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Cybersecurity Attack and Defense in Wireless Sensors Networks
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Cybersecurity Attack and Defense in Wireless Sensors Networks

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

Deadline for manuscript submissions: closed (10 July 2024) | Viewed by 14665

Special Issue Editors

Prof. Dr. Tony Jan

E-Mail Website
Guest Editor
School of Design, Torrens University, Sydney, NSW 2007, Australia
Interests: cybersecurity for Internet of Things (IoT) applications; IoT fog analytics for real-time ICT applications (augmented reality); smart cities and real-time digital infrastructures (smart traffics and digital twins)
Special Issues, Collections and Topics in MDPI journals
Dr. Ammar Alazab

E-Mail Website
Guest Editor
School of IT, Melbourne Institute of Technology, Melbourne, VIC 3000, Australia
Interests: information security; database; machine learning; smart 5G networks; green 5G communication; sleep mode; Markov decision process; energy efficient 5G networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, wireless sensor networks have gained tremendous popularity across various domains, enabling seamless data collection and analysis. However, with their widespread deployment, they have become a target for potential cybersecurity threats. As these networks play a vital role in critical applications such as healthcare, environmental monitoring, industrial automation, and smart cities, it is imperative to explore and address the challenges posed by cyberattacks.

This Special Issue aims to collect cutting-edge research, innovative solutions, and practical insights from experts in the field of cybersecurity and wireless sensor networks. We invite authors to submit their original research papers, review articles, case studies, and experimental studies that address, but are not limited to, the following topics:

  1. Threat modeling and risk assessment in wireless sensor networks;
  2. Novel cyber-attack detection and prevention techniques;
  3. Secure communication protocols for wireless sensor networks;
  4. Intrusion detection and response mechanisms in sensor networks;
  5. Machine learning and AI approaches for cybersecurity in sensor networks;
  6. Privacy and data protection in sensor network applications;
  7. Secure key management and authentication schemes;
  8. Resilience and fault tolerance in wireless sensor networks;
  9. Case studies and real-world implementations of secure sensor networks;
  10. Emerging trends and future directions in sensor network security.

Prof. Dr. Tony Jan
Dr. Ammar Alazab
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 (6 papers)

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Research

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16 pages, 1482 KiB  
Article
SecureVision: Advanced Cybersecurity Deepfake Detection with Big Data Analytics
by Naresh Kumar and Ankit Kundu
Sensors 2024, 24(19), 6300; https://doi.org/10.3390/s24196300 - 29 Sep 2024
Viewed by 1899
Abstract
SecureVision is an advanced and trustworthy deepfake detection system created to tackle the growing threat of ‘deepfake’ movies that tamper with media, undermine public trust, and jeopardize cybersecurity. We present a novel approach that combines big data analytics with state-of-the-art deep learning algorithms [...] Read more.
SecureVision is an advanced and trustworthy deepfake detection system created to tackle the growing threat of ‘deepfake’ movies that tamper with media, undermine public trust, and jeopardize cybersecurity. We present a novel approach that combines big data analytics with state-of-the-art deep learning algorithms to detect altered information in both audio and visual domains. One of SecureVision’s primary innovations is the use of multi-modal analysis, which improves detection capabilities by concurrently analyzing many media forms and strengthening resistance against advanced deepfake techniques. The system’s efficacy is further enhanced by its capacity to manage large datasets and integrate self-supervised learning, which guarantees its flexibility in the ever-changing field of digital deception. In the end, this study helps to protect digital integrity by providing a proactive, scalable, and efficient defense against the ubiquitous threat of deepfakes, thereby establishing a new benchmark for privacy and security measures in the digital era. Full article
(This article belongs to the Special Issue Cybersecurity Attack and Defense in Wireless Sensors Networks)
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23 pages, 4472 KiB  
Article
Adversarial Attacks against Deep-Learning-Based Automatic Dependent Surveillance-Broadcast Unsupervised Anomaly Detection Models in the Context of Air Traffic Management
by Peng Luo, Buhong Wang, Jiwei Tian, Chao Liu and Yong Yang
Sensors 2024, 24(11), 3584; https://doi.org/10.3390/s24113584 - 2 Jun 2024
Viewed by 859
Abstract
Deep learning has shown significant advantages in Automatic Dependent Surveillance-Broadcast (ADS-B) anomaly detection, but it is known for its susceptibility to adversarial examples which make anomaly detection models non-robust. In this study, we propose Time Neighborhood Accumulation Iteration F [...] Read more.
Deep learning has shown significant advantages in Automatic Dependent Surveillance-Broadcast (ADS-B) anomaly detection, but it is known for its susceptibility to adversarial examples which make anomaly detection models non-robust. In this study, we propose Time Neighborhood Accumulation Iteration Fast Gradient Sign Method (TNAI-FGSM) adversarial attacks which fully take into account the temporal correlation of an ADS-B time series, stabilize the update directions of adversarial samples, and escape from poor local optimum during the process of iterating. The experimental results show that TNAI-FGSM adversarial attacks can successfully attack ADS-B anomaly detection models and improve the transferability of ADS-B adversarial examples. Moreover, the TNAI-FGSM is superior to two well-known adversarial attacks called the Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM). To the best of our understanding, we demonstrate, for the first time, the vulnerability of deep-learning-based ADS-B time series unsupervised anomaly detection models to adversarial examples, which is a crucial step in safety-critical and cost-critical Air Traffic Management (ATM). Full article
(This article belongs to the Special Issue Cybersecurity Attack and Defense in Wireless Sensors Networks)
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21 pages, 1616 KiB  
Article
A Novel IDS with a Dynamic Access Control Algorithm to Detect and Defend Intrusion at IoT Nodes
by Moutaz Alazab, Albara Awajan, Hadeel Alazzam, Mohammad Wedyan, Bandar Alshawi and Ryan Alturki
Sensors 2024, 24(7), 2188; https://doi.org/10.3390/s24072188 - 29 Mar 2024
Cited by 2 | Viewed by 1357
Abstract
The Internet of Things (IoT) is the underlying technology that has enabled connecting daily apparatus to the Internet and enjoying the facilities of smart services. IoT marketing is experiencing an impressive 16.7% growth rate and is a nearly USD 300.3 billion market. These [...] Read more.
The Internet of Things (IoT) is the underlying technology that has enabled connecting daily apparatus to the Internet and enjoying the facilities of smart services. IoT marketing is experiencing an impressive 16.7% growth rate and is a nearly USD 300.3 billion market. These eye-catching figures have made it an attractive playground for cybercriminals. IoT devices are built using resource-constrained architecture to offer compact sizes and competitive prices. As a result, integrating sophisticated cybersecurity features is beyond the scope of the computational capabilities of IoT. All of these have contributed to a surge in IoT intrusion. This paper presents an LSTM-based Intrusion Detection System (IDS) with a Dynamic Access Control (DAC) algorithm that not only detects but also defends against intrusion. This novel approach has achieved an impressive 97.16% validation accuracy. Unlike most of the IDSs, the model of the proposed IDS has been selected and optimized through mathematical analysis. Additionally, it boasts the ability to identify a wider range of threats (14 to be exact) compared to other IDS solutions, translating to enhanced security. Furthermore, it has been fine-tuned to strike a balance between accurately flagging threats and minimizing false alarms. Its impressive performance metrics (precision, recall, and F1 score all hovering around 97%) showcase the potential of this innovative IDS to elevate IoT security. The proposed IDS boasts an impressive detection rate, exceeding 98%. This high accuracy instills confidence in its reliability. Furthermore, its lightning-fast response time, averaging under 1.2 s, positions it among the fastest intrusion detection systems available. Full article
(This article belongs to the Special Issue Cybersecurity Attack and Defense in Wireless Sensors Networks)
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23 pages, 1386 KiB  
Article
GBDT-IL: Incremental Learning of Gradient Boosting Decision Trees to Detect Botnets in Internet of Things
by Ruidong Chen, Tianci Dai, Yanfeng Zhang, Yukun Zhu, Xin Liu and Erfan Zhao
Sensors 2024, 24(7), 2083; https://doi.org/10.3390/s24072083 - 25 Mar 2024
Viewed by 4039
Abstract
The rapid development of the Internet of Things (IoT) has brought many conveniences to our daily life. However, it has also introduced various security risks that need to be addressed. The proliferation of IoT botnets is one of these risks. Most of researchers [...] Read more.
The rapid development of the Internet of Things (IoT) has brought many conveniences to our daily life. However, it has also introduced various security risks that need to be addressed. The proliferation of IoT botnets is one of these risks. Most of researchers have had some success in IoT botnet detection using artificial intelligence (AI). However, they have not considered the impact of dynamic network data streams on the models in real-world environments. Over time, existing detection models struggle to cope with evolving botnets. To address this challenge, we propose an incremental learning approach based on Gradient Boosting Decision Trees (GBDT), called GBDT-IL, for detecting botnet traffic in IoT environments. It improves the robustness of the framework by adapting to dynamic IoT data using incremental learning. Additionally, it incorporates an enhanced Fisher Score feature selection algorithm, which enables the model to achieve a high accuracy even with a smaller set of optimal features, thereby reducing the system resources required for model training. To evaluate the effectiveness of our approach, we conducted experiments on the BoT-IoT, N-BaIoT, MedBIoT, and MQTTSet datasets. We compared our method with similar feature selection algorithms and existing concept drift detection algorithms. The experimental results demonstrated that our method achieved an average accuracy of 99.81% using only 25 features, outperforming similar feature selection algorithms. Furthermore, our method achieved an average accuracy of 96.88% in the presence of different types of drifting data, which is 2.98% higher than the best available concept drift detection algorithms, while maintaining a low average false positive rate of 3.02%. Full article
(This article belongs to the Special Issue Cybersecurity Attack and Defense in Wireless Sensors Networks)
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17 pages, 4773 KiB  
Article
Sinkhole Attack Defense Strategy Integrating SPA and Jaya Algorithms in Wireless Sensor Networks
by Zhijun Teng, Mingzhe Li, Libo Yu, Jinliang Gu and Meng Li
Sensors 2023, 23(24), 9709; https://doi.org/10.3390/s23249709 - 8 Dec 2023
Cited by 1 | Viewed by 1083
Abstract
A sinkhole attack is characterized by low difficulty to launch, high destructive power, and difficulty to detect and defend. It is a common attack mode for wireless sensor networks. This paper proposes a sinkhole attack detection and defense strategy integrating SPA and Jaya [...] Read more.
A sinkhole attack is characterized by low difficulty to launch, high destructive power, and difficulty to detect and defend. It is a common attack mode for wireless sensor networks. This paper proposes a sinkhole attack detection and defense strategy integrating SPA and Jaya algorithms in wireless sensor networks (WSNs). Then, combined with the SPA trust model, the trust values of suspicious nodes were calculated, and the attack nodes were detected. The Jaya algorithm was adopted to avoid the attacked area so that nodes can find the route to communicate with the real Sink, and attack nodes are isolated in the network to improve the capabilities of network directional defense. The simulation results show that the improved detection algorithm can effectively detect malicious nodes in the network, and the defense strategy implemented in the attacked area can improve the packet delivery rate, reduce network delay and energy consumption, and improve the security and reliability of wireless sensor networks. Full article
(This article belongs to the Special Issue Cybersecurity Attack and Defense in Wireless Sensors Networks)
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Review

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45 pages, 7383 KiB  
Review
A Survey on Satellite Communication System Security
by Minjae Kang, Sungbin Park and Yeonjoon Lee
Sensors 2024, 24(9), 2897; https://doi.org/10.3390/s24092897 - 1 May 2024
Cited by 3 | Viewed by 4503
Abstract
In recent years, satellite communication systems (SCSs) have rapidly developed in terms of their role and capabilities, promoted by advancements in space launch technologies. However, this rapid development has also led to the emergence of significant security vulnerabilities, demonstrated through real-world targeted attacks [...] Read more.
In recent years, satellite communication systems (SCSs) have rapidly developed in terms of their role and capabilities, promoted by advancements in space launch technologies. However, this rapid development has also led to the emergence of significant security vulnerabilities, demonstrated through real-world targeted attacks such as AcidRain and AcidPour that demand immediate attention from the security community. In response, various countermeasures, encompassing both technological and policy-based approaches, have been proposed to mitigate these threats. However, the multitude and diversity of these proposals make their comparison complex, requiring a systemized view of the landscape. In this paper, we systematically categorize and analyze both attacks and defenses within the framework of confidentiality, integrity, and availability, focusing on specific threats that pose substantial risks to SCSs. Furthermore, we evaluate existing countermeasures against potential threats in SCS environments and offer insights into the security policies of different nations, recognizing the strategic importance of satellite communications as a national asset. Finally, we present prospective security challenges and solutions for future SCSs, including full quantum communication, AI-integrated SCSs, and standardized protocols for the next generation of terrestrial–space communication. Full article
(This article belongs to the Special Issue Cybersecurity Attack and Defense in Wireless Sensors Networks)
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