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Security and Privacy in Internet of Things and Wireless Sensor Networks

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

Deadline for manuscript submissions: 20 December 2024 | Viewed by 4848

Special Issue Editors

Prof. Dr. Yong Wang

E-Mail Website
Guest Editor
The Beacom College of Computer and Cyber Sciences, Dakota State University, Madison, SD 57042, USA
Interests: security and privacy issues in wireless networks, mobile devices, cloud and Internet of Things
Dr. Bhaskar Rimal

E-Mail Website
Guest Editor
The Beacom College of Computer and Cyber Sciences, Dakota State University, Madison, SD 57042, USA
Interests: Internet of Things (IoT) security; cyber-physical systems (CPS) security; critical infrastructure security (e.g., smart grids); wireless communications security (5G, 6G); intelligent transportation systems; cloud/edge computing/networking; machine learning and quantum computing

Special Issue Information

Dear Colleagues,

This Special Issue takes a deeper look at security and privacy issues in the Internet of Things (IoT) that are related to machine learning; blockchain; quantum computing; low-power, low-bandwidth protocols such as Bluetooth Low Energy (BLE), ZigBee, and Z-Wave; and machine-to-machine communication protocols such as MQTT and CoAP. Cybersecurity in the IoT faces challenges such as scalability, constraints on IoT devices, lack of standardization, insufficient trust and integrity, hardware/software vulnerabilities and flaws, malware targeting IoT devices, insecure web interfaces, large attack surfaces, privacy issues, and weakest security links. We delve into the real-world implications of cyber-attacks in the IoT and examine the solutions which are related to device identification, access control, intrusion detection, malware analysis, reverse engineering, software exploitation, etc., to secure the IoT. The goal is to provide practical solutions and thought-provoking insights through case studies, surveys, and original research articles, promoting collaboration between stakeholders and addressing the key security and privacy challenges in the IoT.

The topics related to this collection include, but are not limited to:

  • Device fingerprint in the IoT
  • Machine learning-based approaches for device identification
  • Federated learning for privacy and security in the IoT
  • Blockchain-based solutions for security and privacy in the IoT
  • Intrusion detection in the IoT
  • Malware analysis in the IoT
  • Firmware security
  • Third party firmware analysis in the IoT
  • Security for narrowband IoT networks
  • Vulnerability exploitation in the IoT
  • Zero-day attack detection
  • Security and privacy in Bluetooth Low Energy (BLE), ZigBee, and Z-Wave
  • Security and privacy in MQTT and CoAP
  • Lightweight cryptography in the IoT
  • Quantum and post-quantum era in IoT security
  • Security and privacy for IoT-based healthcare systems
  • IoT security and privacy in agriculture
  • Security for software-defined IoT networks
  • Trust management for the IoT
  • Testbed and experimental results in IoT environments
  • Future trends in IoT security and privacy
  • Role of the government and industry in ensuring IoT security
  • Case studies of successful IoT deployments with a focus on security

Prof. Dr. Yong Wang
Dr. Bhaskar Rimal
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.

Keywords

  • Internet of Things
  • security
  • privacy
  • machine learning
  • blockchain
  • post-quantum

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Published Papers (3 papers)

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Research

17 pages, 1974 KiB  
Article
eMIFS: A Normalized Hyperbolic Ransomware Deterrence Model Yielding Greater Accuracy and Overall Performance
by Abdullah Alqahtani and Frederick T. Sheldon
Sensors 2024, 24(6), 1728; https://doi.org/10.3390/s24061728 - 7 Mar 2024
Cited by 2 | Viewed by 972
Abstract
Early detection of ransomware attacks is critical for minimizing the potential damage caused by these malicious attacks. Feature selection plays a significant role in the development of an efficient and accurate ransomware early detection model. In this paper, we propose an enhanced Mutual [...] Read more.
Early detection of ransomware attacks is critical for minimizing the potential damage caused by these malicious attacks. Feature selection plays a significant role in the development of an efficient and accurate ransomware early detection model. In this paper, we propose an enhanced Mutual Information Feature Selection (eMIFS) technique that incorporates a normalized hyperbolic function for ransomware early detection models. The normalized hyperbolic function is utilized to address the challenge of perceiving common characteristics among features, particularly when there are insufficient attack patterns contained in the dataset. The Term Frequency–Inverse Document Frequency (TF–IDF) was used to represent the features in numerical form, making it ready for the feature selection and modeling. By integrating the normalized hyperbolic function, we improve the estimation of redundancy coefficients and effectively adapt the MIFS technique for early ransomware detection, i.e., before encryption takes place. Our proposed method, eMIFS, involves evaluating candidate features individually using the hyperbolic tangent function (tanh), which provides a suitable representation of the features’ relevance and redundancy. Our approach enhances the performance of existing MIFS techniques by considering the individual characteristics of features rather than relying solely on their collective properties. The experimental evaluation of the eMIFS method demonstrates its efficacy in detecting ransomware attacks at an early stage, providing a more robust and accurate ransomware detection model compared to traditional MIFS techniques. Moreover, our results indicate that the integration of the normalized hyperbolic function significantly improves the feature selection process and ultimately enhances ransomware early detection performance. Full article
(This article belongs to the Special Issue Security and Privacy in Internet of Things and Wireless Sensor Networks)
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25 pages, 497 KiB  
Article
Examination of Traditional Botnet Detection on IoT-Based Bots
by Ashley Woodiss-Field, Michael N. Johnstone and Paul Haskell-Dowland
Sensors 2024, 24(3), 1027; https://doi.org/10.3390/s24031027 - 5 Feb 2024
Cited by 3 | Viewed by 1979
Abstract
A botnet is a collection of Internet-connected computers that have been suborned and are controlled externally for malicious purposes. Concomitant with the growth of the Internet of Things (IoT), botnets have been expanding to use IoT devices as their attack vectors. IoT devices [...] Read more.
A botnet is a collection of Internet-connected computers that have been suborned and are controlled externally for malicious purposes. Concomitant with the growth of the Internet of Things (IoT), botnets have been expanding to use IoT devices as their attack vectors. IoT devices utilise specific protocols and network topologies distinct from conventional computers that may render detection techniques ineffective on compromised IoT devices. This paper describes experiments involving the acquisition of several traditional botnet detection techniques, BotMiner, BotProbe, and BotHunter, to evaluate their capabilities when applied to IoT-based botnets. Multiple simulation environments, using internally developed network traffic generation software, were created to test these techniques on traditional and IoT-based networks, with multiple scenarios differentiated by the total number of hosts, the total number of infected hosts, the botnet command and control (CnC) type, and the presence of aberrant activity. Externally acquired datasets were also used to further test and validate the capabilities of each botnet detection technique. The results indicated, contrary to expectations, that BotMiner and BotProbe were able to detect IoT-based botnets—though they exhibited certain limitations specific to their operation. The results show that traditional botnet detection techniques are capable of detecting IoT-based botnets and that the different techniques may offer capabilities that complement one another. Full article
(This article belongs to the Special Issue Security and Privacy in Internet of Things and Wireless Sensor Networks)
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30 pages, 1266 KiB  
Article
A Total Randomized SLP-Preserving Technique with Improved Privacy and Lifetime in WSNs for IoT and the Impact of Radio Range on SLP
by Florence Mukamanzi, Raja Manjula, Raja Datta, Tejodbhav Koduru, Damien Hanyurwimfura and Mukanyiligira Didacienne
Sensors 2023, 23(24), 9623; https://doi.org/10.3390/s23249623 - 5 Dec 2023
Viewed by 1130
Abstract
Enhanced source location privacy and prolonged network lifetime are imperative for WSNs—the skin of IoT. To address these issues, a novel technique named source location privacy with enhanced privacy and network lifetime (SLP-E) is proposed. It employs a reverse random walk followed by [...] Read more.
Enhanced source location privacy and prolonged network lifetime are imperative for WSNs—the skin of IoT. To address these issues, a novel technique named source location privacy with enhanced privacy and network lifetime (SLP-E) is proposed. It employs a reverse random walk followed by a walk on annular rings, to create divergent routing paths in the network, and finally, min-hop routing together with the walk on dynamic rings to send the packets to the base station (BS). The existing random walk-based SLP approaches have either focused on enhancing only privacy at the cost of network lifetime (NLT) or have aimed at improving the amount of privacy without degrading the network lifetime performance. Unlike these schemes, the objectives of the proposed work are to simultaneously improve the safety period and network lifetime along with achieving uniform privacy. This combination of improvements has not been considered so far in a single SLP random walk-based scheme. Additionally, this study investigates for the first time the impact of the sensors’ radio range on both privacy strength and network lifetime metrics in the context of SLP within WSNs. The performance measurements conducted using the proposed analytical models and the simulation results indicate an improvement in the safety period and network lifespan. The safety period in SLP-E increased by 26.5%, 97%, 123%, and 15.7% when compared with SLP-R, SRR, PRLPRW, and PSSLP techniques, respectively. Similarly, the network lifetime of SLP-E increased by 17.36%, 0.2%, 83.41%, and 13.42% when compared with SLP-R, SRR, PRLPRW, and PSSLP techniques, respectively. No matter where a source node is located within a network, the SLP-E provides uniform and improved privacy and network lifetime. Further, the simulation results demonstrate that the sensors’ radio range has an impact on the safety period, capture ratio, and the network lifetime. Full article
(This article belongs to the Special Issue Security and Privacy in Internet of Things and Wireless Sensor Networks)
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