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Special Issue "Security and Trustworthiness in Industrial IoT"

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

Deadline for manuscript submissions: 31 August 2022 | Viewed by 5978
Please contact the Guest Editor or the Section Managing Editor at ([email protected]) for any queries.

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Special Issue Editors

Dr. Ethiopia Nigussie
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Guest Editor

Turun yliopisto, Turku, Finland
Interests: Internet of Things; IIoT; cybersecurity for low-power networks; self-aware networked systems; ICT4D
Special Issues, Collections and Topics in MDPI journals

Dr. Habtamu Abie
E-Mail Website

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Guest Editor

Norsk Regnesentral (Norwegian Computing Center, NR), 0373 Oslo, Norway
Interests: adaptive security; cybersecurity; Internet of Things; context-awareness; game theory; WBANs
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Industrial IoT (IIoT) systems are revolutionizing industry and businesses by improving service delivery and increasing productivity. They facilitate innovations, developments and disruptive business models in various sectors. However, IIoTs are subject to a multitude of threats. Cyberattacks may have a catastrophic impact on industrial applications including stolen proprietary information and cause physical damage to production systems. The research community has done much work on improving the security of IoT systems, but industrial scenarios bring further constraints to the security solutions. IIoT applications are often safety critical with timing constraints, thus addressing the security and trustworthiness of IIoT systems requires consideration of several dimensions.

This Special Issue encourages authors to submit research results covering security and trust of IIoT systems. Contributions addressing relevant theoretical and practical aspects as well as state-of-the-art review works are welcomed. The Special Issue topics include, but are not limited to:

IIoT devices and protocols security;
Tailored security solutions for specific IIoT applications;
Intrusion detection and prevention system;
Data security, privacy and trustworthiness;
Security and trust management for fog and edge computing;
Machine learning, deep learning and blockchain based security solutions;
Threat and vulnerability in platforms and protocols;
Threat models;
Adaptive security management;
Security metrics and risks;
Hardware security.

Dr. Ethiopia Nigussie
Guest Editor

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 2400 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

cybersecurity
industrial IoT
trust evaluation
intrusion detection and prevention systems
hardware security
adaptive security

Published Papers (5 papers)

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Research

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Open AccessArticle

Symmetric-Key-Based Authentication among the Nodes in a Wireless Sensor and Actuator Network

and

Sensors 2022, 22(4), 1403; https://doi.org/10.3390/s22041403 - 11 Feb 2022

Viewed by 361

Abstract

To enable today’s industrial automation, a significant number of sensors and actuators are required. In order to obtain trust and isolate faults in the data collected by this network, protection against authenticity fraud and nonrepudiation is essential. In this paper, we propose a very efficient symmetric-key-based security mechanism to establish authentication and nonrepudiation among all the nodes including the gateway in a distributed cooperative network, without communicating additional security parameters to establish different types of session keys. The solution also offers confidentiality and anonymity in case there are no malicious nodes. If at most one of the nodes is compromised, authentication and nonrepudiation still remain valid. Even if more nodes get compromised, the impact is limited. Therefore, the proposed method drastically differs from the classical group key management schemes, where one compromised node completely breaks the system. The proposed method is mainly based on a hash chain with multiple outputs defined at the gateway and shared with the other nodes in the network. Full article

(This article belongs to the Special Issue Security and Trustworthiness in Industrial IoT)

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Review

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Open AccessReview

Revisiting the Feasibility of Public Key Cryptography in Light of IIoT Communications

and

Sensors 2022, 22(7), 2561; https://doi.org/10.3390/s22072561 - 27 Mar 2022

Viewed by 497

Abstract

Digital certificates are regarded as the most secure and scalable way of implementing authentication services in the Internet today. They are used by most popular security protocols, including Transport Layer Security (TLS) and Datagram Transport Layer Security (DTLS). The lifecycle management of digital certificates relies on centralized Certification Authority (CA)-based Public Key Infrastructures (PKIs). However, the implementation of PKIs and certificate lifecycle management procedures in Industrial Internet of Things (IIoT) environments presents some challenges, mainly due to the high resource consumption that they imply and the lack of trust in the centralized CAs. This paper identifies and describes the main challenges to implement certificate-based public key cryptography in IIoT environments and it surveys the alternative approaches proposed so far in the literature to address these challenges. Most proposals rely on the introduction of a Trusted Third Party to aid the IIoT devices in tasks that exceed their capacity. The proposed alternatives are complementary and their application depends on the specific challenge to solve, the application scenario, and the capacities of the involved IIoT devices. This paper revisits all these alternatives in light of industrial communication models, identifying their strengths and weaknesses, and providing an in-depth comparative analysis. Full article

(This article belongs to the Special Issue Security and Trustworthiness in Industrial IoT)

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Open AccessReview

IoT Intrusion Detection Taxonomy, Reference Architecture, and Analyses

Khalid Albulayhi

Abdallah A. Smadi

Frederick T. Sheldon

and

Robert K. Abercrombie

Sensors 2021, 21(19), 6432; https://doi.org/10.3390/s21196432 - 26 Sep 2021

Cited by 6 | Viewed by 1616

Abstract

This paper surveys the deep learning (DL) approaches for intrusion-detection systems (IDSs) in Internet of Things (IoT) and the associated datasets toward identifying gaps, weaknesses, and a neutral reference architecture. A comparative study of IDSs is provided, with a review of anomaly-based IDSs on DL approaches, which include supervised, unsupervised, and hybrid methods. All techniques in these three categories have essentially been used in IoT environments. To date, only a few have been used in the anomaly-based IDS for IoT. For each of these anomaly-based IDSs, the implementation of the four categories of feature(s) extraction, classification, prediction, and regression were evaluated. We studied important performance metrics and benchmark detection rates, including the requisite efficiency of the various methods. Four machine learning algorithms were evaluated for classification purposes: Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and an Artificial Neural Network (ANN). Therefore, we compared each via the Receiver Operating Characteristic (ROC) curve. The study model exhibits promising outcomes for all classes of attacks. The scope of our analysis examines attacks targeting the IoT ecosystem using empirically based, simulation-generated datasets (namely the Bot-IoT and the IoTID20 datasets). Full article

(This article belongs to the Special Issue Security and Trustworthiness in Industrial IoT)

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Other

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Open AccessSystematic Review

A Survey of Crypto Ransomware Attack Detection Methodologies: An Evolving Outlook

Abdullah Alqahtani

and

Frederick T. Sheldon

Sensors 2022, 22(5), 1837; https://doi.org/10.3390/s22051837 - 25 Feb 2022

Viewed by 856

Abstract

Recently, ransomware attacks have been among the major threats that target a wide range of Internet and mobile users throughout the world, especially critical cyber physical systems. Due to its unique characteristics, ransomware has attracted the attention of security professionals and researchers toward achieving safer and higher assurance systems that can effectively detect and prevent such attacks. The state-of-the-art crypto ransomware early detection models rely on specific data acquired during the runtime of an attack’s lifecycle. However, the evasive mechanisms that these attacks employ to avoid detection often nullify the solutions that are currently in place. More effort is needed to keep up with an attacks’ momentum to take the current security defenses to the next level. This survey is devoted to exploring and analyzing the state-of-the-art in ransomware attack detection toward facilitating the research community that endeavors to disrupt this very critical and escalating ransomware problem. The focus is on crypto ransomware as the most prevalent, destructive, and challenging variation. The approaches and open issues pertaining to ransomware detection modeling are reviewed to establish recommendations for future research directions and scope. Full article

(This article belongs to the Special Issue Security and Trustworthiness in Industrial IoT)

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Open AccessPerspective

Cybersecurity in Power Grids: Challenges and Opportunities

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Sensors 2021, 21(18), 6225; https://doi.org/10.3390/s21186225 - 16 Sep 2021

Cited by 4 | Viewed by 1201

Abstract

Increasing volatilities within power transmission and distribution force power grid operators to amplify their use of communication infrastructure to monitor and control their grid. The resulting increase in communication creates a larger attack surface for malicious actors. Indeed, cyber attacks on power grids have already succeeded in causing temporary, large-scale blackouts in the recent past. In this paper, we analyze the communication infrastructure of power grids to derive resulting fundamental challenges of power grids with respect to cybersecurity. Based on these challenges, we identify a broad set of resulting attack vectors and attack scenarios that threaten the security of power grids. To address these challenges, we propose to rely on a defense-in-depth strategy, which encompasses measures for (i) device and application security, (ii) network security, and (iii) physical security, as well as (iv) policies, procedures, and awareness. For each of these categories, we distill and discuss a comprehensive set of state-of-the art approaches, as well as identify further opportunities to strengthen cybersecurity in interconnected power grids. Full article

(This article belongs to the Special Issue Security and Trustworthiness in Industrial IoT)

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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Blockchain-based IoT Edge resource dynamic management techniques that can reliably guarantee IoT Edge resource allocation in distributed AIoT environments
Authors: Yoon-Su Jeong
Affiliation: Department of Information and Communication Convergence Engineering, Daejeon-si 35349, Korea
Abstract: Various research in the AIoT environment have recently been done to process information acquired from IoT devices as big data. However, as the AIoT environment becomes more varied, IoT data processing and verification has become a security issue, and the part of processing IoT data stably is becoming increasingly challenging. Storing IoT data in a dispersed AIoT environment not only makes it difficult to govern data directly, but it also leaves IoT Edge resources vulnerable to natural disasters and other risks (network attacks, manager error handling, service errors, etc.). In this paper, we propose a blockchain-based IoT Edge resource management technique that uses a hierarchical convolutional neural network to assist IoT Edge resource allocation in distributed AIoT environments (CNN). The proposed technique uses the Convolution and Pooling procedures to extract the features of IoT Edge resources layer by layer in order to optimize IoT Edge resource allocation for attack patterns of features. The proposed technique, in particular, successfully validates attack pattern resources by linking each IoT Edge resource with a blockchain-based connection to an IoT Edge resource with an attack pattern with a hash chain. Furthermore, the proposed technique dynamically links IoT Edge resource blocks to reduce resource allocation with attack patterns in dispersed AIoT environments, resulting in fewer errors in asymmetrically hashed IoT resources. According to the results of the performance evaluation, the proposed technique reduced the processing time of IoT Edge resources connected to blockchain by 13.9 % when compared to when they didn't. Furthermore, the verification time of IoT Edge resources was improved by an average of 8.1 % by linking the attribute information of IoT Edge resources to each other according to the probability value of IoT Edge resources, and the verification time of IoT Edge resources was improved by an average of 8.1 %. Furthermore, when IoT Edge resources are linked to blockchain according to grouping size, the suggested technique has resulted in up to 13.5 % improvement in accuracy of IoT Edge resource integrity. When IoT Edge servers validate IoT Edge resources after grouping IoT Edge resources into n, the proposed technique showed an average 10.3 percent decreased integrity verification delay as the blockchain size of IoT Edge resources rose. Finally, the overhead of IoT Edge resources grew by 14.2 % on average as the number of grouped IoT Edge resources increased.

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