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Advances in Internet of Things (IoT) Technologies and Cybersecurity
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Advances in Internet of Things (IoT) Technologies and Cybersecurity

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 March 2025 | Viewed by 10895

Special Issue Editors

Prof. Dr. Vladimir Simov Jotsov

E-Mail Website
Guest Editor
1. Department of Information Technology, University of Library Studies and Information Technologies, 1784 Sofia, Bulgaria
2. Department of Computer Engineering and Cybersecurity, International Information Technology University, Almaty 050000, Kazakhstan
Interests: mobile security; industrial security; autonomous systems; agents; data science; knowledge/data modeling; preprocessing; machine learning
Dr. Iqbal H. Sarker

E-Mail Website
Guest Editor
Australian Cyber Security Cooperative Research Centre (CRC) & Security Research Institute, Edith Cowan University, Perth, WA 6027, Australia
Interests: cybersecurity; AI and machine learning; data science; IoT and smart city applications; digital twin and critical infrastructure security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapidly growing Internet of Things technologies (IoT) and constantly changing threats force the developers of contemporary cybersecurity ecosystems to resolve a bundle of rapidly changing and sometimes controversial tasks. Everybody should pay for their comfort requirements, which are embedded in security, quality, and life standards. First, mobile security should be data-driven; otherwise, it will eventually be destroyed using machine learning/chatbots and many other tools available on the dark side of the Internet. The main goal of this Special Issue is the ideas, methods, and results of how to build a combination of active and passive defense instruments as a base for construction of autonomous ecosystems.

Architectures, standards, devices, control methods: each aspect of the advanced technology field under consideration gives us a small picture of a large cybersecurity puzzle task. There is no general solution; the innovative system must forecast and prepare for new, unknown types of attacks. The desired cybersecurity ecosystems include, but are not limited to, cloud/fog security applications; twin, virtual, and other modeling possibilities; methods for transfer from algorithms to autonomous evolving systems; machine learning, deep learning, the application of big datasets, the usage of reasoners, and other types of software agents, etc.

This Special Issue includes original and innovative research papers in the overlapping fields of:

  • Secured IoT and Smart City Applications;
  • Data-driven security computing;
  • Artificial intelligence and machine learning in Cybersecurity;
  • Data science applications in cyber defense systems;
  • Big data security applications;
  • Ontologies and other security knowledge/data modeling tools;
  • Cloud/edge/fog computing;
  • Architectures of industrial and other novel security systems;
  • Defense ecosystems for Industry 4.0;
  • High-performance cybersecurity computing;
  • Forensic methods against intelligent types of attacks;
  • Other corresponding research fields.

Prof. Dr. Vladimir Simov Jotsov
Dr. Iqbal H. Sarker
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. Applied Sciences 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

  • IoT security
  • secure smart cities, mobile security
  • cloud/fog cybersecurity
  • data science
  • AI and machine learning
  • autonomous defense tools and instruments

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

15 pages, 1309 KiB  
Article
Tradeoffs in Key Rotation Strategies for Industrial Internet of Things Devices and Firmware
by Sunil Joshi, Kenneth Crowther and Jarvis Robinson
Appl. Sci. 2024, 14(21), 9942; https://doi.org/10.3390/app14219942 - 30 Oct 2024
Viewed by 1142
Abstract
This paper provides an overview of several secure boot architectures with a focus on key rotation. It expands on a practitioner note that the authors submitted to the 2023 IEEE Secure Development Conference. Key rotation is important due to the frequency of lost [...] Read more.
This paper provides an overview of several secure boot architectures with a focus on key rotation. It expands on a practitioner note that the authors submitted to the 2023 IEEE Secure Development Conference. Key rotation is important due to the frequency of lost signing keys and the difficulty of managing secret keys for the long lifetimes of Industrial Internet of Things (IIOT) devices. Key rotation is not simple for IIOT due to limited resources during a secure boot process and the constraints of the firmware utilities that come from the chip vendors. This paper reviews and compares five common architectures for a secure boot that are seen across the IIOT community. For each architecture, it provides some key strengths and weaknesses associated with that architecture. The paper then provides a detailed comparison and analysis of the architectures to convince the IIOT community to move towards a strong use of certificates (instead of the traditional use of raw public keys). The intent of this paper is to provide a practitioner’s perspective on these challenges and the tradeoffs in hopes of inviting comments from chip vendors and the broader community. Full article
(This article belongs to the Special Issue Advances in Internet of Things (IoT) Technologies and Cybersecurity)
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Figure 1

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<p>Generic concept of public key on device to validate firmware signature.</p> Full article ">Figure 2
<p>Preslotted keys approach.</p> Full article ">Figure 3
<p>UPTANE design.</p> Full article ">Figure 4
<p>X.509 for firmware signing.</p> Full article ">Figure 5
<p>Tradeoffs between IIOT key rotation architectures (A) through (E) described in the previous subsections according to the measures defined in <a href="#applsci-14-09942-t001" class="html-table">Table 1</a>.</p> Full article ">
19 pages, 2164 KiB  
Article
Enhancing IoT Security Using GA-HDLAD: A Hybrid Deep Learning Approach for Anomaly Detection
by Ibrahim Mutambik
Appl. Sci. 2024, 14(21), 9848; https://doi.org/10.3390/app14219848 - 28 Oct 2024
Cited by 1 | Viewed by 1693
Abstract
The adoption and use of the Internet of Things (IoT) have increased rapidly over recent years, and cyber threats in IoT devices have also become more common. Thus, the development of a system that can effectively identify malicious attacks and reduce security threats [...] Read more.
The adoption and use of the Internet of Things (IoT) have increased rapidly over recent years, and cyber threats in IoT devices have also become more common. Thus, the development of a system that can effectively identify malicious attacks and reduce security threats in IoT devices has become a topic of great importance. One of the most serious threats comes from botnets, which commonly attack IoT devices by interrupting the networks required for the devices to run. There are a number of methods that can be used to improve security by identifying unknown patterns in IoT networks, including deep learning and machine learning approaches. In this study, an algorithm named the genetic algorithm with hybrid deep learning-based anomaly detection (GA-HDLAD) is developed, with the aim of improving security by identifying botnets within the IoT environment. The GA-HDLAD technique addresses the problem of high dimensionality by using a genetic algorithm during feature selection. Hybrid deep learning is used to detect botnets; the approach is a combination of recurrent neural networks (RNNs), feature extraction techniques (FETs), and attention concepts. Botnet attacks commonly involve complex patterns that the hybrid deep learning (HDL) method can detect. Moreover, the use of FETs in the model ensures that features can be effectively extracted from spatial data, while temporal dependencies are captured by RNNs. Simulated annealing (SA) is utilized to select the hyperparameters necessary for the HDL approach. In this study, the GA-HDLAD system is experimentally assessed using a benchmark botnet dataset, and the findings reveal that the system provides superior results in comparison to existing detection methods. Full article
(This article belongs to the Special Issue Advances in Internet of Things (IoT) Technologies and Cybersecurity)
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Figure 1

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<p>Confusion matrices for training and testing phases of the GA-HDLAD model for different data splits.</p> Full article ">Figure 2
<p>Average performance of GA-HDLAD methodology with 70:30 split.</p> Full article ">Figure 3
<p>Average performance of GA-HDLAD methodology with 60:40 split.</p> Full article ">Figure 4
<p>Performance curve of the GA-HDLAD model showing accuracy.</p> Full article ">Figure 5
<p>Loss trend of the GA-HDLAD model.</p> Full article ">Figure 6
<p>Precision–recall curve with confidence intervals showing the GA-HDLAD model’s performance.</p> Full article ">Figure 7
<p>ROC curve with confidence intervals for the GA-HDLAD model.</p> Full article ">Figure 8
<p>Comparative analysis of the GA-HDLAD model against competing detection systems.</p> Full article ">
22 pages, 4655 KiB  
Article
Deep-Learning-Based Approach for IoT Attack and Malware Detection
by Burak Taşcı
Appl. Sci. 2024, 14(18), 8505; https://doi.org/10.3390/app14188505 - 20 Sep 2024
Cited by 2 | Viewed by 4349
Abstract
The Internet of Things (IoT), introduced by Kevin Ashton in the late 1990s, has transformed technology usage globally, enhancing efficiency and convenience but also posing significant security challenges. With the proliferation of IoT devices expected to exceed 29 billion by 2030, securing these [...] Read more.
The Internet of Things (IoT), introduced by Kevin Ashton in the late 1990s, has transformed technology usage globally, enhancing efficiency and convenience but also posing significant security challenges. With the proliferation of IoT devices expected to exceed 29 billion by 2030, securing these devices is crucial. This study proposes an optimized 1D convolutional neural network (1D CNN) model for effectively classifying IoT security data. The model architecture includes input, convolutional, self-attention, and output layers, utilizing GELU activation, dropout, and normalization techniques to improve performance and prevent overfitting. The model was evaluated using the CIC IoT 2023, CIC-MalMem-2022, and CIC-IDS2017 datasets, achieving impressive results: 98.36% accuracy, 100% precision, 99.96% recall, and 99.95% F1-score for CIC IoT 2023; 99.90% accuracy, 99.98% precision, 99.97% recall, and 99.96% F1-score for CIC-MalMem-2022; and 99.99% accuracy, 99.99% precision, 99.98% recall, and 99.98% F1-score for CIC-IDS2017. These outcomes demonstrate the model’s effectiveness in detecting and classifying various IoT-related attacks and malware. The study highlights the potential of deep-learning techniques to enhance IoT security, with the developed model showing high performance and low computational overhead, making it suitable for real-time applications and resource-constrained devices. Future research should aim at testing the model on larger datasets and incorporating adaptive learning capabilities to further enhance its robustness. This research significantly contributes to IoT security by providing advanced insights into deploying deep-learning models, encouraging further exploration in this dynamic field. Full article
(This article belongs to the Special Issue Advances in Internet of Things (IoT) Technologies and Cybersecurity)
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Figure 1

Figure 1
<p>Malware families count by category.</p> Full article ">Figure 2
<p>Graphical representation of the proposed CNN.</p> Full article ">Figure 3
<p>Accuracy and loss curves for the proposed CNN model on the CIC IoT 2023 dataset.</p> Full article ">Figure 4
<p>Accuracy and loss curves for the proposed CNN model on the CIC-MalMem-2022.</p> Full article ">Figure 5
<p>Accuracy and loss curves for the proposed CNN model on the CIC-IDS2017.</p> Full article ">Figure 6
<p>Confusion matrix for the CIC IoT 2023 dataset.</p> Full article ">Figure 7
<p>Confusion matrix for the CIC-MalMem-2022 dataset.</p> Full article ">Figure 8
<p>Confusion matrix for the CIC-IDS2017 dataset.</p> Full article ">Figure 9
<p>Comparison of machine-learning methods.</p> Full article ">
13 pages, 454 KiB  
Article
Extending Product Lifecycles—An Initial Model with New and Emerging Existential Design Aspects Required for Long and Extendable Lifecycles
by John Lindström, Petter Kyösti, Foivos Psarommatis, Karl Andersson and Kristiina Starck Enman
Appl. Sci. 2024, 14(13), 5812; https://doi.org/10.3390/app14135812 - 3 Jul 2024
Cited by 1 | Viewed by 1461
Abstract
This paper introduces an evaluated initial model for how product lifecycles can be extended considering new and emerging existential design aspects concerning both general as well as digital/connected products. The initial model, which is cyclic, includes reverse logistics of components and raw materials, [...] Read more.
This paper introduces an evaluated initial model for how product lifecycles can be extended considering new and emerging existential design aspects concerning both general as well as digital/connected products. The initial model, which is cyclic, includes reverse logistics of components and raw materials, as well as information on how to manage data at the end of lifecycles. The aim is to improve long-term sustainability with a high degree of circularity while also achieving increased profitability and competitiveness. Further, we highlighted that product providers must start to evaluate and prepare for how to improve product durability, manage long and extendable lifespans, and achieve circularity with reverse logistics to close the loops. Additionally, updatability and upgradability are also required to stay current with time and create value while being cybersecure. Otherwise, customers’ expectations, various legal and regulatory aspects, as well as other existential design aspects can halt or even terminate a product’s lifecycle. Full article
(This article belongs to the Special Issue Advances in Internet of Things (IoT) Technologies and Cybersecurity)
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<p>Initial model concerning extending product lifecycles to achieve long and extendable lifecycles in the light of new and emerging existential design aspects.</p> Full article ">
13 pages, 828 KiB  
Article
Secure IoT Communication: Implementing a One-Time Pad Protocol with True Random Numbers and Secure Multiparty Sums
by Julio Fenner, Patricio Galeas, Francisco Escobar and Rail Neira
Appl. Sci. 2024, 14(12), 5354; https://doi.org/10.3390/app14125354 - 20 Jun 2024
Cited by 1 | Viewed by 1128
Abstract
We introduce an innovative approach for secure communication in the Internet of Things (IoT) environment using a one-time pad (OTP) protocol. This protocol is augmented by incorporating a secure multiparty sum protocol to produce OTP keys from genuine random numbers obtained from the [...] Read more.
We introduce an innovative approach for secure communication in the Internet of Things (IoT) environment using a one-time pad (OTP) protocol. This protocol is augmented by incorporating a secure multiparty sum protocol to produce OTP keys from genuine random numbers obtained from the physical phenomena observed in each device. We have implemented our method using ZeroC-Ice v.3.7, dependable middleware for distributed computing, demonstrating its practicality in various hybrid IoT scenarios, particularly in devices with limited processing capabilities. The security features of our protocol are evaluated under the Dolev–Yao threat model, providing a thorough assessment of its defense against potential cyber threats. Full article
(This article belongs to the Special Issue Advances in Internet of Things (IoT) Technologies and Cybersecurity)
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Figure 1

Figure 1
<p>Diagram depicting key agreement between <math display="inline"><semantics> <msub> <mi>P</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>P</mi> <mn>2</mn> </msub> </semantics></math> from the private inputs <math display="inline"><semantics> <msub> <mi>x</mi> <mn>1</mn> </msub> </semantics></math> (in red) and <math display="inline"><semantics> <msub> <mi>x</mi> <mn>2</mn> </msub> </semantics></math> (in green) by using <math display="inline"><semantics> <msub> <mi>P</mi> <mn>3</mn> </msub> </semantics></math> (in blue) as an auxiliary unit in the SMPC protocol. The total sum is <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <msub> <mi>s</mi> <mn>1</mn> </msub> <mo>+</mo> <msub> <mi>s</mi> <mn>2</mn> </msub> <mo>+</mo> <msub> <mi>s</mi> <mn>3</mn> </msub> <mo>=</mo> <msub> <mi>x</mi> <mn>1</mn> </msub> <mo>+</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>+</mo> <msub> <mi>x</mi> <mn>3</mn> </msub> </mrow> </semantics></math>, where <math display="inline"><semantics> <msub> <mi>x</mi> <mn>3</mn> </msub> </semantics></math> is random and cannot be traced by observation of the communication.</p> Full article ">Figure 2
<p>Diagram depictingOn-the-Fly Key Agreement and encryption between <math display="inline"><semantics> <msub> <mi>P</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>P</mi> <mn>2</mn> </msub> </semantics></math> using <math display="inline"><semantics> <msub> <mi>P</mi> <mn>3</mn> </msub> </semantics></math> (in blue) as an auxiliary unit in the SMPC protocol. Data owned by the client, server, and Dummy party are represented in red, green, and blue, respectively.</p> Full article ">Figure 3
<p>Simple SMP-OTF4IOT’s “Hello secret world!”: client sending encrypted message and server receiving and decrypting the message.</p> Full article ">
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