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AI-Driven Cybersecurity in IoT-Based Systems
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AI-Driven Cybersecurity in IoT-Based Systems

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

Deadline for manuscript submissions: 1 May 2025 | Viewed by 7495

Special Issue Editors

Prof. Dr. Wenbing Zhao

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, OH 44115, USA
Interests: computational intelligence; Internet of Things; big data
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Pan Wang

E-Mail Website
Guest Editor
School of Modern Posts, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Interests: AI enabled network management; information and network security; Internet of Things; AI for recommender system; future communications and networks

Special Issue Information

Dear Colleagues,

With the rapid development of the Internet of Things (IoT), including Industrial IoT and Home IoT, sensors and IoT devices are starting to play an ever increasingly important role in various fields such as smart grids, power plants, manufacturing, and supply chains. Such sensors and IoT-based systems could be vulnerable to various cyber attacks. As an innovative data-driven paradigm, artificial intelligence (AI) has revolutionized several classical areas such as image, video, speech, and text processing. However, when applying to mitigating cyberattacks, the inherent limitations of AI such as data-dependent, weak generalization, and lack of explanability have obstructed the development of AI-driven cybersecurity. Furthermore, sensors and IoT devices have imposed additional obstables, including low power, heterogenity, and weak computing power, which makes applying AI for cybersecurity even more challenging. In this Special Issue, we are welcoming research and development that aims to apply and improve AI to enable and empower cybersecurity in sensors and IoT-based systems.

Prof. Dr. Wenbing Zhao
Prof. Dr. Pan Wang
Guest Editors

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Keywords

  • AI
  • cybersecruity
  • IoT
  • sensor based system

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Published Papers (6 papers)

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Research

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24 pages, 2431 KiB  
Article
Identifying Tampered Radio-Frequency Transmissions in LoRa Networks Using Machine Learning
by Nurettin Selcuk Senol, Amar Rasheed, Mohamed Baza and Maazen Alsabaan
Sensors 2024, 24(20), 6611; https://doi.org/10.3390/s24206611 - 14 Oct 2024
Cited by 1 | Viewed by 1084
Abstract
Long-range networks, renowned for their long-range, low-power communication capabilities, form the backbone of many Internet of Things systems, enabling efficient and reliable data transmission. However, detecting tampered frequency signals poses a considerable challenge due to the vulnerability of LoRa devices to radio-frequency interference [...] Read more.
Long-range networks, renowned for their long-range, low-power communication capabilities, form the backbone of many Internet of Things systems, enabling efficient and reliable data transmission. However, detecting tampered frequency signals poses a considerable challenge due to the vulnerability of LoRa devices to radio-frequency interference and signal manipulation, which can undermine both data integrity and security. This paper presents an innovative method for identifying tampered radio frequency transmissions by employing five sophisticated anomaly detection algorithms—Local Outlier Factor, Isolation Forest, Variational Autoencoder, traditional Autoencoder, and Principal Component Analysis within the framework of a LoRa-based Internet of Things network structure. The novelty of this work lies in applying image-based tampered frequency techniques with these algorithms, offering a new perspective on securing LoRa transmissions. We generated a dataset of over 26,000 images derived from real-world experiments with both normal and manipulated frequency signals by splitting video recordings of LoRa transmissions into frames to thoroughly assess the performance of each algorithm. Our results demonstrate that Local Outlier Factor achieved the highest accuracy of 97.78%, followed by Variational Autoencoder, traditional Autoencoder and Principal Component Analysis at 97.27%, and Isolation Forest at 84.49%. These findings highlight the effectiveness of these methods in detecting tampered frequencies, underscoring their potential for enhancing the reliability and security of LoRa networks. Full article
(This article belongs to the Special Issue AI-Driven Cybersecurity in IoT-Based Systems)
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12 pages, 17759 KiB  
Article
Attention-Enhanced Defensive Distillation Network for Channel Estimation in V2X mm-Wave Secure Communication
by Xingyu Qi, Yuanjian Liu and Yingchun Ye
Sensors 2024, 24(19), 6464; https://doi.org/10.3390/s24196464 - 7 Oct 2024
Viewed by 1037
Abstract
Millimeter-wave (mm-wave) technology, crucial for future networks and vehicle-to-everything (V2X) communication in intelligent transportation, offers high data rates and bandwidth but is vulnerable to adversarial attacks, like interference and eavesdropping. It is crucial to protect V2X mm-wave communication from cybersecurity attacks, as traditional [...] Read more.
Millimeter-wave (mm-wave) technology, crucial for future networks and vehicle-to-everything (V2X) communication in intelligent transportation, offers high data rates and bandwidth but is vulnerable to adversarial attacks, like interference and eavesdropping. It is crucial to protect V2X mm-wave communication from cybersecurity attacks, as traditional security measures often fail to counter sophisticated threats and complex attacks. To tackle these difficulties, the current study introduces an attention-enhanced defensive distillation network (AEDDN) to improve robustness and accuracy in V2X mm-wave communication under adversarial attacks. The AEDDN model combines the transformer algorithm with defensive distillation, leveraging the transformer’s attention mechanism to focus on critical channel features and adapt to complex conditions. This helps mitigate adversarial examples by filtering misleading data. Defensive distillation further strengthens the model by smoothing decision boundaries, making it less sensitive to small perturbations. To evaluate and validate the AEDDN model, this study uses a publicly available dataset called 6g-channel-estimation and a proprietary dataset named MMMC, comparing the simulation results with the convolutional neural network (CNN) model. The findings from the experiments indicate that the AEDDN, especially in the complex V2X mm-wave environment, demonstrates enhanced performance. Full article
(This article belongs to the Special Issue AI-Driven Cybersecurity in IoT-Based Systems)
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15 pages, 1694 KiB  
Article
Improved Scheme for Data Aggregation of Distributed Oracle for Intelligent Internet of Things
by Ruiyang Gao, Yongtao Xue, Wei Wang, Yin Lu, Guan Gui and Shimin Xu
Sensors 2024, 24(17), 5625; https://doi.org/10.3390/s24175625 - 30 Aug 2024
Viewed by 1019
Abstract
Oracle is a data supply mechanism that provides real-world data for blockchain. It serves as a bridge between blockchain and the IoT world, playing a crucial role in solving problems such as data sharing and device management in the IoT field. The main [...] Read more.
Oracle is a data supply mechanism that provides real-world data for blockchain. It serves as a bridge between blockchain and the IoT world, playing a crucial role in solving problems such as data sharing and device management in the IoT field. The main challenge at this stage is determining how to achieve data privacy protection in distributed Oracle machines to safeguard the value hidden in data on the blockchain. In this paper, we propose an improved scheme for distributed Oracle data aggregation based on Paillier encryption algorithm, which achieves end-to-end data privacy protection from devices to users. To address the issue of dishonest distributed Oracle machines running out of funds, we have designed an algorithm called PICA (Paillier-based InChain Aggregation). Based on the aggregation on the Chainlink chain and the Paillier encryption algorithm, random numbers are introduced to avoid the problem of dishonest Oracle machines running out of funds. We use the traffic coverage method to solve the problem of exposed request paths in distributed Oracle machines. Simulation and experimental results show that in small and medium-sized IoT application scenarios with 10,000 data nodes, each additional false request in a single request will result in a delay of about 2 s in data acquisition and can achieve a request response time of 20 s. The proposed method can achieve user data privacy protection. Full article
(This article belongs to the Special Issue AI-Driven Cybersecurity in IoT-Based Systems)
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17 pages, 1107 KiB  
Article
Explainable Deep Learning-Based Feature Selection and Intrusion Detection Method on the Internet of Things
by Xuejiao Chen, Minyao Liu, Zixuan Wang and Yun Wang
Sensors 2024, 24(16), 5223; https://doi.org/10.3390/s24165223 - 12 Aug 2024
Cited by 2 | Viewed by 1230
Abstract
With the rapid advancement of the Internet of Things, network security has garnered increasing attention from researchers. Applying deep learning (DL) has significantly enhanced the performance of Network Intrusion Detection Systems (NIDSs). However, due to its complexity and “black box” problem, deploying DL-based [...] Read more.
With the rapid advancement of the Internet of Things, network security has garnered increasing attention from researchers. Applying deep learning (DL) has significantly enhanced the performance of Network Intrusion Detection Systems (NIDSs). However, due to its complexity and “black box” problem, deploying DL-based NIDS models in practical scenarios poses several challenges, including model interpretability and being lightweight. Feature selection (FS) in DL models plays a crucial role in minimizing model parameters and decreasing computational overheads while enhancing NIDS performance. Hence, selecting effective features remains a pivotal concern for NIDSs. In light of this, this paper proposes an interpretable feature selection method for encrypted traffic intrusion detection based on SHAP and causality principles. This approach utilizes the results of model interpretation for feature selection to reduce feature count while ensuring model reliability. We evaluate and validate our proposed method on two public network traffic datasets, CICIDS2017 and NSL-KDD, employing both a CNN and a random forest (RF). Experimental results demonstrate superior performance achieved by our proposed method. Full article
(This article belongs to the Special Issue AI-Driven Cybersecurity in IoT-Based Systems)
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25 pages, 874 KiB  
Article
PrivShieldROS: An Extended Robot Operating System Integrating Ethereum and Interplanetary File System for Enhanced Sensor Data Privacy
by Tianhao Wang, Ke Chen, Zhaohua Zheng, Jiahao Guo, Xiying Zhao and Shenhui Zhang
Sensors 2024, 24(10), 3241; https://doi.org/10.3390/s24103241 - 20 May 2024
Cited by 3 | Viewed by 1292
Abstract
With the application of robotics in security monitoring, medical care, image analysis, and other high-privacy fields, vision sensor data in robotic operating systems (ROS) faces the challenge of enhancing secure storage and transmission. Recently, it has been proposed that the distributed advantages of [...] Read more.
With the application of robotics in security monitoring, medical care, image analysis, and other high-privacy fields, vision sensor data in robotic operating systems (ROS) faces the challenge of enhancing secure storage and transmission. Recently, it has been proposed that the distributed advantages of blockchain be taken advantage of to improve the security of data in ROS. Still, it has limitations such as high latency and large resource consumption. To address these issues, this paper introduces PrivShieldROS, an extended robotic operating system developed by InterPlanetary File System (IPFS), blockchain, and HybridABEnc to enhance the confidentiality and security of vision sensor data in ROS. The system takes advantage of the decentralized nature of IPFS to enhance data availability and robustness while combining HybridABEnc for fine-grained access control. In addition, it ensures the security and confidentiality of the data distribution mechanism by using blockchain technology to store data content identifiers (CID) persistently. Finally, the effectiveness of this system is verified by three experiments. Compared with the state-of-the-art blockchain-extended ROS, PrivShieldROS shows improvements in key metrics. This paper has been partly submitted to IROS 2024. Full article
(This article belongs to the Special Issue AI-Driven Cybersecurity in IoT-Based Systems)
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Review

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25 pages, 699 KiB  
Review
Blockchain-Facilitated Cybersecurity for Ubiquitous Internet of Things with Space–Air–Ground Integrated Networks: A Survey
by Wenbing Zhao, Shunkun Yang and Xiong Luo
Sensors 2025, 25(2), 383; https://doi.org/10.3390/s25020383 - 10 Jan 2025
Viewed by 574
Abstract
This article presents a systematic review on blockchain-facilitated cybersecurity solutions for Internet of Things (IoT) devices in space–air–ground integrated networks (SAGIN). First, we identify the objectives and the context of the blockchain-based solutions for SAGIN. Although, typically, the blockchain is primarily used to [...] Read more.
This article presents a systematic review on blockchain-facilitated cybersecurity solutions for Internet of Things (IoT) devices in space–air–ground integrated networks (SAGIN). First, we identify the objectives and the context of the blockchain-based solutions for SAGIN. Although, typically, the blockchain is primarily used to enhance the trustworthiness of some systems or operations, it is necessary to document exactly in what context the blockchain is used that is specific to the IoT and SAGIN. Second, we investigate how blockchain technology is used to achieve the objectives. Again, we want to report the technical details on how blockchain is used in this specific field instead of general discussion. Third, we provide a critique on the technical correctness of the blockchain-based solutions. As we elaborate in this article, there are serious technical issues in the proposed solutions. The most pervasive assumption made in many blockchain-based solutions is that higher-level trustworthiness can be achieved by using any form of blockchain. Fourth, we provide a guideline on when blockchain technology could be useful for IoT and SAGIN and what types of blockchain could be useful to enhance the security of ubiquitous IoT in SAGIN. Full article
(This article belongs to the Special Issue AI-Driven Cybersecurity in IoT-Based Systems)
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