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Cybersecurity in the Internet of Things

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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 (25 March 2023) | Viewed by 68795

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Special Issue Editors

Prof. Dr. Christos Xenakis

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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
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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

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Published Papers (14 papers)

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24 pages, 1387 KiB

Open AccessArticle

Role-Driven Clustering of Stakeholders: A Study of IoT Security Improvement

by Latifah Almalki, Amany Alnahdi and Tahani Albalawi

Sensors 2023, 23(12), 5578; https://doi.org/10.3390/s23125578 - 14 Jun 2023

Cited by 1 | Viewed by 2224

Abstract

This study aims to address the challenges of managing the vast amount of data generated by Internet of Things (IoT) devices by categorizing stakeholders based on their roles in IoT security. As the number of connected devices increases, so do the associated security risks, highlighting the need for skilled stakeholders to mitigate these risks and prevent potential attacks. The study proposes a two-part approach, which involves clustering stakeholders according to their responsibilities and identifying relevant features. The main contribution of this research lies in enhancing decision-making processes within IoT security management. The proposed stakeholder categorization provides valuable insights into the diverse roles and responsibilities of stakeholders in IoT ecosystems, enabling a better understanding of their interrelationships. This categorization facilitates more effective decision making by considering the specific context and responsibilities of each stakeholder group. Additionally, the study introduces the concept of weighted decision making, incorporating factors such as role and importance. This approach enhances the decision-making process, enabling stakeholders to make more informed and context-aware decisions in the realm of IoT security management. The insights gained from this research have far-reaching implications. Not only will they benefit stakeholders involved in IoT security, but they will also assist policymakers and regulators in developing effective strategies to address the evolving challenges of IoT security. Full article

(This article belongs to the Special Issue Cybersecurity in the Internet of Things)

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36 pages, 6696 KiB

Open AccessArticle

Hybrid IoT Cyber Range

by Karl Edvard Balto, Muhammad Mudassar Yamin, Andrii Shalaginov and Basel Katt

Sensors 2023, 23(6), 3071; https://doi.org/10.3390/s23063071 - 13 Mar 2023

Cited by 5 | Viewed by 4428

Abstract

The use of IoT devices has increased rapidly in recent times. While the development of new devices is moving quickly, and as prices are being forced down, the costs of developing such devices also needs to be reduced. IoT devices are now trusted with more critical tasks, and it is important that they behave as intended and that the information they process is protected. It is not always the IoT device itself that is the target of a cyber attack, but rather, it can be a tool for another attack. Home consumers, in particular, expect these devices to be easy to use and set up. However, to reduce costs, complexity, and time, security measures are often cut down. To increase awareness and knowledge in IoT security, education, awareness, demonstrations, and training are necessary. Small changes may result in significant security benefits. With increased awareness and knowledge among developers, manufacturers, and users, they can make choices that can improve security. To increase knowledge and awareness in IoT security, a proposed solution is a training ground for IoT security, an IoT cyber range. Cyber ranges have received more attention lately, but not as much in the IoT field, at least not what is publicly available. As the diversity in IoT devices is large with different vendors, architectures, and components and peripherals, it is difficult to find one solution that fits all IoT devices. To some extent, IoT devices can be emulated, but it is not feasible to create emulators for all types of devices. To cover all needs, it is necessary to combine digital emulation with real hardware. A cyber range with this combination is called a hybrid cyber range. This work surveys the requirements for a hybrid IoT cyber range and proposes a design and implementation of a range that fulfills those requirements. Full article

(This article belongs to the Special Issue Cybersecurity in the Internet of Things)

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22 pages, 5162 KiB

Open AccessArticle

Intrusion Detection System for IoT: Analysis of PSD Robustness

by Lamoussa Sanogo, Eric Alata, Alexandru Takacs and Daniela Dragomirescu

Sensors 2023, 23(4), 2353; https://doi.org/10.3390/s23042353 - 20 Feb 2023

Cited by 4 | Viewed by 1795

Abstract

The security of internet of things (IoT) devices remains a major concern. These devices are very vulnerable because of some of their particularities (limited in both their memory and computing power, and available energy) that make it impossible to implement traditional security mechanisms. Consequently, researchers are looking for new security mechanisms adapted to these devices and the networks of which they are part. One of the most promising new approaches is fingerprinting, which aims to identify a given device by associating it with a unique signature built from its unique intrinsic characteristics, i.e., inherent imperfections, introduced by the manufacturing processes of its hardware. However, according to state-of-the-art studies, the main challenge that fingerprinting faces is the nonrelevance of the fingerprinting features extracted from hardware imperfections. Since these hardware imperfections can reflect on the RF signal for a wireless communicating device, in this study, we aim to investigate whether or not the power spectral density (PSD) of a device’s RF signal could be a relevant feature for its fingerprinting, knowing that a relevant fingerprinting feature should remain stable regardless of the environmental conditions, over time and under influence of any other parameters. Through experiments, we were able to identify limits and possibilities of power spectral density (PSD) as a fingerprinting feature. Full article

(This article belongs to the Special Issue Cybersecurity in the Internet of Things)

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21 pages, 1075 KiB

Open AccessArticle

Practical Three-Factor Authentication Protocol Based on Elliptic Curve Cryptography for Industrial Internet of Things

by Xingwen Zhao, Dexin Li and Hui Li

Sensors 2022, 22(19), 7510; https://doi.org/10.3390/s22197510 - 3 Oct 2022

Cited by 22 | Viewed by 2287

Abstract

Because the majority of information in the industrial Internet of things (IIoT) is transmitted over an open and insecure channel, it is indispensable to design practical and secure authentication and key agreement protocols. Considering the weak computational power of sensors, many scholars have designed lightweight authentication protocols that achieve limited security properties. Moreover, these existing protocols are mostly implemented in a single-gateway scenario, whereas the multigateway scenario is not considered. To deal with these problems, this paper presents a novel three-factor authentication and key agreement protocol based on elliptic curve cryptography for IIoT environments. Based on the elliptic curve Diffie–Hellman problem, we present a protocol achieving desirable forward and backward secrecy. The proposed protocol applies to single-gateway and is also extended to multigateway simultaneously. A formal security analysis is described to prove the security of the proposed scheme. Finally, the comparison results demonstrate that our protocol provides more security attributes at a relatively lower computational cost. Full article

(This article belongs to the Special Issue Cybersecurity in the Internet of Things)

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22 pages, 3693 KiB

Open AccessArticle

Towards Robustifying Image Classifiers against the Perils of Adversarial Attacks on Artificial Intelligence Systems

by Theodora Anastasiou, Sophia Karagiorgou, Petros Petrou, Dimitrios Papamartzivanos, Thanassis Giannetsos, Georgia Tsirigotaki and Jelle Keizer

Sensors 2022, 22(18), 6905; https://doi.org/10.3390/s22186905 - 13 Sep 2022

Cited by 5 | Viewed by 3005

Abstract

Adversarial machine learning (AML) is a class of data manipulation techniques that cause alterations in the behavior of artificial intelligence (AI) systems while going unnoticed by humans. These alterations can cause serious vulnerabilities to mission-critical AI-enabled applications. This work introduces an AI architecture augmented with adversarial examples and defense algorithms to safeguard, secure, and make more reliable AI systems. This can be conducted by robustifying deep neural network (DNN) classifiers and explicitly focusing on the specific case of convolutional neural networks (CNNs) used in non-trivial manufacturing environments prone to noise, vibrations, and errors when capturing and transferring data. The proposed architecture enables the imitation of the interplay between the attacker and a defender based on the deployment and cross-evaluation of adversarial and defense strategies. The AI architecture enables (i) the creation and usage of adversarial examples in the training process, which robustify the accuracy of CNNs, (ii) the evaluation of defense algorithms to recover the classifiers’ accuracy, and (iii) the provision of a multiclass discriminator to distinguish and report on non-attacked and attacked data. The experimental results show promising results in a hybrid solution combining the defense algorithms and the multiclass discriminator in an effort to revitalize the attacked base models and robustify the DNN classifiers. The proposed architecture is ratified in the context of a real manufacturing environment utilizing datasets stemming from the actual production lines. Full article

(This article belongs to the Special Issue Cybersecurity in the Internet of Things)

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25 pages, 1007 KiB

Open AccessArticle

Lightweight On-Device Detection of Android Malware Based on the Koodous Platform and Machine Learning

by Mateusz Krzysztoń, Bartosz Bok, Marcin Lew and Andrzej Sikora

Sensors 2022, 22(17), 6562; https://doi.org/10.3390/s22176562 - 31 Aug 2022

Cited by 6 | Viewed by 2813

Abstract

Currently, Android is the most popular operating system among mobile devices. However, as the number of devices with the Android operating system increases, so does the danger of using them. This is especially important as smartphones increasingly authenticate critical activities(e-banking, e-identity). BotSense Mobile is a tool already integrated with some critical applications (e-banking, e-identity) to increase user safety. In this paper, we focus on the novel functionality of BotSense Mobile: the detection of malware applications on a user device. In addition to the standard blacklist approach, we propose a machine learning-based model for unknown malicious application detection. The lightweight neural network model is deployed on an edge device to avoid sending sensitive user data outside the device. For the same reason, manifest-related features can be used by the detector only. We present a comprehensive empirical analysis of malware detection conducted on recent data (May–June, 2022) from the Koodous platform, which is a collaborative platform where over 70 million Android applications were collected. The research highlighted the problem of machine learning model aging. We evaluated the lightweight model on recent Koodous data and obtained

f_{1} = 0.77

and high precision (

0.9

). Full article

(This article belongs to the Special Issue Cybersecurity in the Internet of Things)

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26 pages, 9940 KiB

Open AccessArticle

Vulnerabilities of Live-Streaming Services in Korea

by Sun-Hong Hwang, Ga-Yeong Kim, Su-Hwan Myeong, Tai-Sic Yun, Seung-Min Yoon, Tai-Ho Kim and Ieck-Chae Euom

Sensors 2022, 22(10), 3766; https://doi.org/10.3390/s22103766 - 15 May 2022

Cited by 2 | Viewed by 2901

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, 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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17 pages, 438 KiB

Open AccessArticle

Compact Finite Field Multiplication Processor Structure for Cryptographic Algorithms in IoT Devices with Limited Resources

by Atef Ibrahim and Fayez Gebali

Sensors 2022, 22(6), 2090; https://doi.org/10.3390/s22062090 - 8 Mar 2022

Cited by 2 | Viewed by 2170

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. 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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16 pages, 5591 KiB

Open AccessArticle

A Hybrid Intelligent Framework to Combat Sophisticated Threats in Secure Industries

by Danish Javeed, Tianhan Gao, Muhammad Taimoor Khan and Duaa Shoukat

Sensors 2022, 22(4), 1582; https://doi.org/10.3390/s22041582 - 17 Feb 2022

Cited by 34 | Viewed by 3179

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, 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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27 pages, 5767 KiB

Open AccessArticle

A Trust Management Model for IoT Devices and Services Based on the Multi-Criteria Decision-Making Approach and Deep Long Short-Term Memory Technique

by Yara Alghofaili and Murad A. Rassam

Sensors 2022, 22(2), 634; https://doi.org/10.3390/s22020634 - 14 Jan 2022

Cited by 53 | Viewed by 12125

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 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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27 pages, 1420 KiB

Open AccessArticle

Hash-Chain Fog/Edge: A Mode-Based Hash-Chain for Secured Mutual Authentication Protocol Using Zero-Knowledge Proofs in Fog/Edge

by Mayuresh Sunil Pardeshi, Ruey-Kai Sheu and Shyan-Ming Yuan

Sensors 2022, 22(2), 607; https://doi.org/10.3390/s22020607 - 13 Jan 2022

Cited by 9 | Viewed by 3465

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 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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18 pages, 794 KiB

Open AccessArticle

Realguard: A Lightweight Network Intrusion Detection System for IoT Gateways

by Xuan-Ha Nguyen, Xuan-Duong Nguyen, Hoang-Hai Huynh and Kim-Hung Le

Sensors 2022, 22(2), 432; https://doi.org/10.3390/s22020432 - 7 Jan 2022

Cited by 90 | Viewed by 8877

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 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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24 pages, 672 KiB

Open AccessEditor’s ChoiceReview

A Survey of Machine and Deep Learning Methods for Privacy Protection in the Internet of Things

by Eva Rodríguez, Beatriz Otero and Ramon Canal

Sensors 2023, 23(3), 1252; https://doi.org/10.3390/s23031252 - 21 Jan 2023

Cited by 40 | Viewed by 8606

Abstract

Recent advances in hardware and information technology have accelerated the proliferation of smart and interconnected devices facilitating the rapid development of the Internet of Things (IoT). IoT applications and services are widely adopted in environments such as smart cities, smart industry, autonomous vehicles, and eHealth. As such, IoT devices are ubiquitously connected, transferring sensitive and personal data without requiring human interaction. Consequently, it is crucial to preserve data privacy. This paper presents a comprehensive survey of recent Machine Learning (ML)- and Deep Learning (DL)-based solutions for privacy in IoT. First, we present an in depth analysis of current privacy threats and attacks. Then, for each ML architecture proposed, we present the implementations, details, and the published results. Finally, we identify the most effective solutions for the different threats and attacks. Full article

(This article belongs to the Special Issue Cybersecurity in the Internet of Things)

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38 pages, 1869 KiB

Open AccessReview

Cybersecurity Risk Management Framework for Blockchain Identity Management Systems in Health IoT

by Bandar Alamri, Katie Crowley and Ita Richardson

Sensors 2023, 23(1), 218; https://doi.org/10.3390/s23010218 - 25 Dec 2022

Cited by 14 | Viewed by 6014

Abstract

Blockchain (BC) has recently paved the way for developing Decentralized Identity Management (IdM) systems for different information systems. Researchers widely use it to develop decentralized IdM systems for the Health Internet of Things (HIoT). HIoT is considered a vulnerable system that produces and processes sensitive data. BC-based IdM systems have the potential to be more secure and privacy-aware than centralized IdM systems. However, many studies have shown potential security risks to using BC. A Systematic Literature Review (SLR) conducted by the authors on BC-based IdM systems in HIoT systems showed a lack of comprehensive security and risk management frameworks for BC-based IdM systems in HIoT. Conducting a further SLR focusing on risk management and supplemented by Grey Literature (GL), in this paper, a security taxonomy, security framework, and cybersecurity risk management framework for the HIoT BC-IdM systems are identified and proposed. The cybersecurity risk management framework will significantly assist developers, researchers, and organizations in developing a secure BC-based IdM to ensure HIoT users’ data privacy and security. Full article

(This article belongs to the Special Issue Cybersecurity in the Internet of Things)

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