the IoT, creating a need for lightweight yet robust cryptographic protocols and scalable authentication methods. Machine learning is being widely explored for intrusion detection, but obstacles such as high false positives, poor generalization, and vulnerability to adversarial attacks remain unsolved. Ensuring privacy-preserving data sharing through federated learning, securing over-the-air updates, and developing post-quantum cryptography suitable for constrained devices are also critical areas of study. Moreover, IoT networks remain vulnerable to large-scale botnets, insecure edge/fog nodes, and interoperability issues due to the lack of standardized security frameworks. These challenges highlight the urgent need for adaptive, explainable, and resource-efficient solutions to secure the future of IoT networks.

This Special Issue will disseminate new solutions, techniques, and case studies pertaining to IoT cyber-security challenges. Its objective is to publish high‐quality articles presenting security algorithms, protocols, frameworks, and solutions for IoT networks. Relevant papers detailing unpublished, original, and state‐of‐the‐art research and not currently under review by a conference or journal will be considered.

Potential topics of interest for this Special Issue include, but are not limited to, the following:

  • Lightweight and scalable security protocols;
  • Zero-trust and adaptive security architectures;
  • Intrusion and anomaly detection through AI/ML;
  • Federated and privacy-preserving learning;
  • Secure device identity and authentication;
  • Resilient and secure firmware updates;
  • Post-quantum cryptography for the IoT;
  • IoT botnet detection and mitigation;
  • Secure edge/fog computing;
  • Blockchain and distributed ledgers for the IoT;
  • Standardization and interoperability;
  • Explainable and trustworthy IoT security.

Dr. Fatima Salahdine
Prof. Dr. Naima Kaabouch
Guest Editors

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Keywords

  • internet of things
  • cyber-security
  • artificial intelligence

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Published Papers (2 papers)

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22 pages, 1028 KB  
Article
AutoBoost-IoT: A Hybrid Model for Intrusion Detection in IoT Networks
by Mehdi Moucharraf, Mohammed Ridouani, Fatima Salahdine and Naima Kaabouch
Future Internet 2026, 18(5), 229; https://doi.org/10.3390/fi18050229 - 23 Apr 2026
Cited by 1 | Viewed by 837
Abstract
The rapid growth of IoT ecosystem has significantly increased the potential threats and attack vectors in the recent times, thereby requiring intrusion detection mechanisms that are highly accurate and scalable in nature. This paper presents a hybrid intrusion detection system that involves the [...] Read more.
The rapid growth of IoT ecosystem has significantly increased the potential threats and attack vectors in the recent times, thereby requiring intrusion detection mechanisms that are highly accurate and scalable in nature. This paper presents a hybrid intrusion detection system that involves the usage of both supervised and unsupervised machine learning methods to detect different kinds of attacks present in the IoT network. In the first step, Random Forest-based feature extraction is adopted to determine the most important features from the highly dimensional network traffic data. After this, the extracted features are compressed using the Deep AutoEncoder model into latent features that are fed into multiple classifiers to classify the traffic into various IoT attack classes and normal traffic class. Specifically, the classifiers used in the process include XGBoost, SVM, Logistic Regression, Naive Bayes and Multilayer Perceptron models. Multiple IoT benchmark datasets, such as N-BaIoT and CICIoT2023, are used to evaluate the performance of the proposed hybrid intrusion detection system. It was found that the XGBoost classifier performed better than others, obtaining an accuracy rate of 99.63% and 98.94% on the N-BaIoT and CICIoT2023 datasets, respectively. The above-discussed results show the high potential of the proposed architecture for generalization in various IoT environments. From the results, one can see that it is highly effective to integrate deep learning for extracting features from data and using boosting techniques for classification to develop an efficient IDS system. Full article
(This article belongs to the Special Issue IoT Networks Security)
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18 pages, 787 KB  
Article
Multi-Criteria Selection of Network Security Configuration Using NSGA-II
by Bagdat Yagaliyeva, Valery Lakhno, Myroslav Lakhno, Boris Gusev, Kaiyrbek Makulov and Tomiris Sundet
Future Internet 2026, 18(3), 134; https://doi.org/10.3390/fi18030134 - 5 Mar 2026
Viewed by 943
Abstract
The problem of multi-criteria selection of network security configurations (NSC) under resource constraints and the necessity to comply with information security (IS) policies is addressed in this study. A formal mathematical model of the problem has been developed, encompassing the definition of a [...] Read more.
The problem of multi-criteria selection of network security configurations (NSC) under resource constraints and the necessity to comply with information security (IS) policies is addressed in this study. A formal mathematical model of the problem has been developed, encompassing the definition of a set of possible security mechanism configurations, the formalization of objective functions reflecting security levels, throughput, and deployment costs, and the introduction of constraints on feasible solutions. The NSGA-II (Non-dominated Sorting Genetic Algorithm II) optimization algorithm is employed to generate a set of Pareto-optimal solutions, ensuring uniform coverage of compromise configurations. A software package implemented in Python 3 incorporates modules for population generation, fitness evaluation, selection, crossover, mutation operators, and result visualization. Computational experiments (CE) were conducted to validate the effectiveness of the proposed approach. The evolution dynamics of the Pareto hypervolume were analyzed, the uniformity of solution distribution in the objective space was studied, and the impact of algorithm parameters on convergence to the optimal solution was examined. The results demonstrate that the proposed methodology enables the formation of NSC sets that achieve a balanced trade-off between security, throughput, and IS system deployment costs. Full article
(This article belongs to the Special Issue IoT Networks Security)
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