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New Trends for Securing the Internet of Things
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New Trends for Securing 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: closed (15 October 2022) | Viewed by 13693

Special Issue Editors

Dr. Diego Martín

E-Mail Website
Guest Editor
ETSI de Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense 30, 28040 Madrid, Spain
Interests: Internet of Things; blockchain technologies; cyber physical systems; knowledge management; information retrieval
Special Issues, Collections and Topics in MDPI journals
Dr. Masoud Kaveh

E-Mail
Guest Editor
ETSI de Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense 30, 28040 Madrid, Spain
Interests: PUFs; hardware security; cryptographic protocols; physical security; Blockchain security
Dr. Mohammad Reza Mosavi

E-Mail Website
Guest Editor
Department of Electrical Engineering, Iran University of Science and Technology, Tehran 13114-16846, Iran
Interests: IoT security; machine learning; PUFs; Hardware security

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) is one of the most important emerging technologies. IoT aims to integrate physical devices/sensors in a wide range of applications where all of them are connected to the Internet. Communication links in the IoT environment can be compromised by attackers; hence, utilization of cryptographic primitives (symmetric and asymmetric algorithms, one-way hash functions, blockchain technology, etc.) has been essential to build secure communication protocols. Furthermore, IoT devices/sensors are mostly deployed in the open, making them vulnerable to physical attacks. Therefore, the method of implementing and storing of cryptographic primitives on IoT devices has significantly grown in importance. Improper implementation of block ciphers and security algorithms can lead to IoT devices being vulnerable to a type of physical attacks called a side-channel attack, while improper storage of secrets on devices' memory can lead to another type of physical attack called a memory attack.

Physically Unclonable Functions (PUFs) have been proposed as the most promising tools concerning physical security for IoT. PUF is a "unique hardware fingerprint" (like unique fingerprint in human) that can produce unique identities per semiconductor devices like integrated circuits (ICs). PUFs can be used as secret key generators or unique identities in ICs that are embedded in IoT devices/sensors. The unique characterizations of PUFs are based on uncontrollable variations that naturally occur during the manufacturing process of semiconductor ICs.

This Special Issue seeks scientific research/implementations that contribute to the secure sensing, secure key generation, secure identification, efficient authentication, and secure communication with a standpoint of cyber/physical security in the IoT network. Authors can submit papers in the areas of hardware security, cyber/physical security approaches for cyber-physical systems, efficient designing and implementation of PUFs, cryptographic protocols, machine learning and IoT security, attacks on and cryptanalysis of PUFs, blockchain-based protocols, and other related areas.

Dr. Diego Martín
Dr. Masoud Kaveh
Dr. Mohammad Reza Mosavi
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 2600 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

  • IoT
  • Security
  • PUF
  • Cryptography

Published Papers (5 papers)

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Research

30 pages, 1060 KiB  
Article
Relevant Cybersecurity Aspects of IoT Microservices Architectures Deployed over Next-Generation Mobile Networks
by Constantin Lucian Aldea, Razvan Bocu and Anca Vasilescu
Sensors 2023, 23(1), 189; https://doi.org/10.3390/s23010189 - 24 Dec 2022
Cited by 1 | Viewed by 1793
Abstract
The design and implementation of secure IoT platforms and software solutions represent both a required functional feature and a performance acceptance factor nowadays. This paper describes relevant cybersecurity problems considered during the proposed microservices architecture development. Service composition mechanisms and their security are [...] Read more.
The design and implementation of secure IoT platforms and software solutions represent both a required functional feature and a performance acceptance factor nowadays. This paper describes relevant cybersecurity problems considered during the proposed microservices architecture development. Service composition mechanisms and their security are affected by the underlying hardware components and networks. The overall speedup of the platforms, which are implemented using the new 5G networks, and the capabilities of new performant IoT devices may be wasted by an inadequate combination of authentication services and security mechanisms, by the architectural misplacing of the encryption services, or by the inappropriate subsystems scaling. Considering the emerging microservices platforms, the Spring Boot alternative is used to implement data generation services, IoT sensor reading services, IoT actuators control services, and authentication services, and ultimately assemble them into a secure microservices architecture. Furthermore, considering the designed architecture, relevant security aspects related to the medical and energy domains are analyzed and discussed. Based on the proposed architectural concept, it is shown that well-designed and orchestrated architectures that consider the proper security aspects and their functional influence can lead to stable and secure implementations of the end user’s software platforms. Full article
(This article belongs to the Special Issue New Trends for Securing the Internet of Things)
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18 pages, 12079 KiB  
Article
A Mathematically Generated Noise Technique for Ultrasound Systems
by Hojong Choi and Seung-Hyeok Shin
Sensors 2022, 22(24), 9709; https://0-doi-org.brum.beds.ac.uk/10.3390/s22249709 - 11 Dec 2022
Cited by 1 | Viewed by 881
Abstract
Ultrasound systems have been widely used for consultation; however, they are susceptible to cyberattacks. Such ultrasound systems use random bits to protect patient information, which is vital to the stability of information-protecting systems used in ultrasound machines. The stability of the random bit [...] Read more.
Ultrasound systems have been widely used for consultation; however, they are susceptible to cyberattacks. Such ultrasound systems use random bits to protect patient information, which is vital to the stability of information-protecting systems used in ultrasound machines. The stability of the random bit must satisfy its unpredictability. To create a random bit, noise generated in hardware is typically used; however, extracting sufficient noise from systems is challenging when resources are limited. There are various methods for generating noises but most of these studies are based on hardware. Compared with hardware-based methods, software-based methods can be easily accessed by the software developer; therefore, we applied a mathematically generated noise function to generate random bits for ultrasound systems. Herein, we compared the performance of random bits using a newly proposed mathematical function and using the frequency of the central processing unit of the hardware. Random bits are generated using a raw bitmap image measuring 1000 × 663 bytes. The generated random bit analyzes the sampling data in generation time units as time-series data and then verifies the mean, median, and mode. To further apply the random bit in an ultrasound system, the image is randomized by applying exclusive mixing to a 1000 × 663 ultrasound phantom image; subsequently, the comparison and analysis of statistical data processing using hardware noise and the proposed algorithm were provided. The peak signal-to-noise ratio and mean square error of the images are compared to evaluate their quality. As a result of the test, the min entropy estimate (estimated value) was 7.156616/8 bit in the proposed study, which indicated a performance superior to that of GetSystemTime. These results show that the proposed algorithm outperforms the conventional method used in ultrasound systems. Full article
(This article belongs to the Special Issue New Trends for Securing the Internet of Things)
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17 pages, 4221 KiB  
Article
A Novel Deep Supervised Learning-Based Approach for Intrusion Detection in IoT Systems
by Sahba Baniasadi, Omid Rostami, Diego Martín and Mehrdad Kaveh
Sensors 2022, 22(12), 4459; https://0-doi-org.brum.beds.ac.uk/10.3390/s22124459 - 13 Jun 2022
Cited by 18 | Viewed by 2424
Abstract
The Internet of Things (IoT) has become one of the most important concepts in various aspects of our modern life in recent years. However, the most critical challenge for the world-wide use of the IoT is to address its security issues. One of [...] Read more.
The Internet of Things (IoT) has become one of the most important concepts in various aspects of our modern life in recent years. However, the most critical challenge for the world-wide use of the IoT is to address its security issues. One of the most important tasks to address the security challenges in the IoT is to detect intrusion in the network. Although the machine/deep learning-based solutions have been repeatedly used to detect network intrusion through recent years, there is still considerable potential to improve the accuracy and performance of the classifier (intrusion detector). In this paper, we develop a novel training algorithm to better tune the parameters of the used deep architecture. To specifically do so, we first introduce a novel neighborhood search-based particle swarm optimization (NSBPSO) algorithm to improve the exploitation/exploration of the PSO algorithm. Next, we use the advantage of NSBPSO to optimally train the deep architecture as our network intrusion detector in order to obtain better accuracy and performance. For evaluating the performance of the proposed classifier, we use two network intrusion detection datasets named UNSW-NB15 and Bot-IoT to rate the accuracy and performance of the proposed classifier. Full article
(This article belongs to the Special Issue New Trends for Securing the Internet of Things)
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26 pages, 5423 KiB  
Article
Blockchain-Based Authentication and Trust Management Mechanism for Smart Cities
by Muhammad Asif, Zeeshan Aziz, Maaz Bin Ahmad, Adnan Khalid, Hammad Abdul Waris and Asfandyar Gilani
Sensors 2022, 22(7), 2604; https://0-doi-org.brum.beds.ac.uk/10.3390/s22072604 - 29 Mar 2022
Cited by 27 | Viewed by 3799
Abstract
Security has always been the main concern for the internet of things (IoT)-based systems. Blockchain, with its decentralized and distributed design, prevents the risks of the existing centralized methodologies. Conventional security and privacy architectures are inapplicable in the spectrum of IoT due to [...] Read more.
Security has always been the main concern for the internet of things (IoT)-based systems. Blockchain, with its decentralized and distributed design, prevents the risks of the existing centralized methodologies. Conventional security and privacy architectures are inapplicable in the spectrum of IoT due to its resource constraints. To overcome this problem, this paper presents a Blockchain-based security mechanism that enables secure authorized access to smart city resources. The presented mechanism comprises the ACE (Authentication and Authorization for Constrained Environments) framework-based authorization Blockchain and the OSCAR (Object Security Architecture for the Internet of Things) object security model. The Blockchain lays out a flexible and trustless authorization mechanism, while OSCAR makes use of a public ledger to structure multicast groups for authorized clients. Moreover, a meteor-based application is developed to provide a user-friendly interface for heterogeneous technologies belonging to the smart city. The users would be able to interact with and control their smart city resources such as traffic lights, smart electric meters, surveillance cameras, etc., through this application. To evaluate the performance and feasibility of the proposed mechanism, the authorization Blockchain is implemented on top of the Ethereum network. The authentication mechanism is developed in the node.js server and a smart city is simulated with the help of Raspberry Pi B+. Furthermore, mocha and chai frameworks are used to assess the performance of the system. Experimental results reveal that the authentication response time is less than 100 ms even if the average hand-shaking time increases with the number of clients. Full article
(This article belongs to the Special Issue New Trends for Securing the Internet of Things)
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16 pages, 5758 KiB  
Article
Deep PUF: A Highly Reliable DRAM PUF-Based Authentication for IoT Networks Using Deep Convolutional Neural Networks
by Fatemeh Najafi, Masoud Kaveh, Diego Martín and Mohammad Reza Mosavi
Sensors 2021, 21(6), 2009; https://0-doi-org.brum.beds.ac.uk/10.3390/s21062009 - 12 Mar 2021
Cited by 19 | Viewed by 3505
Abstract
Traditional authentication techniques, such as cryptographic solutions, are vulnerable to various attacks occurring on session keys and data. Physical unclonable functions (PUFs) such as dynamic random access memory (DRAM)-based PUFs are introduced as promising security blocks to enable cryptography and authentication services. However, [...] Read more.
Traditional authentication techniques, such as cryptographic solutions, are vulnerable to various attacks occurring on session keys and data. Physical unclonable functions (PUFs) such as dynamic random access memory (DRAM)-based PUFs are introduced as promising security blocks to enable cryptography and authentication services. However, PUFs are often sensitive to internal and external noises, which cause reliability issues. The requirement of additional robustness and reliability leads to the involvement of error-reduction methods such as error correction codes (ECCs) and pre-selection schemes that cause considerable extra overheads. In this paper, we propose deep PUF: a deep convolutional neural network (CNN)-based scheme using the latency-based DRAM PUFs without the need for any additional error correction technique. The proposed framework provides a higher number of challenge-response pairs (CRPs) by eliminating the pre-selection and filtering mechanisms. The entire complexity of device identification is moved to the server side that enables the authentication of resource-constrained nodes. The experimental results from a 1Gb DDR3 show that the responses under varying conditions can be classified with at least a 94.9% accuracy rate by using CNN. After applying the proposed authentication steps to the classification results, we show that the probability of identification error can be drastically reduced, which leads to a highly reliable authentication. Full article
(This article belongs to the Special Issue New Trends for Securing the Internet of Things)
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