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Electronics
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Security and Trust in Internet of Things and Edge Computing
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Security and Trust in Internet of Things and Edge Computing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (16 September 2024) | Viewed by 5558

Special Issue Editor

Prof. Dr. Domenico Rosaci

E-Mail Website
Guest Editor
DIIES, University Mediterranea of Reggio Calabria, 89122 Reggio Calabria, Italy
Interests: trust and reputation systems; Internet of Things; distributed artificial intelligence; artificial neural network; multiagent systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues:

The Internet of Things (IoT) is acquiring increasing importance in our daily lives. Its purpose to change the world by designing smart environments is realized by adaptive forms of cooperation among smart objects (SOs) that can collect and exchange a high quantity of data through network infrastructures, generally connecting millions of IoT nodes. This scenario generates a massive quantity of data to be processed, engendering several computational problems. Formerly, several cloud-based environments were realized for allowing access to IoT devices and providing them with communication, computational, and storage resources. This solution prevents the consumption of valuable resources from carrying out such activities locally. For example, in the Cloud of Things, data are stored and processed on the cloud and the results are sent to the IoT layer (Smart Objects). Unfortunately, the adoption of this architecture generates overheads as network latencies, which represent a very critical issue for IoT applications that are usually time sensitive. To solve the issues above, edge computing allows computational and communication overhead to be shifted from SOs, having limited power and computational resources, to edge servers provided with significant resources and nearby the SOs. This way, edge computing can relieve the peak in traffic flows, mitigate the bandwidth requirements, reduce the transmission latency during data computing or storage in IoT activities, and increase the IoT network lifetime and effectiveness. In such an edge computing IoT scenario, potentially heterogeneous SOs can cooperate with well-equipped SOs placed in their proximity to consume/produce services and/or extract/exchange knowledge.

However, trusting inappropriate counterparts can expose SOs to several potential threats due to malicious, fraudulent, and/or disliked behaviors. Risks can significantly increase in the presence of open and heterogeneous environments and/or when the involved relationships include fee payments or other valuable benefits. We argue that a certain level of confidence and mutual trustworthiness is fundamental for motivating the sharing actors to interact on the basis of a reasonable hope to be engaged in fulfilling interactions. Conversely, a poor level of confidence can compromise the possibility of choosing a reliable partner. To mitigate the risks due to unreliable partners, security and trust systems can be adopted to create a confident atmosphere. If security systems can guarantee that some crucial activities (e.g., payments) are executed correctly, trust and reputation systems are capable to provide a measure regarding the expectation that a trustor has to receive benefits from a trustee by taking into account direct or indirect information about past behaviors or events.

This Special Issue of Electronics aims to present papers in the domain of Security and Trust in IoT and Cloud Computing, including system architectures, models of trust and reputation, computational techniques, standards, and applications. We invite researchers to solicit novel and innovative research papers or insightful review papers.

The topics of interest include, but are not limited to, the following:

  • Security solutions in IoT application domains (including but not limited to smart cities, industry 4.0, smart factories, intelligent transportation systems, digital healthcare, supply chain, etc.);
  • Trust models, protocols and algorithms, and approaches for IoT systems;
  • Security mechanisms for embedded IoT devices (malware protection, firmware security, OS hardening, secure software development, root-of-trust establishment, runtime integrity verification, remote attestation, and secure update mechanisms);
  • Case studies of real security incidents related to IoT systems and applications;
  • Approaches to guaranteeing security in Edge Computing (including but not limited to authentication and access control, availability and auditing, data security and privacy, formal methods, key management, lightweight cryptography, malware detection, protocol security for Edge Computing etc.);
  • Trust management of edge system;
  • Vulnerability analysis for Edge Computing;
  • Emerging trends and new directions in security and trust in IoT and edge computing.

Dr. Domenico Rosaci
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. Electronics 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

  • trust and reputation
  • security and privacy
  • IoT
  • edge computing

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Published Papers (6 papers)

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Research

16 pages, 1806 KiB  
Article
Dynamic Client Clustering, Bandwidth Allocation, and Workload Optimization for Semi-Synchronous Federated Learning
by Liangkun Yu, Xiang Sun, Rana Albelaihi, Chaeeun Park and Sihua Shao
Electronics 2024, 13(23), 4585; https://doi.org/10.3390/electronics13234585 - 21 Nov 2024
Viewed by 319
Abstract
Federated Learning (FL) revolutionizes collaborative machine learning among Internet of Things (IoT) devices by enabling them to train models collectively while preserving data privacy. FL algorithms fall into two primary categories: synchronous and asynchronous. While synchronous FL efficiently handles straggler devices, its convergence [...] Read more.
Federated Learning (FL) revolutionizes collaborative machine learning among Internet of Things (IoT) devices by enabling them to train models collectively while preserving data privacy. FL algorithms fall into two primary categories: synchronous and asynchronous. While synchronous FL efficiently handles straggler devices, its convergence speed and model accuracy can be compromised. In contrast, asynchronous FL allows all devices to participate but incurs high communication overhead and potential model staleness. To overcome these limitations, the paper introduces a semi-synchronous FL framework that uses client tiering based on computing and communication latencies. Clients in different tiers upload their local models at distinct frequencies, striking a balance between straggler mitigation and communication costs. Building on this, the paper proposes the Dynamic client clustering, bandwidth allocation, and local training for semi-synchronous Federated learning (DecantFed) algorithm to dynamically optimize client clustering, bandwidth allocation, and local training workloads in order to maximize data sample processing rates in FL. DecantFed dynamically optimizes client clustering, bandwidth allocation, and local training workloads for maximizing data processing rates in FL. It also adapts client learning rates according to their tiers, thus addressing the model staleness issue. Extensive simulations using benchmark datasets like MNIST and CIFAR-10, under both IID and non-IID scenarios, demonstrate DecantFed’s superior performance. It outperforms FedAvg and FedProx in convergence speed and delivers at least a 28% improvement in model accuracy, compared to FedProx. Full article
(This article belongs to the Special Issue Security and Trust in Internet of Things and Edge Computing)
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37 pages, 1076 KiB  
Article
Distributed Ledger-Based Authentication and Authorization of IoT Devices in Federated Environments
by Michał Jarosz, Konrad Wrona and Zbigniew Zieliński
Electronics 2024, 13(19), 3932; https://doi.org/10.3390/electronics13193932 - 4 Oct 2024
Viewed by 786
Abstract
One of the main security challenges when federating separate Internet of Things (IoT) administrative domains is effective Identity and Access Management, which is required to establish trust and secure communication between federated IoT devices. The primary goal of the work is to develop [...] Read more.
One of the main security challenges when federating separate Internet of Things (IoT) administrative domains is effective Identity and Access Management, which is required to establish trust and secure communication between federated IoT devices. The primary goal of the work is to develop a “lightweight” protocol to enable authentication and authorization of IoT devices in federated environments and ensure the secure communication of IoT devices. We propose a novel Lightweight Authentication and Authorization Framework for Federated IoT (LAAFFI) which takes advantage of the unique fingerprint of IoT devices based on their configuration and additional hardware modules, such as Physical Unclonable Function, to provide flexible authentication and authorization based on Distributed Ledger technology. Moreover, LAAFFI supports IoT devices with limited computing resources and devices not equipped with secure storage space. We implemented a prototype of LAAFFI and evaluated its performance in the Hyperledger Fabric-based IoT framework. Three main metrics were evaluated: latency, throughput (number of operations or transactions per second), and network resource utilization rate (transmission overhead introduced by the LAAFFI protocol). The performance tests conducted confirmed the high efficiency and suitability of the protocol for federated IoT environments. Also, all LAAFFI components are scalable as confirmed by tests. We formally evaluated LAAFFI security using Verifpal as a formal verification tool. Based on the models developed for Verifpal, we validated their security properties, such as message secrecy, authenticity, and freshness. Our results show that the proposed solution can improve the security of federated IoT environments while providing zero-day interoperability and high scalability. Compared to existing solutions, LAAFFI is more efficient due to the use of symmetric cryptography and algorithms adapted for operations involving IoT devices. LAAFFI supports multiple authorization mechanisms, and since it also offers authentication and accountability, it meets the requirements of Authentication, Authorization and Accounting (AAA). It uses Distributed Ledger (DL) and smart contracts to ensure that the request complies with the policies agreed between the organizations. LAAFFI offers authentication of devices belonging to a single organization and different organizations, with the assurance that the encryption key will be shared with another device only if the appropriate security policy is met. The proposed protocol is particularly useful for ensuring the security of federated IoT environments created ad hoc for special missions, e.g., operations conducted by NATO countries and disaster relief operations Humanitarian Assistance and Disaster Relief (HADR) involving military forces and civilian services, where immediate interoperability is required. Full article
(This article belongs to the Special Issue Security and Trust in Internet of Things and Edge Computing)
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11 pages, 286 KiB  
Article
Applying Trust Patterns to Model Complex Trustworthiness in the Internet of Things
by Fabrizio Messina, Domenico Rosaci and Giuseppe M. L. Sarnè
Electronics 2024, 13(11), 2107; https://doi.org/10.3390/electronics13112107 - 29 May 2024
Viewed by 566
Abstract
Key aspects of communities of the Internet of Things (IoT) smart objects presenting social aspects are represented by trust and reputation relationships between the objects. Several trustworthiness models have been presented in the literature in the context of multi-smart object community that could [...] Read more.
Key aspects of communities of the Internet of Things (IoT) smart objects presenting social aspects are represented by trust and reputation relationships between the objects. Several trustworthiness models have been presented in the literature in the context of multi-smart object community that could be adopted in the IoT scenario; however, most of these approaches represent the different dimensions of trust using scalar measures, then integrating these measures in a global trustworthiness value. In this paper, we discuss the limitation of this approach in the IoT context, highlighting the necessity of modeling complex trust relationships that cannot be captured by a vector-based model, and we propose a new trust model in which the trust perceived by an object with respect to another object is modeled by a directed, weighted graph whose vertices are trust dimensions and whose arcs represent relationships between trust dimensions. By using this new model, we provide the IoT community with the possibility of representing also situations in which an object does not know a trust dimension, e.g., reliability, but it is able to derive it from another one, e.g., honesty. The introduced model can represent any trust structure of the type illustrated above, in which several trust dimensions are mutually dependent. Full article
(This article belongs to the Special Issue Security and Trust in Internet of Things and Edge Computing)
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17 pages, 2698 KiB  
Article
A Knowledge Graph Completion Algorithm Based on the Fusion of Neighborhood Features and vBiLSTM Encoding for Network Security
by Wenbo Zhang, Mengxuan Wang, Guangjie Han, Yongxin Feng and Xiaobo Tan
Electronics 2024, 13(9), 1661; https://doi.org/10.3390/electronics13091661 - 25 Apr 2024
Cited by 1 | Viewed by 691
Abstract
Knowledge graphs in the field of network security can integrate diverse, heterogeneous, and fragmented network security data, further explore the relationships between data, and provide support for deep analysis. Currently, there is sparse security information in the field of network security knowledge graphs. [...] Read more.
Knowledge graphs in the field of network security can integrate diverse, heterogeneous, and fragmented network security data, further explore the relationships between data, and provide support for deep analysis. Currently, there is sparse security information in the field of network security knowledge graphs. The limited information provided by traditional text encoding models leads to insufficient reasoning ability, greatly restricting the development of this field. Starting from text encoding, this paper first addresses the issue of the inadequate capabilities of traditional models using a deep learning model for assistance. It designs a vBiLSTM model based on a word2vec and BiLSTM combination to process network security texts. By utilizing word vector models to retain semantic information in entities and extract key features to input processed data into BiLSTM networks for extracting higher-level features that better capture and express their deeper meanings, this design significantly enhances understanding and expression capabilities toward complex semantics in long sentences before inputting final feature vectors into the KGC-N model. The KGC-N model uses feature vectors combined with graph structure information to fuse forward and reverse domain features and then utilizes a Transformer decoder to decode predictions and complete missing information within the network security knowledge map. Compared with other models using evaluation metrics such as MR, MRR demonstrates that employing our proposed method effectively improves performance on completion tasks and increases comprehension abilities toward complex relations, thereby enhancing accuracy and efficiency when completing knowledge graphs. Full article
(This article belongs to the Special Issue Security and Trust in Internet of Things and Edge Computing)
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20 pages, 783 KiB  
Article
Enhancing Security and Trust in Internet of Things through Meshtastic Protocol Utilising Low-Range Technology
by Fabrizio Messina, Corrado Santoro and Federico Fausto Santoro
Electronics 2024, 13(6), 1055; https://doi.org/10.3390/electronics13061055 - 12 Mar 2024
Cited by 1 | Viewed by 1580
Abstract
The rapid proliferation of Internet of Things (IoT) devices has raised significant concerns regarding the trustworthiness of IoT devices, which is becoming a crucial aspect of our daily lives. In this paper, we deal with this important aspect by taking into account Meshtastic, [...] Read more.
The rapid proliferation of Internet of Things (IoT) devices has raised significant concerns regarding the trustworthiness of IoT devices, which is becoming a crucial aspect of our daily lives. In this paper, we deal with this important aspect by taking into account Meshtastic, a dynamic mesh networking protocol that offers robustness and adaptability, important characteristics for the dynamic and heterogeneous IoT environment. LoRaWAN (Low-Range Wide Area Network), a low-power, long-range wireless communication standard, introduces energy efficiency and extends the reach of IoT networks, enabling secure communication over extended distances. To improve the trustworthiness of IoT devices, we present an integrated approach that leverages the strengths of Meshstastic’s dynamic mesh networking capabilities and LoRa’s low-power, long-range communication, along with the integration of a reputation model specifically designed for IoT. We evaluated the performance of the proposed solution through several simulations and real-world experiments. The results show that the devices’ measured values of trust reflect the real behaviour of the devices. These findings underscore the viability and applicability of the Meshtastic protocol utilising LoRa technology as a pivotal step towards establishing resilient and trustworthy IoT infrastructures in the face of evolving security challenges. Full article
(This article belongs to the Special Issue Security and Trust in Internet of Things and Edge Computing)
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16 pages, 824 KiB  
Article
A Secure and Efficient Dynamic Analysis Scheme for Genome Data within SGX-Assisted Servers
by Bao Li, Fucai Zhou, Qiang Wang and Da Feng
Electronics 2023, 12(24), 5004; https://doi.org/10.3390/electronics12245004 - 14 Dec 2023
Viewed by 939
Abstract
With the rapid development of the Internet of Things (IoT), more and more user devices access the network and generate large amounts of genome data. These genome data possess significant medical value when researched. However, traditional genome analysis confronts security and efficiency challenges, [...] Read more.
With the rapid development of the Internet of Things (IoT), more and more user devices access the network and generate large amounts of genome data. These genome data possess significant medical value when researched. However, traditional genome analysis confronts security and efficiency challenges, including access pattern leakage, low efficiency, and single analysis methods. Thus, we propose a secure and efficient dynamic analysis scheme for genome data within a Software Guard Extension (SGX)-assisted server, called SEDASGX. Our approach involves designing a secure analysis framework based on SGXs and implementing various analysis methods within the enclave. The access pattern of genome data is always obfuscated during the analysis and update process, ensuring privacy and security. Furthermore, our scheme not only achieves higher analysis efficiency but also enables dynamic updating of genome data. Our results indicate that the SEDASGX analysis method is nearly 2.5 times more efficient than non-SGX methods, significantly enhancing the analysis speed of large-scale genome data. Full article
(This article belongs to the Special Issue Security and Trust in Internet of Things and Edge Computing)
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