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Recent Advances in Cybersecurity, IoT Security, and Blockchain Technologies
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Recent Advances in Cybersecurity, IoT Security, and Blockchain Technologies

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

Deadline for manuscript submissions: closed (30 March 2021) | Viewed by 32414

Special Issue Editors

Prof. Dr. Jiankun Hu

E-Mail Website
Guest Editor
School of Engineering and Information technology, University of New South Wales Canberra, Northcott Drive, Canberra, ACT 2610, Australia
Interests: biometrics; security; cybersecurity; bio-cryptography
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hao-Tian Wu

E-Mail Website
Guest Editor
School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China
Interests: cyber security; data hiding in encrypted domain; blockchain; privacy protection
Dr. Chan Yeob Yeun

E-Mail Website
Guest Editor
Center for Cyber-Physical Systems, EECS Department, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates
Interests: cyber security; IoT security; cloud security; security for big data analytics; AI for cyber security

Special Issue Information

Dear Colleagues,

With the boom in applications of Internet of Things (IoT), cyberspace has become pervasive. While this has brought about enormous benefits for businesses and a multitude of opportunities, it has also created pervasive loopholes for cyberattacks. This Special Issue will provide a forum for reporting recent advances in this field. Most issues concerning IoT security and privacy focus on the aspect of computing-constrained resources. While such efforts are needed, the IoT concept itself is not confined to resource-constrained factors. With the developments in hardware miniaturization, the capacity of IoT devices has followed Moore’s law, doubling every two years. Therefore, there is a need to bring general cybersecurity and IoT security works together as the boundaries blur. This Special Issue will provide such a forum to present the latest research works in general cybersecurity and IoT security. Technical contribution papers, industrial case studies, and review papers are welcome. Topics can include (but are not limited to):

  • General cybersecurity
  • Applied cryptography
  • Smart grid security
  • Security and privacy in IoT
  • Blockchain technology and blockchain-based applications
  • Multimedia security including watermarking, data hiding
  • Data forensics including malware detection and propagation
  • Adversarial machine learning
  • Network and host intrusion detection
  • Biometrics security including liveness detection
  • Access control and authentication technologies

Prof. Dr. Jiankun Hu
Prof. Dr. Hao-Tian Wu
Prof. Dr. Chan Yeob Yeun
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 (5 papers)

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Research

18 pages, 1093 KiB  
Article
Proposal of an Economy of Things Architecture and an Approach Comparing Cryptocurrencies
by Bruno Machado Agostinho, Mario Antônio Ribeiro Dantas and Alex Sandro Roschildt Pinto
Sensors 2021, 21(9), 3239; https://doi.org/10.3390/s21093239 - 7 May 2021
Viewed by 2852
Abstract
In the present computational scenario, one can perceive the emergence of cryptocurrencies and the increased utilization of IoT devices, which are pushing to new challenges, opportunities, and behavior changes. It is still not known how these technologies will impact the current business and [...] Read more.
In the present computational scenario, one can perceive the emergence of cryptocurrencies and the increased utilization of IoT devices, which are pushing to new challenges, opportunities, and behavior changes. It is still not known how these technologies will impact the current business and economic models. In this regard, this study proposes an economy of things architecture and an approach comparing several cryptocurrencies. Therefore, the proposed architecture aims to use these new opportunities to enable device-to-device (D2D) interaction based on this novel paradigm, called the Economy of Things (EoT). An experimental environment was conducted to compare characteristics of the cryptocurrencies Ripple, Iota, and Ethereum. The initial results show several interesting differences related to transaction costs, errors, speeds, and threads. Full article
(This article belongs to the Special Issue Recent Advances in Cybersecurity, IoT Security, and Blockchain Technologies)
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24 pages, 1508 KiB  
Article
A Dense Neural Network Approach for Detecting Clone ID Attacks on the RPL Protocol of the IoT
by Carlos D. Morales-Molina, Aldo Hernandez-Suarez, Gabriel Sanchez-Perez, Linda K. Toscano-Medina, Hector Perez-Meana, Jesus Olivares-Mercado, Jose Portillo-Portillo, Victor Sanchez and Luis Javier Garcia-Villalba
Sensors 2021, 21(9), 3173; https://doi.org/10.3390/s21093173 - 3 May 2021
Cited by 24 | Viewed by 3799
Abstract
At present, new data sharing technologies, such as those used in the Internet of Things (IoT) paradigm, are being extensively adopted. For this reason, intelligent security controls have become imperative. According to good practices and security information standards, particularly those regarding security in [...] Read more.
At present, new data sharing technologies, such as those used in the Internet of Things (IoT) paradigm, are being extensively adopted. For this reason, intelligent security controls have become imperative. According to good practices and security information standards, particularly those regarding security in depth, several defensive layers are required to protect information assets. Within the context of IoT cyber-attacks, it is fundamental to continuously adapt new detection mechanisms for growing IoT threats, specifically for those becoming more sophisticated within mesh networks, such as identity theft and cloning. Therefore, current applications, such as Intrusion Detection Systems (IDS), Intrusion Prevention Systems (IPS), and Security Information and Event Management Systems (SIEM), are becoming inadequate for accurately handling novel security incidents, due to their signature-based detection procedures using the matching and flagging of anomalous patterns. This project focuses on a seldom-investigated identity attack—the Clone ID attack—directed at the Routing Protocol for Low Power and Lossy Networks (RPL), the underlying technology for most IoT devices. Hence, a robust Artificial Intelligence-based protection framework is proposed, in order to tackle major identity impersonation attacks, which classical applications are prone to misidentifying. On this basis, unsupervised pre-training techniques are employed to select key characteristics from RPL network samples. Then, a Dense Neural Network (DNN) is trained to maximize deep feature engineering, with the aim of improving classification results to protect against malicious counterfeiting attempts. Full article
(This article belongs to the Special Issue Recent Advances in Cybersecurity, IoT Security, and Blockchain Technologies)
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20 pages, 813 KiB  
Article
UTM-Chain: Blockchain-Based Secure Unmanned Traffic Management for Internet of Drones
by Azza Allouch, Omar Cheikhrouhou, Anis Koubâa, Khalifa Toumi, Mohamed Khalgui and Tuan Nguyen Gia
Sensors 2021, 21(9), 3049; https://doi.org/10.3390/s21093049 - 27 Apr 2021
Cited by 56 | Viewed by 7906
Abstract
Unmanned aerial systems (UAVs) are dramatically evolving and promoting several civil applications. However, they are still prone to many security issues that threaten public safety. Security becomes even more challenging when they are connected to the Internet as their data stream is exposed [...] Read more.
Unmanned aerial systems (UAVs) are dramatically evolving and promoting several civil applications. However, they are still prone to many security issues that threaten public safety. Security becomes even more challenging when they are connected to the Internet as their data stream is exposed to attacks. Unmanned traffic management (UTM) represents one of the most important topics for small unmanned aerial systems for beyond-line-of-sight operations in controlled low-altitude airspace. However, without securing the flight path exchanges between drones and ground stations or control centers, serious security threats may lead to disastrous situations. For example, a predefined flight path could be easily altered to make the drone perform illegal operations. Motivated by these facts, this paper discusses the security issues for UTM’s components and addresses the security requirements for such systems. Moreover, we propose UTM-Chain, a lightweight blockchain-based security solution using hyperledger fabric for UTM of low-altitude UAVs which fits the computational and storage resources limitations of UAVs. Moreover, UTM-Chain provides secure and unalterable traffic data between the UAVs and their ground control stations. The performance of the proposed system related to transaction latency and resource utilization is analyzed by using cAdvisor. Finally, the analysis of security aspects demonstrates that the proposed UTM-Chain scheme is feasible and extensible for the secure sharing of UAV data. Full article
(This article belongs to the Special Issue Recent Advances in Cybersecurity, IoT Security, and Blockchain Technologies)
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21 pages, 1972 KiB  
Article
Integration of Blockchain, IoT and Machine Learning for Multistage Quality Control and Enhancing Security in Smart Manufacturing
by Zeinab Shahbazi and Yung-Cheol Byun
Sensors 2021, 21(4), 1467; https://doi.org/10.3390/s21041467 - 20 Feb 2021
Cited by 103 | Viewed by 9784
Abstract
Smart manufacturing systems are growing based on the various requests for predicting the reliability and quality of equipment. Many machine learning techniques are being examined to that end. Another issue which considers an important part of industry is data security and management. To [...] Read more.
Smart manufacturing systems are growing based on the various requests for predicting the reliability and quality of equipment. Many machine learning techniques are being examined to that end. Another issue which considers an important part of industry is data security and management. To overcome the problems mentioned above, we applied the integrated methods of blockchain and machine learning to secure system transactions and handle a dataset to overcome the fake dataset. To manage and analyze the collected dataset, big data techniques were used. The blockchain system was implemented in the private Hyperledger Fabric platform. Similarly, the fault diagnosis prediction aspect was evaluated based on the hybrid prediction technique. The system’s quality control was evaluated based on non-linear machine learning techniques, which modeled that complex environment and found the true positive rate of the system’s quality control approach. Full article
(This article belongs to the Special Issue Recent Advances in Cybersecurity, IoT Security, and Blockchain Technologies)
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17 pages, 770 KiB  
Article
An Investigation of Insider Threat Mitigation Based on EEG Signal Classification
by Jung Hwan Kim, Chul Min Kim and Man-Sung Yim
Sensors 2020, 20(21), 6365; https://doi.org/10.3390/s20216365 - 8 Nov 2020
Cited by 13 | Viewed by 6398
Abstract
This study proposes a scheme to identify insider threats in nuclear facilities through the detection of malicious intentions of potential insiders using subject-wise classification. Based on electroencephalography (EEG) signals, a classification model was developed to identify whether a subject has a malicious intention [...] Read more.
This study proposes a scheme to identify insider threats in nuclear facilities through the detection of malicious intentions of potential insiders using subject-wise classification. Based on electroencephalography (EEG) signals, a classification model was developed to identify whether a subject has a malicious intention under scenarios of being forced to become an insider threat. The model also distinguishes insider threat scenarios from everyday conflict scenarios. To support model development, 21-channel EEG signals were measured on 25 healthy subjects, and sets of features were extracted from the time, time–frequency, frequency and nonlinear domains. To select the best use of the available features, automatic selection was performed by random-forest-based algorithms. The k-nearest neighbor, support vector machine with radial kernel, naïve Bayes, and multilayer perceptron algorithms were applied for the classification. By using EEG signals obtained while contemplating becoming an insider threat, the subject-wise model identified malicious intentions with 78.57% accuracy. The model also distinguished insider threat scenarios from everyday conflict scenarios with 93.47% accuracy. These findings could be utilized to support the development of insider threat mitigation systems along with existing trustworthiness assessments in the nuclear industry. Full article
(This article belongs to the Special Issue Recent Advances in Cybersecurity, IoT Security, and Blockchain Technologies)
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