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Future Internet
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IoT Networks Security
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IoT Networks Security

A Special Issue of Future Internet (ISSN 1999-5903) belonging to the section "Cybersecurity".

Deadline for manuscript submissions: closed (10 September 2026) | Viewed by 3956

Editors

Dr. Fatima Salahdine

E-Mail Website
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
Prof. Dr. Naima Kaabouch

E-Mail Website
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 Relevant papers detailing unpublished, original, and state‐of‐the‐art research and not currently under review by a conference or journal will be considered.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

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet 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

  • internet of things
  • cyber-security
  • artificial intelligence

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Published Papers (4 papers)

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29 pages, 3927 KB  
Article
PUG: A PUF-Anchored Unified Group Key Management Protocol for Internet of Drones
by Tajdid Ul Alam and Mohamed Younis
Future Internet 2026, 18(10), 529; https://doi.org/10.3390/fi18100529 - 1 Oct 2026
Viewed by 181
Abstract
Drone swarms operate collaboratively and serve critical missions, which require a common session encryption key to secure exchanged data. This key must be updated when new drones join—or existing ones depart—the swarm. Classical key management protocols force a trade-off between multi-message re-key chains [...] Read more.
Drone swarms operate collaboratively and serve critical missions, which require a common session encryption key to secure exchanged data. This key must be updated when new drones join—or existing ones depart—the swarm. Classical key management protocols force a trade-off between multi-message re-key chains and high broadcast cost, and they often degrade under packet loss and jamming. This paper presents PUG, a physically unclonable function (PUF)-anchored unified group key management protocol for the Internet of Drones that completes every membership event—join, leave, eviction, or re-key—with a single self-contained authenticated broadcast. The group controller encrypts the new session key separately for each drone under secrets derived from their PUFs. The controller then treats each node-specific ciphertext as a Chinese Remainder Theorem residue under a prime modulus that is node- and time-specific and derived from the PUF response. Solving this system of congruences yields a group lock, which is broadcast to all members in one message. Long-term private key material is never written to the drone’s nonvolatile memory, and a captured node is evicted from the group to keep future communication secured within the group and to prevent durable secrets that could be used to recover past group traffic. Membership update ensures forward and backward secrecy at epoch granularity. The simulation results show that our protocol is effective across various loss rates, maintains the largest share of the swarm keyed under sustained jamming, and imposes limited control traffic while being efficient in terms of communication and computation overheads. Full article
(This article belongs to the Special Issue IoT Networks Security)
24 pages, 10483 KB  
Article
Lightweight Multi-Attribute Physical Layer Authentication in LoRaWAN: A Comparative Study of Knowledge Distillation and Direct Training
by Azita Pourghasem, Raimund Kirner, Athanasios Tsokanos, Iosif Mporas and Alexios Mylonas
Future Internet 2026, 18(9), 462; https://doi.org/10.3390/fi18090462 - 28 Aug 2026
Viewed by 280
Abstract
LoRaWAN has become a widely adopted communication technology for Internet of Things applications because of its long-range communication capability and low power consumption. However, resource-constrained LoRaWAN networks remain vulnerable to physical-layer attacks such as jamming and battery-depletion attacks. This paper investigates architectural compression [...] Read more.
LoRaWAN has become a widely adopted communication technology for Internet of Things applications because of its long-range communication capability and low power consumption. However, resource-constrained LoRaWAN networks remain vulnerable to physical-layer attacks such as jamming and battery-depletion attacks. This paper investigates architectural compression and Knowledge Distillation (KD) for lightweight multi-attribute Physical Layer Authentication (PLA) in LoRaWAN networks. A higher-capacity Teacher model is used to train progressively smaller Student models, and three Student-training strategies—Direct training, Hard-KD, and Soft-KD—are systematically compared to determine whether Teacher supervision provides an advantage beyond architectural compression alone. The framework uses the Received Signal Strength Indicator (RSSI), altitude, and the battery level as input attributes and is evaluated offline using a device-aware semi-synthetic dataset derived from the publicly available Brno LoRaWAN dataset. The resulting dataset contains Normal Communication, Jamming Attack, and Battery Depletion Attack traffic. The experimental results show that Student (8) preserves a classification performance close to that of the Teacher across all three training strategies, achieving Macro F1-scores of 90.619 ± 0.056% for Direct training, 90.591 ± 0.069% for Hard-KD, and 90.626 ± 0.083% for Soft-KD, compared with 91.007% for the Teacher. Student (8) reduces the number of trainable parameters from 2435 to 59 (97.6%) and the serialised model size from 46.65 KB to 2.80 KB (94.0%), while exhibiting a substantially higher measured batch-processing throughput under the evaluated CPU-based implementation. A feature-ablation analysis further indicates that the RSSI and battery level provide the dominant discriminatory information, while the altitude provides a smaller but measurable contextual contribution. The comparable performance of Direct, Hard-KD, and Soft-KD demonstrates that KD does not consistently outperform direct training for the evaluated Student architectures; rather, architectural compression itself accounts for much of the retained performance. These findings demonstrate that compact Student architectures can substantially reduce the model complexity while preserving a strong classification performance within the evaluated multi-attribute PLA framework, supporting the further investigation of lightweight network-side security mechanisms for LoRaWAN deployments. Full article
(This article belongs to the Special Issue IoT Networks Security)
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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 4 | Viewed by 1252
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 1451
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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