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Electronics
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Advances in IoT Security
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Advances in IoT Security

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 14244

Special Issue Editors

Dr. Abebe Diro

E-Mail Website
Guest Editor
College of Business and Law, RMIT University, Melbourne, VIC 3001, Australia
Interests: cybersecurity; artificial intelligence; network and communications
Special Issues, Collections and Topics in MDPI journals
Dr. Mohiuddin Ahmed

E-Mail Website
Guest Editor
Computing and Security, School of Science, Edith Cowan University, Joondalup, WA 6027, Australia
Interests: cybersecurity; anomaly detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are delighted to announce a special issue on "Advances in IoT Security" in MDPI  Electronics. The Internet of Things (IoT) has transformed various industries by connecting numerous devices and enabling seamless communication and data exchange. However, this proliferation of interconnected devices has also raised significant security concerns, as IoT networks are susceptible to various cyber threats and attacks.

The objective of this special issue is to explore the latest advancements, challenges, and opportunities in IoT security and present innovative solutions to address the evolving cybersecurity landscape. We invite researchers and practitioners to contribute original research papers, review articles, and case studies that shed light on various aspects of IoT security.

Potential topics of interest for this special issue include, but are not limited to:

  • Threat modeling and risk assessment for IoT systems
  • Cryptographic techniques and protocols for securing IoT communications
  • Privacy protection and data integrity in IoT environments
  • Intrusion detection and prevention mechanisms for IoT networks
  • Trust and identity management in IoT ecosystems
  • Security architectures and frameworks for IoT deployments
  • Secure firmware and software update mechanisms for IoT devices
  • Machine learning and artificial intelligence for IoT security analytics
  • Blockchain-based approaches for securing IoT systems
  • Secure authentication and access control in IoT environments
  • Physical layer security techniques for IoT networks
  • Standards and regulations for ensuring IoT security and privacy

We encourage authors to present novel research findings, empirical evaluations, and practical implementations that contribute to the advancement of IoT security. All submitted manuscripts will undergo a rigorous peer-review process to ensure the highest quality and relevance to the field.

Dr. Abebe Diro
Dr. Mohiuddin Ahmed
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. Electronics 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 2400 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

  • cybersecurity
  • IoT Security
  • anomaly detection
  • blockchain

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Published Papers (7 papers)

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Research

19 pages, 1264 KiB  
Article
Real-Time Adaptive and Lightweight Anomaly Detection Based on a Chaotic System in Cyber–Physical Systems
by Jung Kyu Park and Youngmi Baek
Electronics 2025, 14(3), 598; https://doi.org/10.3390/electronics14030598 - 3 Feb 2025
Cited by 1 | Viewed by 983
Abstract
When cyber–physical systems (CPSs) are connected to the Internet or other CPSs with connectivity, external adversaries can potentially gain access to the CPS and attempt to control the electronic control units (ECUs). In particular, the lack of confidentiality and integrity in the controller [...] Read more.
When cyber–physical systems (CPSs) are connected to the Internet or other CPSs with connectivity, external adversaries can potentially gain access to the CPS and attempt to control the electronic control units (ECUs). In particular, the lack of confidentiality and integrity in the controller area networks (CANs) of CPSs makes it difficult to distinguish malicious data from legitimate data. The security vulnerabilities of CPSs, which are frequently exposed to adversaries, pose the risk of destabilizing the lives of humans. Therefore, we propose a real-time adaptive and lightweight anomaly detection (RALAD) mechanism that efficiently and securely detects anomalies within a given virtual group though verification of the data integrity and key management of stateless synchronization based on a chaotic system while driving. These characteristics prevent an adversary from authenticating maliciously modified messages even though it captures legitimate messages on the CAN bus. RALAD shows a clear difference from others in terms of (1) its unique secret key-sharing method and approach to secret key generation for each message, (2) safe controlling support after detecting anomalies, and (3) its software-based solution that eliminates the need for hardware secure modules. It leads to freedom from the issues of additional cost, weight, and wiring in CPSs. The proposed method achieves real-time anomaly detection, and the experiment results show a 100% detection rate for all attacks. This demonstrates that RALAD maintains high reliability and efficiency, even under various bus load conditions and attack rates. Full article
(This article belongs to the Special Issue Advances in IoT Security)
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24 pages, 8199 KiB  
Article
Redefining 6G Network Slicing: AI-Driven Solutions for Future Use Cases
by Robert Botez, Daniel Zinca and Virgil Dobrota
Electronics 2025, 14(2), 368; https://doi.org/10.3390/electronics14020368 - 18 Jan 2025
Cited by 1 | Viewed by 1822
Abstract
The evolution from 5G to 6G networks is driven by the need to meet the stringent requirements, i.e., ultra-reliable, low-latency, and high-throughput communication. The new services are called Further-Enhanced Mobile Broadband (feMBB), Extremely Reliable and Low-Latency Communications (ERLLCs), Ultra-Massive Machine-Type Communications (umMTCs), Massive [...] Read more.
The evolution from 5G to 6G networks is driven by the need to meet the stringent requirements, i.e., ultra-reliable, low-latency, and high-throughput communication. The new services are called Further-Enhanced Mobile Broadband (feMBB), Extremely Reliable and Low-Latency Communications (ERLLCs), Ultra-Massive Machine-Type Communications (umMTCs), Massive Ultra-Reliable Low-Latency Communications (mURLLCs), and Mobile Broadband Reliable Low-Latency Communications (MBRLLCs). Network slicing emerges as a critical enabler in 6G, providing virtualized, end-to-end network segments tailored to diverse application needs. Despite its significance, existing datasets for slice selection are limited to 5G or LTE-A contexts, lacking relevance to the enhanced requirements. In this work, we present a novel synthetic dataset tailored to 6G network slicing. By analyzing the emerging service requirements, we generated traffic parameters, including latency, packet loss, jitter, and transfer rates. Machine Learning (ML) models like Random Forest (RF), Decision Tree (DT), XGBoost, Support Vector Machine (SVM), and Feedforward Neural Network (FNN) were trained on this dataset, achieving over 99% accuracy in both slice classification and handover prediction. Our results highlight the potential of this dataset as a valuable tool for developing AI-assisted 6G network slicing mechanisms. While still in its early stages, the dataset lays a foundation for future research. As the 6G standardization advances, we aim to refine the dataset and models, ultimately enabling real-time, intelligent slicing solutions for next-generation networks. Full article
(This article belongs to the Special Issue Advances in IoT Security)
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22 pages, 552 KiB  
Article
SelTZ: Fine-Grained Data Protection for Edge Neural Networks Using Selective TrustZone Execution
by Sehyeon Jeong and Hyunyoung Oh
Electronics 2025, 14(1), 123; https://doi.org/10.3390/electronics14010123 - 31 Dec 2024
Viewed by 778
Abstract
This paper presents an approach to protecting deep neural network privacy on edge devices using ARM TrustZone. We propose a selective layer protection technique that balances performance and privacy. Rather than executing entire layers within the TrustZone secure environment, which leads to significant [...] Read more.
This paper presents an approach to protecting deep neural network privacy on edge devices using ARM TrustZone. We propose a selective layer protection technique that balances performance and privacy. Rather than executing entire layers within the TrustZone secure environment, which leads to significant performance and memory overhead, we selectively protect only the most sensitive subset of data from each layer. Our method strategically partitions layer computations between normal and secure worlds, optimizing TrustZone usage while providing robust defenses against privacy attacks. Through extensive experiments on standard datasets (CIFAR-100 and ImageNet-Tiny), we demonstrate that our approach reduces membership inference attack (MIA) success rates from over 90% to near random guess (50%) while achieving up to 7.3× speedup and 71% memory reduction compared to state-of-the-art approaches. On resource-constrained edge devices with limited secure memory, our selective approach enables protection of significantly more layers than full layer protection methods while maintaining strong privacy guarantees through efficient data partitioning and parallel processing across security boundaries. Full article
(This article belongs to the Special Issue Advances in IoT Security)
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19 pages, 1798 KiB  
Article
Drift Adaptive Online DDoS Attack Detection Framework for IoT System
by Yonas Kibret Beshah, Surafel Lemma Abebe and Henock Mulugeta Melaku
Electronics 2024, 13(6), 1004; https://doi.org/10.3390/electronics13061004 - 7 Mar 2024
Cited by 11 | Viewed by 2142
Abstract
Internet of Things (IoT) security is becoming important with the growing popularity of IoT devices and their wide applications. Recent network security reports revealed a sharp increase in the type, frequency, sophistication, and impact of distributed denial of service (DDoS) attacks on IoT [...] Read more.
Internet of Things (IoT) security is becoming important with the growing popularity of IoT devices and their wide applications. Recent network security reports revealed a sharp increase in the type, frequency, sophistication, and impact of distributed denial of service (DDoS) attacks on IoT systems, making DDoS one of the most challenging threats. DDoS is used to commit actual, effective, and profitable cybercrimes. The current machine learning-based IoT DDoS attack detection systems use batch learning techniques, and hence are unable to maintain their performance over time in a dynamic environment. The dynamicity of heterogeneous IoT data causes concept drift issues that result in performance degradation and automation difficulties in detecting DDoS. In this study, we propose an adaptive online DDoS attack detection framework that detects and adapts to concept drifts in streaming data using a number of features often used in DDoS attack detection. This paper also proposes a novel accuracy update weighted probability averaging ensemble (AUWPAE) approach to detect concept drift and optimize zero-day DDoS detection. We evaluated the proposed framework using IoTID20 and CICIoT2023 dataset containing benign and DDoS traffic data. The results show that the proposed adaptive online DDoS attack detection framework is able to detect DDoS attacks with an accuracy of 99.54% and 99.33% for the respective datasets. Full article
(This article belongs to the Special Issue Advances in IoT Security)
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15 pages, 4143 KiB  
Article
Traffic Fingerprints for Homogeneous IoT Traffic Based on Packet Payload Transition Patterns
by Mingrui Fan, Jiaqi Gao, Yaru He, Weidong Shi and Yueming Lu
Electronics 2024, 13(5), 930; https://doi.org/10.3390/electronics13050930 - 29 Feb 2024
Viewed by 1417
Abstract
Traffic fingerprint was considered an effective security protection mechanism in IoT scenarios because it can be used to automatically identify accessed devices. However, the results of replication experiments show that the classic traffic fingerprints based on simple network traffic attribute features have a [...] Read more.
Traffic fingerprint was considered an effective security protection mechanism in IoT scenarios because it can be used to automatically identify accessed devices. However, the results of replication experiments show that the classic traffic fingerprints based on simple network traffic attribute features have a significantly lower ability to identify accessed devices in real 5G IoT scenarios compared to what was stated in traditional IoT scenarios. The growing homogenization of IoT traffic caused by the application of 5G is believed to be the reason for the poor ability of traditional traffic fingerprints to identify 5G IoT terminals. Studying an enhanced traffic fingerprint is necessary to accommodate the homogeneous Internet of Things traffic. In addition, during the reproducing experiments, we noticed that the solution of overlap is a key factor that restricts the recognition ability of one-vs-all multi-classifiers, and the efficiency of existing methods still has some room for optimization. Based on targeted improvements to these two issues, we proposed an enhanced IoT terminal traffic fingerprint based on packet payload transition patterns to improve the device recognition ability in homogeneous IoT traffic. Additionally, we designed an improved solution for overlap based on density centers to expedite decision making. According to the experimental results, when compared with the existing traffic fingerprint, the proposed traffic fingerprint in this study demonstrated a Macro-Average Precision of close to 90% for network traffic from real 5G IoT terminals. The proposed overlap solution based on the density centers reduced the decision-making time from hundreds of seconds to tens of seconds while ensuring decision-making accuracy. Full article
(This article belongs to the Special Issue Advances in IoT Security)
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24 pages, 4705 KiB  
Article
Machine Learning Techniques for Cyberattack Prevention in IoT Systems: A Comparative Perspective of Cybersecurity and Cyberdefense in Colombia
by Emanuel Ortiz-Ruiz, Juan Ramón Bermejo, Juan Antonio Sicilia and Javier Bermejo
Electronics 2024, 13(5), 824; https://doi.org/10.3390/electronics13050824 - 20 Feb 2024
Cited by 3 | Viewed by 3596
Abstract
This study investigates the application of machine learning techniques for cyberattack prevention in Internet of Things (IoT) systems, focusing on the specific context of cyberattacks in Colombia. The research presents a comparative perspective on cyberattacks in Colombia, aiming to identify the most effective [...] Read more.
This study investigates the application of machine learning techniques for cyberattack prevention in Internet of Things (IoT) systems, focusing on the specific context of cyberattacks in Colombia. The research presents a comparative perspective on cyberattacks in Colombia, aiming to identify the most effective machine learning methods for mitigating and preventing such threats. The study evaluates the performance of logistic regression, naïve Bayes, perceptron, and k-nearest neighbors algorithms in the context of cyberattack prevention. Results reveal the strengths and weaknesses of these techniques in addressing the unique challenges posed by cyberattackers in Colombia’s IoT infrastructure. The findings provide valuable insights for enhancing cybersecurity measures in the region and contribute to the broader field of IoT security. Full article
(This article belongs to the Special Issue Advances in IoT Security)
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20 pages, 1618 KiB  
Article
Leveraging Artificial Intelligence and Provenance Blockchain Framework to Mitigate Risks in Cloud Manufacturing in Industry 4.0
by Mifta Ahmed Umer, Elefelious Getachew Belay and Luis Borges Gouveia
Electronics 2024, 13(3), 660; https://doi.org/10.3390/electronics13030660 - 5 Feb 2024
Cited by 3 | Viewed by 2657
Abstract
Cloud manufacturing is an evolving networked framework that enables multiple manufacturers to collaborate in providing a range of services, including design, development, production, and post-sales support. The framework operates on an integrated platform encompassing a range of Industry 4.0 technologies, such as Industrial [...] Read more.
Cloud manufacturing is an evolving networked framework that enables multiple manufacturers to collaborate in providing a range of services, including design, development, production, and post-sales support. The framework operates on an integrated platform encompassing a range of Industry 4.0 technologies, such as Industrial Internet of Things (IIoT) devices, cloud computing, Internet communication, big data analytics, artificial intelligence, and blockchains. The connectivity of industrial equipment and robots to the Internet opens cloud manufacturing to the massive attack risk of cybersecurity and cyber crime threats caused by external and internal attackers. The impacts can be severe because the physical infrastructure of industries is at stake. One potential method to deter such attacks involves utilizing blockchain and artificial intelligence to track the provenance of IIoT devices. This research explores a practical approach to achieve this by gathering provenance data associated with operational constraints defined in smart contracts and identifying deviations from these constraints through predictive auditing using artificial intelligence. A software architecture comprising IIoT communications to machine learning for comparing the latest data with predictive auditing outcomes and logging appropriate risks was designed, developed, and tested. The state changes in the smart ledger of smart contracts were linked with the risks so that the blockchain peers can detect high deviations and take actions in a timely manner. The research defined the constraints related to physical boundaries and weightlifting limits allocated to three forklifts and showcased the mechanisms of detecting risks of breaking these constraints with the help of artificial intelligence. It also demonstrated state change rejections by blockchains at medium and high-risk levels. This study followed software development in Java 8 using JDK 8, CORDA blockchain framework, and Weka package for random forest machine learning. As a result of this, the model, along with its design and implementation, has the potential to enhance efficiency and productivity, foster greater trust and transparency in the manufacturing process, boost risk management, strengthen cybersecurity, and advance sustainability efforts. Full article
(This article belongs to the Special Issue Advances in IoT Security)
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