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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: closed (31 July 2023) | Viewed by 20977

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Special Issue Editor

Dr. Dongxi Liu

E-Mail Website
Guest Editor

CSIRO Data61, Marsfield, NSW 2122, Australia
Interests: post-quantum cryptography; IoT system security; data security in cloud computing; security protocols and blockchains

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) has been rapidly developed and deployed for some time in various application domains, such as critical infrastructures (power grid, water supply facilities, etc.), agriculture and manufacturing, and healthcare. Current IoT devices are usually integrated vertically with their own application systems, without fully reaching their potential. We anticipate that with further development of IoT, the IoT devices, even from untrusted domains, could interact more with each other, and enable more dynamic IoT applications, making IoT devices, and computing as a service, broaden and deepen the scope of IoT benefits. High interaction of dynamic collection of IoT devices poses new challenges to IoT security and privacy.

Moreover, with the emerging of metaverse, the IoT network is extended with new “things”, such as mixed reality equipment. This extended IoT network incorporates human beings into the immersive cyber space, allowing more complex interaction. It is not clear what security and privacy problems could be in the IoT network extended with metaverse things.

This Special Issue looks for the papers that help understand and solve the problems of security and privacy of IoT in its new stage, as described above. The main topics include, but are not limited to, the following ones:

Security and privacy frameworks for new IoT network;
Security and privacy frameworks for metaverse;
Secure protocols for dynamic IoT networks and metaverse;
Privacy mechanisms for collective IoT devices;
Secure data sharing and integration mechanisms across IoT networks;
Distributed AI/ML and edge computing in IoT network;
Lightweight authentication and access control of federated IoT network;
Novel applications of IoT with security and privacy;
Secure application and application frameworks of metaverse;
Lightweight post-quantum cryptography for IoT networks;
Trust model of IoT devices;
Blockchain for IoT networks;
IoT network architecture and platform facilitating interactions.

Dr. Dongxi Liu
Guest Editor

Manuscript Submission Information

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Keywords

Internet of Things
metaverse
IoT security
data privacy
data sharing and integration
agile IoT application
IoT computing as a service

Published Papers (6 papers)

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Research

22 pages, 2774 KiB

Open AccessArticle

A Holistic Review of Cyber–Physical–Social Systems: New Directions and Opportunities

by Theresa Sobb, Benjamin Turnbull and Nour Moustafa

Sensors 2023, 23(17), 7391; https://doi.org/10.3390/s23177391 - 24 Aug 2023

Cited by 2 | Viewed by 1950

Abstract

A Cyber–Physical–Social System (CPSS) is an evolving subset of Cyber–Physical Systems (CPS), which involve the interlinking of the cyber, physical, and social domains within a system-of-systems mindset. CPSS is in a growing state, which combines secure digital technologies with physical systems (e.g., sensors and actuators) and incorporates social aspects (e.g., human interactions and behaviors, and societal norms) to facilitate automated and secure services to end-users and organisations. This paper reviews the field of CPSS, especially in the scope of complexity theory and cyber security to determine its impact on CPS and social media’s influence activities. The significance of CPSS lies in its potential to provide solutions to complex societal problems that are difficult to address through traditional approaches. With the integration of physical, social, and cyber components, CPSS can realize the full potential of IoT, big data analytics, and machine learning, leading to increased efficiency, improved sustainability and better decision making. CPSS presents exciting opportunities for innovation and advancement in multiple domains, improving the quality of life for people around the world. Research challenges to CPSS include the integration of hard and soft system components within all three domains, in addition to sociological metrics, data security, processing optimization and ethical implications. The findings of this paper note key research trends in the fields of CPSS, and recent novel contributions, followed by identified research gaps and future work. Full article

(This article belongs to the Special Issue Security and Privacy for IoT and Metaverse)

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18 pages, 1738 KiB

Open AccessArticle

An Adaptive Simultaneous Multi-Protocol Extension of CRAFT

by Louis Moreau, Emmanuel Conchon and Damien Sauveron

Sensors 2023, 23(8), 4074; https://doi.org/10.3390/s23084074 - 18 Apr 2023

Viewed by 762

Abstract

An exponential number of devices connect to Internet of Things (IoT) networks every year, increasing the available targets for attackers. Protecting such networks and devices against cyberattacks is still a major concern. A proposed solution to increase trust in IoT devices and networks is remote attestation. Remote attestation establishes two categories of devices, verifiers and provers. Provers must send an attestation to verifiers when requested or at regular intervals to maintain trust by proving their integrity. Remote attestation solutions exist within three categories: software, hardware and hybrid attestation. However, these solutions usually have limited use-cases. For instance, hardware mechanisms should be used but cannot be used alone, and software protocols are usually efficient in particular contexts, such as small networks or mobile networks. More recently, frameworks such as CRAFT have been proposed. Such frameworks enable the use of any attestation protocol within any network. However, as these frameworks are still recent, there is still considerable room for improvement. In this paper, we improve CRAFT’s flexibility and security by proposing ASMP (adaptative simultaneous multi-protocol) features. These features fully enable the use of multiple remote attestation protocols for any devices. They also enable devices to seamlessly switch protocols at any time depending on factors such as the environment, context, and neighboring devices. A comprehensive evaluation of these features in a real-world scenario and use-cases demonstrates that they improve CRAFT’s flexibility and security with minimal impact on performance. Full article

(This article belongs to the Special Issue Security and Privacy for IoT and Metaverse)

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18 pages, 1054 KiB

Open AccessArticle

LPWAN Cyber Security Risk Analysis: Building a Secure IQRF Solution

by Mohammed Bouzidi, Ahmed Amro, Yaser Dalveren, Faouzi Alaya Cheikh and Mohammad Derawi

Sensors 2023, 23(4), 2078; https://doi.org/10.3390/s23042078 - 12 Feb 2023

Cited by 1 | Viewed by 1879

Abstract

Low-power wide area network (LPWAN) technologies such as IQRF are becoming increasingly popular for a variety of Internet of Things (IoT) applications, including smart cities, industrial control, and home automation. However, LPWANs are vulnerable to cyber attacks that can disrupt the normal operation of the network or compromise sensitive information. Therefore, analyzing cybersecurity risks before deploying an LPWAN is essential, as it helps identify potential vulnerabilities and threats as well as allowing for proactive measures to be taken to secure the network and protect against potential attacks. In this paper, a security risk analysis of IQRF technology is conducted utilizing the failure mode effects analysis (FMEA) method. The results of this study indicate that the highest risk corresponds to four failure modes, namely compromised end nodes, a compromised coordinator, a compromised gateway and a compromised communication between nodes. Moreover, through this methodology, a qualitative risk evaluation is performed to identify potential security threats in the IQRF network and propose countermeasures to mitigate the risk of cyber attacks on IQRF networks. Full article

(This article belongs to the Special Issue Security and Privacy for IoT and Metaverse)

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17 pages, 3167 KiB

Open AccessArticle

Metaverse in Healthcare Integrated with Explainable AI and Blockchain: Enabling Immersiveness, Ensuring Trust, and Providing Patient Data Security

by Sikandar Ali, Abdullah, Tagne Poupi Theodore Armand, Ali Athar, Ali Hussain, Maisam Ali, Muhammad Yaseen, Moon-Il Joo and Hee-Cheol Kim

Sensors 2023, 23(2), 565; https://doi.org/10.3390/s23020565 - 04 Jan 2023

Cited by 50 | Viewed by 11115

Abstract

Digitization and automation have always had an immense impact on healthcare. It embraces every new and advanced technology. Recently the world has witnessed the prominence of the metaverse which is an emerging technology in digital space. The metaverse has huge potential to provide a plethora of health services seamlessly to patients and medical professionals with an immersive experience. This paper proposes the amalgamation of artificial intelligence and blockchain in the metaverse to provide better, faster, and more secure healthcare facilities in digital space with a realistic experience. Our proposed architecture can be summarized as follows. It consists of three environments, namely the doctor’s environment, the patient’s environment, and the metaverse environment. The doctors and patients interact in a metaverse environment assisted by blockchain technology which ensures the safety, security, and privacy of data. The metaverse environment is the main part of our proposed architecture. The doctors, patients, and nurses enter this environment by registering on the blockchain and they are represented by avatars in the metaverse environment. All the consultation activities between the doctor and the patient will be recorded and the data, i.e., images, speech, text, videos, clinical data, etc., will be gathered, transferred, and stored on the blockchain. These data are used for disease prediction and diagnosis by explainable artificial intelligence (XAI) models. The GradCAM and LIME approaches of XAI provide logical reasoning for the prediction of diseases and ensure trust, explainability, interpretability, and transparency regarding the diagnosis and prediction of diseases. Blockchain technology provides data security for patients while enabling transparency, traceability, and immutability regarding their data. These features of blockchain ensure trust among the patients regarding their data. Consequently, this proposed architecture ensures transparency and trust regarding both the diagnosis of diseases and the data security of the patient. We also explored the building block technologies of the metaverse. Furthermore, we also investigated the advantages and challenges of a metaverse in healthcare. Full article

(This article belongs to the Special Issue Security and Privacy for IoT and Metaverse)

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17 pages, 4441 KiB

Open AccessArticle

Fog-Assisted Deep-Learning-Empowered Intrusion Detection System for RPL-Based Resource-Constrained Smart Industries

by Danish Attique, Hao Wang and Ping Wang

Sensors 2022, 22(23), 9416; https://doi.org/10.3390/s22239416 - 02 Dec 2022

Cited by 5 | Viewed by 1416

Abstract

The Internet of Things (IoT) is a prominent and advanced network communication technology that has familiarized the world with smart industries. The conveniently acquirable nature of IoT makes it susceptible to a diversified range of potential security threats. The literature has brought forth a plethora of solutions for ensuring secure communications in IoT-based smart industries. However, resource-constrained sectors still demand significant attention. We have proposed a fog-assisted deep learning (DL)-empowered intrusion detection system (IDS) for resource-constrained smart industries. The proposed Cuda–deep neural network gated recurrent unit (Cu-DNNGRU) framework was trained on the N-BaIoT dataset and was evaluated on judicious performance metrics, including accuracy, precision, recall, and F1-score. Additionally, the Cu-DNNGRU was empirically investigated alongside state-of-the-art classifiers, including Cu-LSTMDNN, Cu-BLSTM, and Cu-GRU. An extensive performance comparison was also undertaken among the proposed IDS and some outstanding solutions from the literature. The simulation results showed ample strength with respect to the validation of the proposed framework. The proposed Cu-DNNGRU achieved 99.39% accuracy, 99.09% precision, 98.89% recall, and an F1-score of 99.21%. In the performance comparison, the values were substantially higher than those of the benchmarked schemes, as well as competitive security solutions from the literature. Full article

(This article belongs to the Special Issue Security and Privacy for IoT and Metaverse)

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35 pages, 3392 KiB

Open AccessArticle

SEHIDS: Self Evolving Host-Based Intrusion Detection System for IoT Networks

by Mohammed Baz

Sensors 2022, 22(17), 6505; https://doi.org/10.3390/s22176505 - 29 Aug 2022

Cited by 6 | Viewed by 2711

Abstract

The Internet of Things (IoT) offers unprecedented opportunities to access anything from anywhere and at any time. It is, therefore, not surprising that the IoT acts as a paramount infrastructure for most modern and envisaged systems, including but not limited to smart homes, e-health, and intelligent transportation systems. However, the prevalence of IoT networks and the important role they play in various critical aspects of our lives make them a target for various types of advanced cyberattacks: Dyn attack, BrickerBot, Sonic, Smart Deadbolts, and Silex are just a few examples. Motivated by the need to protect IoT networks, this paper proposes SEHIDS: Self Evolving Host-based Intrusion Detection System. The underlying approach of SEHIDS is to equip each IoT node with a simple Artificial Neural Networks (ANN) architecture and a lightweight mechanism through which an IoT device can train this architecture online and evolves it whenever its performance prediction is degraded. By this means, SEHIDS enables each node to generate the ANN architecture required to detect the threats it faces, which makes SEHIDS suitable for the heterogeneity and turbulence of traffic amongst nodes. Moreover, the gradual evolution of the SEHIDS architecture facilitates retaining it to its near-minimal configurations, which saves the resources required to compute, store, and manipulate the model’s parameters and speeds up the convergence of the model to the zero-classification regions. It is noteworthy that SEHIDS specifies the evolving criteria based on the outcomes of the built-in model’s loss function, which is, in turn, facilitates using SEHIDS to develop the two common types of IDS: signature-based and anomaly-based. Where in the signature-based IDS version, a supervised architecture (i.e., multilayer perceptron architecture) is used to classify different types of attacks, while in the anomaly-based IDS version, an unsupervised architecture (i.e., replicator neuronal network) is used to distinguish benign from malicious traffic. Comprehensive assessments for SEHIDS from different perspectives were conducted with three recent datasets containing a variety of cyberattacks targeting IoT networks: BoT-IoT, TON-IOT, and IoTID20. These results of assessments demonstrate that SEHIDS is able to make accurate predictions of 1 True Positive and is suitable for IoT networks with the order of small fractions of the resources of typical IoT devices. Full article

(This article belongs to the Special Issue Security and Privacy for IoT and Metaverse)

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