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Security and Privacy of the Internet of Things for Industrial Applications

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

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 27169

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Special Issue Editors

Prof. Dr. Rongxing Lu

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Guest Editor

Faculty of Computer Science, University of New Brunswick, Fredericton, NB E3A 0A1, Canada
Interests: blockchain; distributed concensus; distributed ledger; smart contract; security; privacy, trust, applications
Special Issues, Collections and Topics in MDPI journals

Dr. Sajjad Dadkhah

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Guest Editor

Canadian Institute for Cybersecurity (CIC), Faculty of Computer Science, University of New Brunswick (UNB), Fresericton, NB E3B 5A3, Canada
Interests: multimedia watermarking and security; cyber security; IoT security; security in machine learning techniques

Prof. Dr. Jianting Ning

E-Mail Website
Guest Editor

College of Computer and Cyber Security, Fujian Normal University, Fuzhou 350007, China
Interests: applied cryptography; data security; blockchain security and privacy; privacy-enhancing technology; cloud security; lightweight secure systems; password systems

Dr. Beibei Li

E-Mail Website
Guest Editor

School Of Cyber Science And Engineering, Sichuan University, Chengdu 610207, China
Interests: cyber-physical security; IoT security; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the Industrial Internet of Things (IoTs), which uses IoT devices together with big data and machine learning techniques, has greatly transformed manufacturing and industrial processes, not only improving productivity and analytics, but also transforming workplaces. Nevertheless, the Industrial IoTs still faces many security and privacy challenges. If those challenges cannot be dealt with, the Industrial IoTs cannot reach its final stage of development.

This Special Issue, therefore, aims to collate original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of Industrial IoTs.

Potential topics include, but are not limited, to the following:

Industrial IoT trust, security and privacy;
Trustworthy Industrial IoT data management;
AI-based data analytics for Industrial IoT;
Industrial IoT security protocols;
Cybersecurity for OT/IT convergence;
Digital twin security and privacy in Industrial IoT;
Blockchain technologies in Industrial IoT.

Dr. Rongxing Lu
Dr. Sajjad Dadkhah
Prof. Dr. Jianting Ning
Dr. Beibei Li
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.

Published Papers (7 papers)

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Research

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19 pages, 3167 KiB

Open AccessArticle

A Security Information Transmission Method Based on DHR for Seafloor Observation Network

by Fei Ying, Shengjie Zhao and Jia Wang

Sensors 2024, 24(4), 1147; https://doi.org/10.3390/s24041147 - 09 Feb 2024

Viewed by 811

Abstract

A seafloor observation network (SON) consists of a large number of heterogeneous devices that monitor the deep sea and communicate with onshore data centers. Due to the long-distance information transmission and the risk of malicious attacks, ensuring the integrity of data in transit is essential. A cryptographically secure frame check sequence (FCS) has shown great advantages in protecting data integrity. However, the commonly used FCS has a collision possibility, which poses a security risk; furthermore, reducing the encryption calculation cost is a challenge. In this paper, we propose a secure, lightweight encryption scheme for transmitted data inspired by mimic defense from dynamic heterogeneous redundancy theory. Specifically, we use dynamic keys to encrypt a data block and generate multiple encrypted heterogeneous blocks for transmission. These continuously changing encrypted data blocks increase the confusion regarding the original encoded data, making it challenging for attackers to interpret and modify the data blocks. Additionally, the redundant information from the multiple blocks can identify and recover tampered data. Our proposed scheme is suitable for resource-constrained environments where lightweight encryption is crucial. Through experimental demonstrations and analysis methods, we determine the effectiveness of our encryption scheme in reducing computational costs and improving security performance to protect data integrity. Full article

(This article belongs to the Special Issue Security and Privacy of the Internet of Things for Industrial Applications)

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17 pages, 8433 KiB

Open AccessArticle

Smart Farm Security by Combining IoT Sensor Network and Virtualized Mycelium Network

by Nurdiansyah Sirimorok, Rio Mukhtarom Paweroi, Andi Arniaty Arsyad and Mario Köppen

Sensors 2023, 23(21), 8689; https://doi.org/10.3390/s23218689 - 24 Oct 2023

Cited by 1 | Viewed by 955

Abstract

In today’s world, merging sensor-based security systems with contemporary principles has become crucial. As we witness the ever-growing number of interconnected devices in the Internet of Things (IoT), it is imperative to have robust and trustworthy security measures in place. In this paper, we examine the idea of virtualizing the communication infrastructure for smart farming in the context of IoT. Our approach utilizes a metaverse-based framework that mimics natural processes such as mycelium network growth communication with a security-concept-based srtificial immune system (AIS) and transaction models of a multi-agent system (MAS). The mycelium, a bridge that transfers nutrients from one plant to another, is an underground network (IoT below ground) that can interconnect multiple plants. Our objective is to study and simulate the mycelium’s behavior, which serves as an underground IoT, and we anticipate that the simulation results, supported by diverse aspects, can be a reference for future IoT network development. A proof of concept is presented, demonstrating the capabilities of such a virtualized network for dedicated sensor communication and easy reconfiguration for various needs. Full article

(This article belongs to the Special Issue Security and Privacy of the Internet of Things for Industrial Applications)

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17 pages, 1003 KiB

Open AccessArticle

N-Accesses: A Blockchain-Based Access Control Framework for Secure IoT Data Management

by Teng Hu, Siqi Yang, Yanping Wang, Gongliang Li, Yulong Wang, Gang Wang and Mingyong Yin

Sensors 2023, 23(20), 8535; https://doi.org/10.3390/s23208535 - 18 Oct 2023

Viewed by 1054

Abstract

With the rapid advancement of network communication and big data technologies, the Internet of Things (IoT) has permeated every facet of our lives. Meanwhile, the interconnected IoT devices have generated a substantial volume of data, which possess both economic and strategic value. However, owing to the inherently open nature of IoT environments and the limited capabilities and the distributed deployment of IoT devices, traditional access control methods fall short in addressing the challenges of secure IoT data management. On the one hand, the single point of failure issue is inevitable for the centralized access control schemes. On the other hand, most decentralized access control schemes still face problems such as token underutilization, the insecure distribution of user permissions, and inefficiency.This paper introduces a blockchain-based access control framework to address these challenges. Specifically, the proposed framework enables data owners to host their data and achieves user-defined lightweight data management. Additionally, through the strategic amalgamation of smart contracts and hash-chains, our access control scheme can limit the number of times (i.e., n-times access) a user can access the IoT data before the deadline. This also means that users can utilize their tokens multiple times (predefined by the data owner) within the deadline, thereby improving token utilization while ensuring strict access control. Furthermore, by leveraging the intrinsic characteristics of blockchain, our framework allows data owners to gain capabilities for auditing the access records of their data and verifying them. To empirically validate the effectiveness of our proposed framework and approach, we conducted extensive simulations, and the experimental results demonstrated the feasibility and efficiency of our solution. Full article

(This article belongs to the Special Issue Security and Privacy of the Internet of Things for Industrial Applications)

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16 pages, 4106 KiB

Open AccessArticle

Credit Card Fraud Detection: An Improved Strategy for High Recall Using KNN, LDA, and Linear Regression

by Jiwon Chung and Kyungho Lee

Sensors 2023, 23(18), 7788; https://doi.org/10.3390/s23187788 - 10 Sep 2023

Cited by 3 | Viewed by 4606

Abstract

Efficiently and accurately identifying fraudulent credit card transactions has emerged as a significant global concern along with the growth of electronic commerce and the proliferation of Internet of Things (IoT) devices. In this regard, this paper proposes an improved algorithm for highly sensitive credit card fraud detection. Our approach leverages three machine learning models: K-nearest neighbor, linear discriminant analysis, and linear regression. Subsequently, we apply additional conditional statements, such as “IF” and “THEN”, and operators, such as “>“ and “<“, to the results. The features extracted using this proposed strategy achieved a recall of 1.0000, 0.9701, 1.0000, and 0.9362 across the four tested fraud datasets. Consequently, this methodology outperforms other approaches employing single machine learning models in terms of recall. Full article

(This article belongs to the Special Issue Security and Privacy of the Internet of Things for Industrial Applications)

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26 pages, 2850 KiB

Open AccessArticle

CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment

by Euclides Carlos Pinto Neto, Sajjad Dadkhah, Raphael Ferreira, Alireza Zohourian, Rongxing Lu and Ali A. Ghorbani

Sensors 2023, 23(13), 5941; https://doi.org/10.3390/s23135941 - 26 Jun 2023

Cited by 29 | Viewed by 14449

Abstract

Nowadays, the Internet of Things (IoT) concept plays a pivotal role in society and brings new capabilities to different industries. The number of IoT solutions in areas such as transportation and healthcare is increasing and new services are under development. In the last decade, society has experienced a drastic increase in IoT connections. In fact, IoT connections will increase in the next few years across different areas. Conversely, several challenges still need to be faced to enable efficient and secure operations (e.g., interoperability, security, and standards). Furthermore, although efforts have been made to produce datasets composed of attacks against IoT devices, several possible attacks are not considered. Most existing efforts do not consider an extensive network topology with real IoT devices. The main goal of this research is to propose a novel and extensive IoT attack dataset to foster the development of security analytics applications in real IoT operations. To accomplish this, 33 attacks are executed in an IoT topology composed of 105 devices. These attacks are classified into seven categories, namely DDoS, DoS, Recon, Web-based, brute force, spoofing, and Mirai. Finally, all attacks are executed by malicious IoT devices targeting other IoT devices. The dataset is available on the CIC Dataset website. Full article

(This article belongs to the Special Issue Security and Privacy of the Internet of Things for Industrial Applications)

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18 pages, 7907 KiB

Open AccessArticle

IoT and Deep Learning-Based Farmer Safety System

by Yudhi Adhitya, Grathya Sri Mulyani, Mario Köppen and Jenq-Shiou Leu

Sensors 2023, 23(6), 2951; https://doi.org/10.3390/s23062951 - 08 Mar 2023

Cited by 1 | Viewed by 1641

Abstract

Farming is a fundamental factor driving economic development in most regions of the world. As in agricultural activity, labor has always been hazardous and can result in injury or even death. This perception encourages farmers to use proper tools, receive training, and work in a safe environment. With the wearable device as an Internet of Things (IoT) subsystem, the device can read sensor data as well as compute and send information. We investigated the validation and simulation dataset to determine whether accidents occurred with farmers by applying the Hierarchical Temporal Memory (HTM) classifier with each dataset input from the quaternion feature that represents 3D rotation. The performance metrics analysis showed a significant 88.00% accuracy, precision of 0.99, recall of 0.04, F_Score of 0.09, average Mean Square Error (MSE) of 5.10, Mean Absolute Error (MAE) of 0.19, and a Root Mean Squared Error (RMSE) of 1.51 for the validation dataset, 54.00% accuracy, precision of 0.97, recall of 0.50, F_Score of 0.66, MSE = 0.06, MAE = 3.24, and = 1.51 for the Farming-Pack motion capture (mocap) dataset. The computational framework with wearable device technology connected to ubiquitous systems, as well as statistical results, demonstrate that our proposed method is feasible and effective in solving the problem’s constraints in a time series dataset that is acceptable and usable in a real rural farming environment for optimal solutions. Full article

(This article belongs to the Special Issue Security and Privacy of the Internet of Things for Industrial Applications)

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Review

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29 pages, 1714 KiB

Open AccessReview

A Systematic Review of Data-Driven Attack Detection Trends in IoT

by Safwana Haque, Fadi El-Moussa, Nikos Komninos and Rajarajan Muttukrishnan

Sensors 2023, 23(16), 7191; https://doi.org/10.3390/s23167191 - 15 Aug 2023

Cited by 2 | Viewed by 2419

Abstract

The Internet of Things is perhaps a concept that the world cannot be imagined without today, having become intertwined in our everyday lives in the domestic, corporate and industrial spheres. However, irrespective of the convenience, ease and connectivity provided by the Internet of Things, the security issues and attacks faced by this technological framework are equally alarming and undeniable. In order to address these various security issues, researchers race against evolving technology, trends and attacker expertise. Though much work has been carried out on network security to date, it is still seen to be lagging in the field of Internet of Things networks. This study surveys the latest trends used in security measures for threat detection, primarily focusing on the machine learning and deep learning techniques applied to Internet of Things datasets. It aims to provide an overview of the IoT datasets available today, trends in machine learning and deep learning usage, and the efficiencies of these algorithms on a variety of relevant datasets. The results of this comprehensive survey can serve as a guide and resource for identifying the various datasets, experiments carried out and future research directions in this field. Full article

(This article belongs to the Special Issue Security and Privacy of the Internet of Things for Industrial Applications)

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