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Recent Trends and Advances in Sensors Cybersecurity
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Recent Trends and Advances in Sensors Cybersecurity

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

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 6474

Special Issue Editors

Prof. Dr. Wei Lu

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Guest Editor
Keene State College, University System of New Hampshire, Concord, NH 03435, USA
Interests: data science; cybersecurity; artificial intelligence
Prof. Dr. Isaac Woungang

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Guest Editor
Department of Computer Science, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
Interests: next generation wireless networks; cloud computing; computational intelligence; telecommunications network design; management and control; distributing systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the past two decades, we have witnessed a strong convergence of human activities with mobile computing, sensing technologies, and sensor systems in the power grid, smart cities, IoT-enabled industrial systems, healthcare, and autonomous automotive industry. Such a merging of human activities with technologies using sensors accelerates scientific discoveries and increases dramatically the opportunities for new businesses, thus resulting in creating powerful information infrastructures for critical facilities, its success, however, has also paved the way for a large number of criminal activities while happening in the cyber domain and having strong implications in the real world. Existing cybersecurity mechanisms generate a number of good ideas, they are far from completed yet due to the evolution of cyberattacks targeted at sensors and their networking systems. This Special Issue, therefore, aims to put together original research and review articles on recent advances, technologies, solutions, applications, and new challenges in this multidisciplinary research field of sensors cybersecurity, with a focus on various aspects of privacy, security, and trust across the entire cybersecurity spectrum. 

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

(1) Sensors-based Critical Infrastructure Protection
(2) Network and Wireless Security on Emerging Wearable Sensors and Systems
(3) Security and Privacy of Sensors with Web and Cloud Security 
(4) Hardware and Software Security in Physical Sensors and Sensors in Industrial Practices
(5) Cybersecurity on Emerging Sensor Applications
(6) Security and Privacy on Internet of Things (IoT) Enabled Sensors
(7) Intrusion Detection and Prevention Technologies on Sensor Networks 
(8) Security Analytics and Data Mining in the Sensors Data Processing 
(9) Cryptographic Technologies and Their Applications on Sensors
(10) Identity, Access Control, and Trust Management for Sensors 
(11) Emerging Cybersecurity Issues in Chemical, Electrochemical and Gas Sensors, Microfluidics and Biosensors, Optical Sensors, Microwave Sensors, and Acoustic and Ultrasonic Sensors

Prof. Dr. Wei Lu
Prof. Dr. Isaac Woungang
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. 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.

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Published Papers (4 papers)

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Research

18 pages, 4163 KiB  
Article
Privacy-Preserving Synthetic Data Generation Method for IoT-Sensor Network IDS Using CTGAN
by Saleh Alabdulwahab, Young-Tak Kim and Yunsik Son
Sensors 2024, 24(22), 7389; https://doi.org/10.3390/s24227389 - 20 Nov 2024
Viewed by 269
Abstract
The increased usage of IoT networks brings about new privacy risks, especially when intrusion detection systems (IDSs) rely on large datasets for machine learning (ML) tasks and depend on third parties for storing and training the ML-based IDS. This study proposes a privacy-preserving [...] Read more.
The increased usage of IoT networks brings about new privacy risks, especially when intrusion detection systems (IDSs) rely on large datasets for machine learning (ML) tasks and depend on third parties for storing and training the ML-based IDS. This study proposes a privacy-preserving synthetic data generation method using a conditional tabular generative adversarial network (CTGAN) aimed at maintaining the utility of IoT sensor network data for IDS while safeguarding privacy. We integrate differential privacy (DP) with CTGAN by employing controlled noise injection to mitigate privacy risks. The technique involves dynamic distribution adjustment and quantile matching to balance the utility–privacy tradeoff. The results indicate a significant improvement in data utility compared to the standard DP method, achieving a KS test score of 0.80 while minimizing privacy risks such as singling out, linkability, and inference attacks. This approach ensures that synthetic datasets can support intrusion detection without exposing sensitive information. Full article
(This article belongs to the Special Issue Recent Trends and Advances in Sensors Cybersecurity)
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23 pages, 1151 KiB  
Article
Enhancing Cybersecurity in Healthcare: Evaluating Ensemble Learning Models for Intrusion Detection in the Internet of Medical Things
by Theyab Alsolami, Bader Alsharif and Mohammad Ilyas
Sensors 2024, 24(18), 5937; https://doi.org/10.3390/s24185937 - 13 Sep 2024
Cited by 1 | Viewed by 1444
Abstract
This study investigates the efficacy of machine learning models for intrusion detection in the Internet of Medical Things, aiming to enhance cybersecurity defenses and protect sensitive healthcare data. The analysis focuses on evaluating the performance of ensemble learning algorithms, specifically Stacking, Bagging, and [...] Read more.
This study investigates the efficacy of machine learning models for intrusion detection in the Internet of Medical Things, aiming to enhance cybersecurity defenses and protect sensitive healthcare data. The analysis focuses on evaluating the performance of ensemble learning algorithms, specifically Stacking, Bagging, and Boosting, using Random Forest and Support Vector Machines as base models on the WUSTL-EHMS-2020 dataset. Through a comprehensive examination of performance metrics such as accuracy, precision, recall, and F1-score, Stacking demonstrates exceptional accuracy and reliability in detecting and classifying cyber attack incidents with an accuracy rate of 98.88%. Bagging is ranked second, with an accuracy rate of 97.83%, while Boosting yielded the lowest accuracy rate of 88.68%. Full article
(This article belongs to the Special Issue Recent Trends and Advances in Sensors Cybersecurity)
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15 pages, 472 KiB  
Article
A Novel Anomaly-Based Intrusion Detection Model Using PSOGWO-Optimized BP Neural Network and GA-Based Feature Selection
by Saeid Sheikhi and Panos Kostakos
Sensors 2022, 22(23), 9318; https://doi.org/10.3390/s22239318 - 30 Nov 2022
Cited by 13 | Viewed by 1991
Abstract
Intrusion detection systems (IDS) are crucial for network security because they enable detection of and response to malicious traffic. However, as next-generation communications networks become increasingly diversified and interconnected, intrusion detection systems are confronted with dimensionality difficulties. Prior works have shown that high-dimensional [...] Read more.
Intrusion detection systems (IDS) are crucial for network security because they enable detection of and response to malicious traffic. However, as next-generation communications networks become increasingly diversified and interconnected, intrusion detection systems are confronted with dimensionality difficulties. Prior works have shown that high-dimensional datasets that simulate real-world network data increase the complexity and processing time of IDS system training and testing, while irrelevant features waste resources and reduce the detection rate. In this paper, a new intrusion detection model is presented which uses a genetic algorithm (GA) for feature selection and optimization algorithms for gradient descent. First, the GA-based method is used to select a set of highly correlated features from the NSL-KDD dataset that can significantly improve the detection ability of the proposed model. A Back-Propagation Neural Network (BPNN) is then trained using the HPSOGWO method, a hybrid combination of the Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) algorithms. Finally, the hybrid HPSOGWO-BPNN algorithm is used to solve binary and multi-class classification problems on the NSL-KDD dataset. The experimental outcomes demonstrate that the proposed model achieves better performance than other techniques in terms of accuracy, with a lower error rate and better ability to detect different types of attacks. Full article
(This article belongs to the Special Issue Recent Trends and Advances in Sensors Cybersecurity)
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17 pages, 727 KiB  
Article
A Formal Energy Consumption Analysis to Secure Cluster-Based WSN: A Case Study of Multi-Hop Clustering Algorithm Based on Spectral Classification Using Lightweight Blockchain
by Yves Frédéric Ebobissé Djéné, Mohammed Sbai El Idrissi, Pierre-Martin Tardif, Ali Jorio, Brahim El Bhiri and Youssef Fakhri
Sensors 2022, 22(20), 7730; https://doi.org/10.3390/s22207730 - 12 Oct 2022
Cited by 2 | Viewed by 1593
Abstract
Wireless Sensors Networks are integrating human daily life at a fast rate. Applications cover a wide range of fields, including home security, agriculture, climate change, fire prevention, and so on and so forth. If WSN were initially flat networks, hierarchical, or cluster-based networks [...] Read more.
Wireless Sensors Networks are integrating human daily life at a fast rate. Applications cover a wide range of fields, including home security, agriculture, climate change, fire prevention, and so on and so forth. If WSN were initially flat networks, hierarchical, or cluster-based networks have been introduced in order to achieve a better performance in terms of energy efficiency, topology management, delay minimization, load balancing, routing techniques, etc. As cluster-based algorithms proved to be efficient in terms of energy balancing, security has been of less importance in the field. Data shared by nodes in a WSN can be very sensitive depending on the field of application. Therefore, it is important to ensure security at various levels of WSN. This paper proposes a formal modeling of the energy consumed to secure communications in a cluster-based WSN in general. The concept is implemented using the Proof of Authentication (POAh) paradigm of blockchain and applied to a Multi-hop Clustering Algorithm based on spectral classification. The studied metrics are residual energy in the network, the number of alive nodes, first and last dead node. Full article
(This article belongs to the Special Issue Recent Trends and Advances in Sensors Cybersecurity)
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