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New Security and Privacy Challenges in Industrial Internet of Things

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (15 August 2022) | Viewed by 18045

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Special Issue Editor

Dr. Pericle Perazzo

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Guest Editor

Department of Information Engineering at University of Pisa, Pisa, Italy
Interests: applied cryptography; Internet of Things; attribute-based encryption; blockchain; post-quantum cryptography; secure routing; secure positioning

Special Issue Information

Dear Colleagues,

The Industrial Internet of Things (IIoT) is a dynamic and emerging trend aimed at employing Internet of Things technologies in industrial processes, especially for automation and data analytics aspects. As an evolution of cyber–physical systems, IIoT systems are nowadays considered as key enablers for the so-called Industry 4.0 revolution. The objective of IIoT is usually to boost competitiveness by providing for ubiquitous connectivity to industrial facilities, but also by improving the decision support systems with better and more comprehensive data analytics. In this sense, IIoT includes notable areas of application such as smart factories, smart logistics, and smart lifecycle management.

Despite their potentials, IIoT deployments are exposed to many security risks at all levels of the communication stack. Moreover, they often treat personal data, for example, data regarding customers or employees, that must be processed according to privacy constraints. This Special Issue of MDPI Journal Applied Sciences aims at attracting original research contributions and SoKs regarding security and privacy challenges and solutions for IIoT environments.

Dr. Pericle Perazzo
Guest Editor

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Keywords

cybersecurity
Industrial Internet of Things
information security
privacy
cyber–physical systems
Industry 4.0

Published Papers (6 papers)

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Research

12 pages, 624 KiB

Open AccessArticle

On the Hardware–Software Integration in Cryptographic Accelerators for Industrial IoT

by Luigi Leonardi, Giuseppe Lettieri, Pericle Perazzo and Sergio Saponara

Appl. Sci. 2022, 12(19), 9948; https://doi.org/10.3390/app12199948 - 3 Oct 2022

Cited by 1 | Viewed by 1582

Abstract

Industrial Internet of Things (IIoT) applies IoT technologies on industrial automation systems with the aims of providing remote sensing, remote control, self-organization and self-maintenance. Since IIoT systems often constitute a critical infrastructure, cybersecurity risks have rapidly increased over the last years. To address cybersecurity requirements, we need to deploy cryptographic processing components which are particularly efficient, considering also that many IIoT systems have real-time constraints. Hardware acceleration can greatly improve the efficiency of cryptographic functions, but the speed-up could be jeopardized by a bad hardware–software integration, which is an aspect often underrated by the literature. Considering that modern IIoT devices often mount an operating system to fulfill their complex tasks, software influence on efficiency cannot be neglected. In this paper, we develop a software–hardware integration of various cryptographic accelerators with a Linux operating system, and we test its performance with two typical IIoT reference applications. We also discuss our design choices and the lessons learned during the development process. Full article

(This article belongs to the Special Issue New Security and Privacy Challenges in Industrial Internet of Things)

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17 pages, 487 KiB

Open AccessArticle

Improved Secure Deep Neural Network Inference Offloading with Privacy-Preserving Scalar Product Evaluation for Edge Computing

by Jiarui Li, Zhuosheng Zhang, Shucheng Yu and Jiawei Yuan

Appl. Sci. 2022, 12(18), 9010; https://doi.org/10.3390/app12189010 - 8 Sep 2022

Cited by 1 | Viewed by 1811

Abstract

Enabling deep learning inferences on resource-constrained devices is important for intelligent Internet of Things. Edge computing makes this feasible by outsourcing resource-consuming operations from IoT devices to edge devices. In such scenarios, sensitive data shall be protected while transmitted to the edge. To address this issue, one major challenge is to efficiently execute inference tasks without hampering the real-time operation of IoT applications. Existing techniques based on complex cryptographic primitives or differential privacy are limited to either efficiency or model accuracy. This paper addresses this challenge with a lightweight interactive protocol by utilizing low-latency IoT-to-edge communication links for computational efficiency. We achieve this with a new privacy-preserving scalar product evaluation technique that caters to the unique requirements of deep learning inference. As compared to the state-of-the-art, our solution offers improved trade-offs among privacy, efficiency, and utility. Experimental results on a Raspberry Pi 4 (Model B) show that our construction can achieve over 14× acceleration versus local execution for AlexNet inference over ImageNet. The proposed privacy-preserving scalar-product-evaluation technique can also be used as a general primitive in other applications. Full article

(This article belongs to the Special Issue New Security and Privacy Challenges in Industrial Internet of Things)

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19 pages, 2522 KiB

Open AccessArticle

Research on a PSO-H-SVM-Based Intrusion Detection Method for Industrial Robotic Arms

by Yulin Zhou, Lun Xie and Hang Pan

Appl. Sci. 2022, 12(6), 2765; https://doi.org/10.3390/app12062765 - 8 Mar 2022

Cited by 7 | Viewed by 1790

Abstract

The automation and intelligence of industrial manufacturing is the core of the fourth industrial revolution, and robotic arms and proprietary networked information systems are an integral part of this vision. However, with the benefits come risks that have been overlooked, and robotic arms have become a heavily attacked area. In order to improve the security of the robotic arm system, this paper proposes an intrusion detection method based on a state classification model. The closure operation process of the robotic arm is divided into five consecutive states, while a support vector machine based on the particle swarm optimization algorithm (PSO-H-SVM) classifies the operation state of the robotic arm. In the detection process, the classifier predicts the operation state of the robotic arm in real time, and the detection method determines whether the state transfer meets the logical requirements, and then determines whether the intrusion occurs. In addition, a response mechanism is proposed on the basis of the intrusion detection system to make protection measures for the robotic arm system. Finally, a physical experiment platform was built to test the intrusion detection method. The results showed that the classification accuracy of the PSO-H-SVM algorithm reached 96.02%, and the detection accuracy of the intrusion detection method reached 90%, which verified the effectiveness and reliability of the intrusion detection method. Full article

(This article belongs to the Special Issue New Security and Privacy Challenges in Industrial Internet of Things)

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20 pages, 7140 KiB

Open AccessArticle

Blockchain Hyperledger with Non-Linear Machine Learning: A Novel and Secure Educational Accreditation Registration and Distributed Ledger Preservation Architecture

by Zaffar Ahmed Shaikh, Abdullah Ayub Khan, Laura Baitenova, Gulmira Zambinova, Natalia Yegina, Natalia Ivolgina, Asif Ali Laghari and Sergey Evgenievich Barykin

Appl. Sci. 2022, 12(5), 2534; https://doi.org/10.3390/app12052534 - 28 Feb 2022

Cited by 26 | Viewed by 3006

Abstract

This paper proposes a novel and secure blockchain hyperledger sawtooth-enabled consortium analytical model for smart educational accreditation credential evaluation. Indeed, candidate academic credentials are generated, verified, and validated by the universities and transmitted to the Higher Education Department (HED). The objective is to enable the procedure of credential verification and analyze tamper-proof forged records before validation. For this reason, we designed and created an accreditation analytical model to investigate individual collected credentials from universities and examine candidates’ records of credibility using machine learning techniques and maintain all these aspects of analysis and addresses in the distributed storage with a secure hash-encryption (SHA-256) blockchain consortium network, which runs on a peer-to-peer (P2P) structure. In this proposed analytical model, we deployed a blockchain distributed mechanism to investigate the examiner and analyst processes of accreditation credential protection and storage criteria, which are referred to as chaincodes or smart contracts. These chaincodes automate the distributed credential schedule, generation, verification, validation, and monitoring of the overall model nodes’ transactions. The chaincodes include candidate registration with the associated university (candidateReg()), certificate-related accreditation credentials update (CIssuanceTrans()), and every node’s transactions preservation in the immutable storage (ULedgerAV()) for further investigations. This model simulates the educational benchmark dataset. The result shows the merit of our model. Through extensive simulations, the blockchain-enabled analytical model provides robust performance in terms of credential management and accreditation credibility problems. Full article

(This article belongs to the Special Issue New Security and Privacy Challenges in Industrial Internet of Things)

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19 pages, 2828 KiB

Open AccessArticle

A Blockchain and Metaheuristic-Enabled Distributed Architecture for Smart Agricultural Analysis and Ledger Preservation Solution: A Collaborative Approach

by Abdullah Ayub Khan, Zaffar Ahmed Shaikh, Larisa Belinskaja, Laura Baitenova, Yulia Vlasova, Zhanneta Gerzelieva, Asif Ali Laghari, Abdul Ahad Abro and Sergey Barykin

Appl. Sci. 2022, 12(3), 1487; https://doi.org/10.3390/app12031487 - 29 Jan 2022

Cited by 32 | Viewed by 3636

Abstract

Distributed forecasting of agriculture commodity prices has an attractive research perspective that delivers active breakthrough analysis of the rapid fluctuations in pricing forecasts for participating stakeholders without being manually dispatched lists. The increased use of an efficient forecasting mechanism for the agriculture information management of generated records and processing creates emerging challenges and limitations. These include new government mandates and regulations, the price of land for expansion, forecasting the growing demand for commodities, fluctuations in the global financial market, food security, and bio-based fuels. Building and deploying distributed dynamic scheduling, management, and monitoring systems of agricultural activities for commodity price forecasting and supply chains require a significant secure and efficient approach. Thus, this paper discusses a collaborative approach where two different folds are demonstrated to cover distinct aspects with different objectives. A metaheuristic-enabled genetic algorithm is designed to receive day-to-day agricultural production details and process and analyze forecast pricing from the records by scheduling, managing, and monitoring them in real-time. The blockchain hyperledger sawtooth distributed modular technology provides a secure communication channel between stakeholders, a private network, protects the forecasting ledger, adds and updates commodity prices, and preserves agricultural information and node transactions in the immutable ledger (IPFS). To accomplish this, we design, develop, and deploy two distinct smart contracts to register the system’s actual stakeholders and allow for the addition of node transactions and exchanges. The second smart contract updates the forecasting commodity pricing ledger and distributes it to participating stakeholders while preserving detailed addresses in storage. The simulation results of the proposed collaborative approach deliver an efficient E-agriculture commodity price forecast with an accuracy of 95.3%. It also maintains ledger transparency, integrity, provenance, availability, and secure operational control and access of agricultural activities. Full article

(This article belongs to the Special Issue New Security and Privacy Challenges in Industrial Internet of Things)

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15 pages, 2294 KiB

Open AccessArticle

Securing SCADA Energy Management System under DDos Attacks Using Token Verification Approach

by Yu-Sheng Yang, Shih-Hsiung Lee, Wei-Che Chen, Chu-Sing Yang, Yuen-Min Huang and Ting-Wei Hou

Appl. Sci. 2022, 12(1), 530; https://doi.org/10.3390/app12010530 - 5 Jan 2022

Cited by 13 | Viewed by 5344

Abstract

The advanced connection requirements of industrial automation and control systems have sparked a new revolution in the Industrial Internet of Things (IIoT), and the Supervisory Control and Data Acquisition (SCADA) network has evolved into an open and highly interconnected network. In addition, the equipment of industrial electronic devices has experienced complete systemic integration by connecting with the SCADA network, and due to the control and monitoring advantages of SCADA, the interconnectivity and working efficiency among systems have been tremendously improved. However, it is inevitable that the SCADA system cannot be separated from the public network, which indicates that there are concerns over cyber-attacks and cyber-threats, as well as information security breaches, in the SCADA network system. According to this context, this paper proposes a module based on the token authentication service to deter attackers from performing distributed denial-of-service (DDoS) attacks. Moreover, a simulated experiment has been conducted in an energy management system in the actual field, and the experimental results have suggested that the security defense architecture proposed by this paper can effectively improve security and is compatible with real field systems. Full article

(This article belongs to the Special Issue New Security and Privacy Challenges in Industrial Internet of Things)

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