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Special Issue "Cybersecurity in the Internet of Things"

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 5512

Special Issue Editors

Prof. Dr. Christos Xenakis
E-Mail Website
Guest Editor
Department of Digital Systems, University of Piraeus, Karaoli and Dimitriou 80, PC 18534 Piraeus, Greece
Interests: network security; authentication; mobile security; IoT security; computer security; smart grid security
Dr. Thanassis Giannetsos
E-Mail Website
Guest Editor
Ubitech Ltd, Digital Security & Trusted Computing Group, Thessalias 8 & Etolias 10, 15232 Chalandri, Athesn, Greece
Interests: trusted computing; applied cryptography; information security; privacy; Internet of Things; secure systems; intrusion detection

Special Issue Information

Dear Colleagues,

With the ever-increasing applications of Internet of Things (IoT) in the wild, there is an abundance of possible vulnerable targets. This IoT revolution has benefited both industrial organizations and multiple vertical sectors, but it has also created unprecedented opportunities for the exploitation of new attack vectors. The aim of this Special Issue is to provide the necessary grounds for discussion and advancement towards enhancing the security and functional safety properties of IoT and their emerging applications. Although most efforts, when it comes to cybersecurity, have the limitation of constrained resources in low-powered devices, the advancement of technology and the paradigm of edge and fog computing have proven that this is not the case anymore, with deployments becoming more decentralized and complex. Advanced critical applications and a wide gamut of mixed-criticality services are deployed in edge devices in increasing frequency, and traditional security should be considered in tandem with IoT security so that solutions deployed in such environments are properly protected. With this in mind, this Special Issue will focus on advanced IoT cybersecurity technologies that not only take into account the per-device security but also their grid-interconnected network as a whole towards establishing “communities of trust”. Its main target is to modernize the approach of cybersecurity in IoT schemas with outreaches to edge and fog computing.

Technical contribution papers, industrial case studies, and review papers are welcome. Topics can include (but are not limited to):

  • Traditional and IoT cybersecurity;
  • Access control, authentication, and authorization;
  • Trust establishment, relationships and propagation, and reputation systems;
  • Attestation technologies and decentralized roots-of-trust;
  • Key management and key recovery;
  • Control-flow binary tracing and RISC-V security;
  • Trusted execution environments and/or TPM security applications in IoT networks;
  • Security in smart grid;
  • Security in edge and fog computing;
  • Usable security and privacy in IoT;
  • Formal verification of security protocols;
  • Data protection in transit and in storage for IoT networks;
  • Blockchain-based cybersecurity applications;
  • Cryptographic trust anchors for secure on- and off-chain knowledge management and data sharing;
  • Malware detection and propagation;
  • Adversarial machine learning;
  • Intrusion detection;
  • Efficient cryptography.

Prof. Dr. Christos Xenakis
Dr. Thanassis Giannetsos
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 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.

Published Papers (6 papers)

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Research

Article
Vulnerabilities of Live-Streaming Services in Korea
Sensors 2022, 22(10), 3766; https://doi.org/10.3390/s22103766 - 15 May 2022
Viewed by 442
Abstract
Recently, the number of users and the demand for live-streaming services have increased. This has exponentially increased the traffic to such services, and live-streaming service platforms in Korea use a grid computing system that distributes traffic to users and reduces traffic loads. However, [...] Read more.
Recently, the number of users and the demand for live-streaming services have increased. This has exponentially increased the traffic to such services, and live-streaming service platforms in Korea use a grid computing system that distributes traffic to users and reduces traffic loads. However, ensuring security with a grid computing system is difficult because the system exchanges general user traffic in a peer-to-peer (P2P) manner instead of receiving data from an authenticated server. Therefore, in this study, to explore the vulnerabilities of a grid computing system, we investigated a vulnerability discovery framework that involves a three-step analysis process and eight detailed activities. Four types of zero-day vulnerabilities, namely video stealing, information disclosure, denial of service, and remote code execution, were derived by analyzing a live-streaming platform in Korea, as a representative service, using grid computing. Full article
(This article belongs to the Special Issue Cybersecurity in the Internet of Things)
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Article
Compact Finite Field Multiplication Processor Structure for Cryptographic Algorithms in IoT Devices with Limited Resources
Sensors 2022, 22(6), 2090; https://doi.org/10.3390/s22062090 - 08 Mar 2022
Viewed by 391
Abstract
The rapid evolution of Internet of Things (IoT) applications, such as e-health and the smart ecosystem, has resulted in the emergence of numerous security flaws. Therefore, security protocols must be implemented among IoT network nodes to resist the majority of the emerging threats. [...] Read more.
The rapid evolution of Internet of Things (IoT) applications, such as e-health and the smart ecosystem, has resulted in the emergence of numerous security flaws. Therefore, security protocols must be implemented among IoT network nodes to resist the majority of the emerging threats. As a result, IoT devices must adopt cryptographic algorithms such as public-key encryption and decryption. The cryptographic algorithms are computationally more complicated to be efficiently implemented on IoT devices due to their limited computing resources. The core operation of most cryptographic algorithms is the finite field multiplication operation, and concise implementation of this operation will have a significant impact on the cryptographic algorithm’s entire implementation. As a result, this paper mainly concentrates on developing a compact and efficient word-based serial-in/serial-out finite field multiplier suitable for usage in IoT devices with limited resources. The proposed multiplier structure is simple to implement in VLSI technology due to its modularity and regularity. The suggested structure is derived from a formal and systematic technique for mapping regular iterative algorithms onto processor arrays. The proposed methodology allows for control of the processor array workload and the workload of each processing element. Managing processor word size allows for control of system latency, area, and consumed energy. The ASIC experimental results indicate that the proposed processor structure reduces area and energy consumption by factors reaching up to 97.7% and 99.2%, respectively. Full article
(This article belongs to the Special Issue Cybersecurity in the Internet of Things)
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Article
A Hybrid Intelligent Framework to Combat Sophisticated Threats in Secure Industries
Sensors 2022, 22(4), 1582; https://doi.org/10.3390/s22041582 - 17 Feb 2022
Cited by 2 | Viewed by 619
Abstract
With the new advancements in Internet of Things (IoT) and its applications in different sectors, such as the industrial sector, by connecting billions of devices and instruments, IoT has evolved as a new paradigm known as the Industrial Internet of Things (IIoT). Nonetheless, [...] Read more.
With the new advancements in Internet of Things (IoT) and its applications in different sectors, such as the industrial sector, by connecting billions of devices and instruments, IoT has evolved as a new paradigm known as the Industrial Internet of Things (IIoT). Nonetheless, its benefits and applications have been approved in different areas, but there are possibilities for various cyberattacks because of its extensive connectivity and diverse nature. Such attacks result in financial loss and data breaches, which urge a consequential need to secure IIoT infrastructure. To combat the threats in the IIoT environment, we proposed a deep-learning SDN-enabled intelligent framework. A hybrid classifier is used for threat detection purposes, i.e., Cu-LSTMGRU + Cu-BLSTM. The proposed model achieved a better detection accuracy with low false-positive rate. We have conducted 10-fold cross-validation to show the unbiasdness of the results. The proposed scheme results are compared with Cu-DNNLSTM and Cu-DNNGRU classifiers, which were tested and trained on the same dataset. We have further compared the proposed model with other existing standard classifiers for a thorough performance evaluation. Results achieved by our proposed scheme are impressive with respect to speed efficiency, F1 score, accuracy, precision, and other evaluation metrics. Full article
(This article belongs to the Special Issue Cybersecurity in the Internet of Things)
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Article
A Trust Management Model for IoT Devices and Services Based on the Multi-Criteria Decision-Making Approach and Deep Long Short-Term Memory Technique
Sensors 2022, 22(2), 634; https://doi.org/10.3390/s22020634 - 14 Jan 2022
Cited by 2 | Viewed by 653
Abstract
Recently, Internet of Things (IoT) technology has emerged in many aspects of life, such as transportation, healthcare, and even education. IoT technology incorporates several tasks to achieve the goals for which it was developed through smart services. These services are intelligent activities that [...] Read more.
Recently, Internet of Things (IoT) technology has emerged in many aspects of life, such as transportation, healthcare, and even education. IoT technology incorporates several tasks to achieve the goals for which it was developed through smart services. These services are intelligent activities that allow devices to interact with the physical world to provide suitable services to users anytime and anywhere. However, the remarkable advancement of this technology has increased the number and the mechanisms of attacks. Attackers often take advantage of the IoTs’ heterogeneity to cause trust problems and manipulate the behavior to delude devices’ reliability and the service provided through it. Consequently, trust is one of the security challenges that threatens IoT smart services. Trust management techniques have been widely used to identify untrusted behavior and isolate untrusted objects over the past few years. However, these techniques still have many limitations like ineffectiveness when dealing with a large amount of data and continuously changing behaviors. Therefore, this paper proposes a model for trust management in IoT devices and services based on the simple multi-attribute rating technique (SMART) and long short-term memory (LSTM) algorithm. The SMART is used for calculating the trust value, while LSTM is used for identifying changes in the behavior based on the trust threshold. The effectiveness of the proposed model is evaluated using accuracy, loss rate, precision, recall, and F-measure on different data samples with different sizes. Comparisons with existing deep learning and machine learning models show superior performance with a different number of iterations. With 100 iterations, the proposed model achieved 99.87% and 99.76% of accuracy and F-measure, respectively. Full article
(This article belongs to the Special Issue Cybersecurity in the Internet of Things)
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Article
Hash-Chain Fog/Edge: A Mode-Based Hash-Chain for Secured Mutual Authentication Protocol Using Zero-Knowledge Proofs in Fog/Edge
Sensors 2022, 22(2), 607; https://doi.org/10.3390/s22020607 - 13 Jan 2022
Viewed by 565
Abstract
Authentication is essential for the prevention of various types of attacks in fog/edge computing. So, a novel mode-based hash chain for secure mutual authentication is necessary to address the Internet of Things (IoT) devices’ vulnerability, as there have been several years of growing [...] Read more.
Authentication is essential for the prevention of various types of attacks in fog/edge computing. So, a novel mode-based hash chain for secure mutual authentication is necessary to address the Internet of Things (IoT) devices’ vulnerability, as there have been several years of growing concerns regarding their security. Therefore, a novel model is designed that is stronger and effective against any kind of unauthorized attack, as IoT devices’ vulnerability is on the rise due to the mass production of IoT devices (embedded processors, camera, sensors, etc.), which ignore the basic security requirements (passwords, secure communication), making them vulnerable and easily accessible. Furthermore, crackable passwords indicate that the security measures taken are insufficient. As per the recent studies, several applications regarding its requirements are the IoT distributed denial of service attack (IDDOS), micro-cloud, secure university, Secure Industry 4.0, secure government, secure country, etc. The problem statement is formulated as the “design and implementation of dynamically interconnecting fog servers and edge devices using the mode-based hash chain for secure mutual authentication protocol”, which is stated to be an NP-complete problem. The hash-chain fog/edge implementation using timestamps, mode-based hash chaining, the zero-knowledge proof property, a distributed database/blockchain, and cryptography techniques can be utilized to establish the connection of smart devices in large numbers securely. The hash-chain fog/edge uses blockchain for identity management only, which is used to store the public keys in distributed ledger form, and all these keys are immutable. In addition, it has no overhead and is highly secure as it performs fewer calculations and requires minimum infrastructure. So, we designed the hash-chain fog/edge (HCFE) protocol, which provides a novel mutual authentication scheme for effective session key agreement (using ZKP properties) with secure protocol communications. The experiment outcomes proved that the hash-chain fog/edge is more efficient at interconnecting various devices and competed favorably in the benchmark comparison. Full article
(This article belongs to the Special Issue Cybersecurity in the Internet of Things)
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Article
Realguard: A Lightweight Network Intrusion Detection System for IoT Gateways
Sensors 2022, 22(2), 432; https://doi.org/10.3390/s22020432 - 07 Jan 2022
Cited by 2 | Viewed by 950
Abstract
Cyber security has become increasingly challenging due to the proliferation of the Internet of things (IoT), where a massive number of tiny, smart devices push trillion bytes of data to the Internet. However, these devices possess various security flaws resulting from the lack [...] Read more.
Cyber security has become increasingly challenging due to the proliferation of the Internet of things (IoT), where a massive number of tiny, smart devices push trillion bytes of data to the Internet. However, these devices possess various security flaws resulting from the lack of defense mechanisms and hardware security support, therefore making them vulnerable to cyber attacks. In addition, IoT gateways provide very limited security features to detect such threats, especially the absence of intrusion detection methods powered by deep learning. Indeed, deep learning models require high computational power that exceeds the capacity of these gateways. In this paper, we introduce Realguard, an DNN-based network intrusion detection system (NIDS) directly operated on local gateways to protect IoT devices within the network. The superiority of our proposal is that it can accurately detect multiple cyber attacks in real time with a small computational footprint. This is achieved by a lightweight feature extraction mechanism and an efficient attack detection model powered by deep neural networks. Our evaluations on practical datasets indicate that Realguard could detect ten types of attacks (e.g., port scan, Botnet, and FTP-Patator) in real time with an average accuracy of 99.57%, whereas the best of our competitors is 98.85%. Furthermore, our proposal effectively operates on resource-constraint gateways (Raspberry PI) at a high packet processing rate reported about 10.600 packets per second. Full article
(This article belongs to the Special Issue Cybersecurity in the Internet of Things)
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