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IoT Network Security (Second Edition)
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IoT Network Security (Second Edition)

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

Deadline for manuscript submissions: 1 September 2025 | Viewed by 3383

Special Issue Editor

Prof. Dr. Jian Li

E-Mail Website
Guest Editor
School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, China
Interests: information security; quantum cryptography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The fast development of the Internet of Things (IoT) has involved enormous evolutions of IoT-empowered smart systems and applications via diverse networks, remote sensors, and endpoint appliances. IoT network security is critical, largely because of the expanded attack surface of threats from vulnerabilities, malware, escalated cyberattacks, information theft and unknown exposure, device mismanagement, and misconfiguration, which are already plaguing networks. It is through the digital control of physical processing in the network that the security of the Internet of Things is no longer limited to basic security principles such as confidentiality, integrity, and non-repudiation. Network security also needs to include security protection for physical resources that receive information in the real world, as well as for various physical devices.

Therefore, this Special Issue aims to collect original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of IoT security.

Potential topics include, but are not limited to, the following:

  • Trust theories and algorithms for IoT sensing networks;
  • Quantum communication and quantum computing for IoT network security;
  • Post-quantum cryptography and algorithms for IoT network security;
  • Decentralized computing and collaborative learning for IoT network security;
  • Machine/deep learning for IoT network security;
  • AI-based data analytics for IoT network security;
  • Blockchain-related applications for IoT network security. 

Prof. Dr. Jian Li
Guest Editor

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. 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

  • cryptography
  • IoT network security
  • information security

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Related Special Issue

Published Papers (5 papers)

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Research

17 pages, 1619 KiB  
Article
Malicious Traffic Detection Method for Power Monitoring Systems Based on Multi-Model Fusion Stacking Ensemble Learning
by Hao Zhang, Ye Liang, Yuanzhuo Li, Sihan Wang, Huimin Gong, Junkai Zhai and Hua Zhang
Sensors 2025, 25(8), 2614; https://doi.org/10.3390/s25082614 - 20 Apr 2025
Viewed by 142
Abstract
With the rapid development of the internet, the increasing amount of malicious traffic poses a significant challenge to the network security of critical infrastructures, including power monitoring systems. As the core part of the power grid operation, the network security of power monitoring [...] Read more.
With the rapid development of the internet, the increasing amount of malicious traffic poses a significant challenge to the network security of critical infrastructures, including power monitoring systems. As the core part of the power grid operation, the network security of power monitoring systems directly affects the stability of the power system and the safety of electricity supply. Nowadays, network attacks are complex and diverse, and traditional rule-based detection methods are no longer adequate. With the advancement of machine learning technologies, researchers have introduced them into the field of traffic detection to address this issue. Current malicious traffic detection methods mostly rely on single machine learning models, which face problems such as poor generalization, low detection accuracy, and instability. To solve these issues, this paper proposes a malicious traffic detection method based on multi-model fusion, using the stacking strategy to integrate models. Compared to single models, stacking enhances the model’s generalization and stability, improving detection accuracy. Experimental results show that the accuracy of the stacking model on the NSL-KDD test set is 96.5%, with an F1 score of 96.6% and a false-positive rate of 1.8%, demonstrating a significant improvement over single models and validating the advantages of multi-model fusion in malicious traffic detection. Full article
(This article belongs to the Special Issue IoT Network Security (Second Edition))
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18 pages, 522 KiB  
Article
Preserving Privacy of Internet of Things Network with Certificateless Ring Signature
by Yang Zhang, Pengxiao Duan, Chaoyang Li, Hua Zhang and Haseeb Ahmad
Sensors 2025, 25(5), 1321; https://doi.org/10.3390/s25051321 - 21 Feb 2025
Cited by 1 | Viewed by 496
Abstract
With the rapid development of quantum computers and quantum computing, Internet of Things (IoT) networks equipped with traditional cryptographic algorithms have become very weak against quantum attacks. This paper focuses on the privacy-preserving problem in IoT networks and proposes a certificateless ring signature [...] Read more.
With the rapid development of quantum computers and quantum computing, Internet of Things (IoT) networks equipped with traditional cryptographic algorithms have become very weak against quantum attacks. This paper focuses on the privacy-preserving problem in IoT networks and proposes a certificateless ring signature (CLRS) scheme. This CLRS is constructed with lattice theories, which show promising advantages in resisting quantum attacks. Meanwhile, the certificateless mechanism reduces the key control ability of the key generation center (KGC) by adding personal secret keys to the private key generated by the system. Meanwhile, the ring signature mechanism protects users’ privacy information through a non-central control mechanism. Next, the security proof in a random oracle model is given, which shows that this CLRS scheme can obtain unforgeability and ensure the signer’s anonymity. Its security properties include non-repudiation, traceability, and post-quantum security. Then, the efficiency comparison and performance results show that this CLRS scheme is more efficient and practical than similar schemes. Moreover, this work presents an exploration of the post-quantum cryptographic algorithm and its application in IoT networks. Full article
(This article belongs to the Special Issue IoT Network Security (Second Edition))
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16 pages, 564 KiB  
Article
Efficient Elliptic-Curve-Cryptography-Based Anonymous Authentication for Internet of Things: Tailored Protocols for Periodic and Remote Control Traffic Patterns
by Shunfang Hu, Yuanyuan Zhang, Yanru Guo, Yanru Chen and Liangyin Chen
Sensors 2025, 25(3), 897; https://doi.org/10.3390/s25030897 - 2 Feb 2025
Viewed by 647
Abstract
IoT-based applications require effective anonymous authentication and key agreement (AKA) protocols to secure data and protect user privacy due to open communication channels and sensitive data. While AKA protocols for these applications have been extensively studied, achieving anonymity remains a challenge. AKA schemes [...] Read more.
IoT-based applications require effective anonymous authentication and key agreement (AKA) protocols to secure data and protect user privacy due to open communication channels and sensitive data. While AKA protocols for these applications have been extensively studied, achieving anonymity remains a challenge. AKA schemes using one-time pseudonyms face resynchronization issues after desynchronization attacks, and the high computational overhead of bilinear pairing and public key encryption limits its applicability. Existing schemes also lack essential security features, causing issues such as vulnerability to ephemeral secret leakage attacks and key compromise impersonation. To address these issues, we propose two novel AKA schemes, PUAKA and RCAKA, designed for different IoT traffic patterns. PUAKA improves end device anonymity in the periodic update pattern by updating one-time pseudonyms with authenticated session keys. RCAKA, for the remote control pattern, ensures anonymity while reducing communication and computation costs using shared signatures and temporary random numbers. A key contribution of RCAKA is its ability to resynchronize end devices with incomplete data in the periodic update pattern, supporting continued authentication. Both protocols’ security is proven under the Real-or-Random model. The performance comparison results show that the proposed protocols exceed existing solutions in security features and communication costs while reducing computational overhead by 32% to 50%. Full article
(This article belongs to the Special Issue IoT Network Security (Second Edition))
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24 pages, 5160 KiB  
Article
Payload State Prediction Based on Real-Time IoT Network Traffic Using Hierarchical Clustering with Iterative Optimization
by Hao Zhang, Jing Wang, Xuanyuan Wang, Kai Lu, Hao Zhang, Tong Xu and Yan Zhou
Sensors 2025, 25(1), 73; https://doi.org/10.3390/s25010073 - 26 Dec 2024
Viewed by 628
Abstract
IoT (Internet of Things) networks are vulnerable to network viruses and botnets, while facing serious network security issues. The prediction of payload states in IoT networks can detect network attacks and achieve early warning and rapid response to prevent potential threats. Due to [...] Read more.
IoT (Internet of Things) networks are vulnerable to network viruses and botnets, while facing serious network security issues. The prediction of payload states in IoT networks can detect network attacks and achieve early warning and rapid response to prevent potential threats. Due to the instability and packet loss of communications between victim network nodes, the constructed protocol state machines of existing state prediction schemes are inaccurate. In this paper, we propose a network payload predictor called IoTGuard, which can predict the payload states in IoT networks based on real-time IoT network traffic. The steps of IoTGuard are briefly as follows: Firstly, the application-layer payloads between different nodes are extracted through a module of network payload separation. Secondly, the classification of payload state within network flows is obtained via a payload extraction module. Finally, the predictor of payload state in a network is trained on a payload set, and these payloads have state labels. Experimental results on the Mozi botnet dataset show that IoTGuard can predict the state of payloads in IoT networks more accurately while ensuring execution efficiency. IoTGuard achieves an accuracy of 86% in network payload prediction, which is 8% higher than the state-of-the-art method NetZob, and the training time is reduced by 52.8%. Full article
(This article belongs to the Special Issue IoT Network Security (Second Edition))
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19 pages, 414 KiB  
Article
Quantum Privacy-Preserving Range Query Protocol for Encrypted Data in IoT Environments
by Chong-Qiang Ye, Jian Li and Xiao-Yu Chen
Sensors 2024, 24(22), 7405; https://doi.org/10.3390/s24227405 - 20 Nov 2024
Cited by 1 | Viewed by 986
Abstract
With the rapid development of IoT technology, securely querying sensitive data collected by devices within a specific range has become a focal concern for users. This paper proposes a privacy-preserving range query scheme based on quantum encryption, along with circuit simulations and performance [...] Read more.
With the rapid development of IoT technology, securely querying sensitive data collected by devices within a specific range has become a focal concern for users. This paper proposes a privacy-preserving range query scheme based on quantum encryption, along with circuit simulations and performance analysis. We first propose a quantum private set similarity comparison protocol and then construct a privacy-preserving range query scheme for IoT environments. By leveraging the properties of quantum homomorphic encryption, the proposed scheme enables encrypted data comparisons, effectively preventing the leakage of sensitive data. The correctness and security analysis demonstrates that the designed protocol guarantees users receive the correct query results while resisting both external and internal attacks. Moreover, the protocol requires only simple quantum states and operations, and does not require users to bear the cost of complex quantum resources, making it feasible under current technological conditions. Full article
(This article belongs to the Special Issue IoT Network Security (Second Edition))
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