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Novel Methods Applied to Security and Privacy Problems in Future...
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Novel Methods Applied to Security and Privacy Problems in Future Networking Technologies

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (15 March 2025) | Viewed by 42952

Special Issue Editors

Prof. Dr. Irfan Awan

E-Mail Website
Guest Editor
Department of Computer Science, University of Bradford, Bradford BD7 1DP, UK
Interests: network security; cyber security; performance modeling of cloud and communication networks
Special Issues, Collections and Topics in MDPI journals
Dr. Amna Qureshi

E-Mail Website
Guest Editor
Department of Computer Science, University of Bradford, Bradford BD7 1DP, UK
Interests: designing, analysing; implementing cryptographic protocols with security and privacy guarantees using concepts of applied cryptography; distributed systems; game theory; logic programming
Special Issues, Collections and Topics in MDPI journals
Dr. Muhammad Shahwaiz Afaqui

E-Mail Website
Guest Editor
Electric & Electronic Engineering, SCEDT Engineering, Teesside University, Middlesbrough TS1 3BX, UK
Interests: WLANs and WPANs (frequency, energy, interference management, among others); cross-layer optimization; network security; 3GPP LTE-WLAN aggregation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Future networking technologies refer to emerging and developing technologies that are anticipated to shape the ways in which we connect, communicate, and share data in the future. These technologies have the potential to revolutionise the way we interact with the world, enabling rapid and more efficient communication. Examples of these technologies include 5G, 6G, blockchain, IoT, cloud computing, and Software-Defined Networking (SDN), among others. Although these technologies offer seamless communication and facilitate the advancement of new applications and services that have not been conceivable with the current networking technologies, these generate a number of security and privacy challenges. Some of the critical challenges to overcome include an increased attack surface, new attack vectors, evolving threats, data privacy and user trust. These are only a few of the security and privacy challenges that must be addressed in order to build a secure and trustworthy future for networking technologies.

To cope with the aforementioned challenges, this Special Issue welcomes original and innovative perspectives on theories, methodologies, schemes, algorithms, and systems related to all aspects of security and privacy in future networking technologies from academia, industry, and government. We invite the contribution of original research papers, survey papers, and position papers to this Special Issue. Potential topics include, but are not limited to, the following:

  • end-to-end communication security, privacy, and trust
  • security and privacy protection in 5G and beyond
  • trust, security, and privacy in cloud/edge computing
  • lightweight and privacy-preserving authentication mechanisms
  • lightweight identify and access management mechanisms
  • quantum cryptography
  • intrusion detection and prevention systems for network security
  • privacy preservation in ai-enabled networks
  • zero trust techniques, architectures, and models
  • security and privacy challenges in internet of things (IOT) networks
  • security, safety and reliability in industrial internet of things (IIOT)
  • secure and privacy-preserving techniques for blockchain in 5G/6G
  • sensing security and privacy for IEEE 802.11

Prof. Dr. Irfan Awan
Dr. Amna Qureshi
Dr. Muhammad Shahwaiz Afaqui
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. 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

  • security
  • privacy
  • trust
  • 5G/6G
  • AI
  • blockchain
  • IoT

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Related Special Issue

Published Papers (12 papers)

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Editorial

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5 pages, 135 KiB  
Editorial
Novel Methods Applied to Security and Privacy Problems in Future Networking Technologies
by Irfan-Ullah Awan, Amna Qureshi and Muhammad Shahwaiz Afaqui
Electronics 2025, 14(9), 1816; https://doi.org/10.3390/electronics14091816 - 29 Apr 2025
Abstract
The rapid development of future networking technologies, such as 5G, 6G, blockchain, the Internet of Things (IoT), cloud computing, and Software-Defined Networking (SDN) is set to revolutionize our methods of connection, communication, and data sharing [...] Full article
(This article belongs to the Special Issue Novel Methods Applied to Security and Privacy Problems in Future Networking Technologies)

Research

Jump to: Editorial, Review

20 pages, 2221 KiB  
Article
An Adversarial Example Generation Algorithm Based on DE-C&W
by Ran Zhang, Qianru Wu and Yifan Wang
Electronics 2025, 14(7), 1274; https://doi.org/10.3390/electronics14071274 - 24 Mar 2025
Viewed by 231
Abstract
Security issues surrounding deep learning models weaken their application effectiveness in various fields. Studying attacks against deep learning models contributes to evaluating their security and improving it in a targeted manner. Among the methods used for this purpose, adversarial example generation methods for [...] Read more.
Security issues surrounding deep learning models weaken their application effectiveness in various fields. Studying attacks against deep learning models contributes to evaluating their security and improving it in a targeted manner. Among the methods used for this purpose, adversarial example generation methods for deep learning models have become a hot topic in academic research. To overcome problems such as extensive network access, high attack costs, and limited universality in generating adversarial examples, this paper proposes a generic algorithm for adversarial example generation based on improved DE-C&W. The algorithm employs an improved differential evolution (DE) algorithm to conduct a global search of the original examples, searching for vulnerable sensitive points susceptible to being attacked. Then, random perturbations are added to these sensitive points to obtain adversarial examples, which are used as the initial input of C&W attack. The loss functions of the C&W attack algorithm are constructed based on these initial input examples, and the loss function is further optimized using the Adaptive Moment Estimation (Adam) algorithm to obtain the optimal perturbation vector. The experimental results demonstrate that the algorithm not only ensures that the generated adversarial examples achieve a higher success rate of attacks, but also exhibits better transferability while reducing the average number of queries and lowering attack costs. Full article
(This article belongs to the Special Issue Novel Methods Applied to Security and Privacy Problems in Future Networking Technologies)
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18 pages, 4998 KiB  
Article
Predicting the Impact of Distributed Denial of Service (DDoS) Attacks in Long-Term Evolution for Machine (LTE-M) Networks Using a Continuous-Time Markov Chain (CTMC) Model
by Mohammed Hammood Mutar, Ahmad Hani El Fawal, Abbass Nasser and Ali Mansour
Electronics 2024, 13(21), 4145; https://doi.org/10.3390/electronics13214145 - 22 Oct 2024
Viewed by 1692
Abstract
The way we connect with the physical world has completely changed because of the advancement of the Internet of Things (IoT). However, there are several difficulties associated with this change. A significant advancement has been the emergence of intelligent machines that are able [...] Read more.
The way we connect with the physical world has completely changed because of the advancement of the Internet of Things (IoT). However, there are several difficulties associated with this change. A significant advancement has been the emergence of intelligent machines that are able to gather data for analysis and decision-making. In terms of IoT security, we are seeing a sharp increase in hacker activities worldwide. Botnets are more common now in many countries, and such attacks are very difficult to counter. In this context, Distributed Denial of Service (DDoS) attacks pose a significant threat to the availability and integrity of online services. In this paper, we developed a predictive model called Markov Detection and Prediction (MDP) using a Continuous-Time Markov Chain (CTMC) to identify and preemptively mitigate DDoS attacks. The MDP model helps in studying, analyzing, and predicting DDoS attacks in Long-Term Evolution for Machine (LTE-M) networks and IoT environments. The results show that using our MDP model, the system is able to differentiate between Authentic, Suspicious, and Malicious traffic. Additionally, we are able to predict the system behavior when facing different DDoS attacks. Full article
(This article belongs to the Special Issue Novel Methods Applied to Security and Privacy Problems in Future Networking Technologies)
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18 pages, 4387 KiB  
Article
Enhanced Image-Based Malware Classification Using Transformer-Based Convolutional Neural Networks (CNNs)
by Moses Ashawa, Nsikak Owoh, Salaheddin Hosseinzadeh and Jude Osamor
Electronics 2024, 13(20), 4081; https://doi.org/10.3390/electronics13204081 - 17 Oct 2024
Cited by 3 | Viewed by 2751
Abstract
As malware samples grow in complexity and employ advanced evasion techniques, traditional detection methods are insufficient for accurately classifying large volumes of sophisticated malware variants. To address this issue, image-based malware classification techniques leveraging machine learning algorithms have been developed as a more [...] Read more.
As malware samples grow in complexity and employ advanced evasion techniques, traditional detection methods are insufficient for accurately classifying large volumes of sophisticated malware variants. To address this issue, image-based malware classification techniques leveraging machine learning algorithms have been developed as a more optimal solution to this challenge. However, accurately classifying content distribution-based features with unique pixel intensities from grayscale images remains a challenge. This paper proposes an enhanced image-based malware classification system using convolutional neural networks (CNNs) using ResNet-152 and vision transformer (ViT). The two architectures are then compared to determine their classification abilities. A total of 6137 benign files and 9861 malicious executables are converted from text files to unsigned integers and then to images. The ViT examined unsigned integers as pixel values, while ResNet-152 converted the pixel values into floating points for classification. The result of the experiments demonstrates a high-performance accuracy of 99.62% with effective hyperparameters of 10-fold cross-validation. The findings indicate that the proposed model is capable of being implemented in dynamic and complex malware environments, achieving a practical computational efficiency of 47.2 s for the identification and classification of new malware samples. Full article
(This article belongs to the Special Issue Novel Methods Applied to Security and Privacy Problems in Future Networking Technologies)
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26 pages, 4212 KiB  
Article
Texture-Image-Oriented Coverless Data Hiding Based on Two-Dimensional Fractional Brownian Motion
by Yen-Ching Chang, Jui-Chuan Liu, Ching-Chun Chang and Chin-Chen Chang
Electronics 2024, 13(20), 4013; https://doi.org/10.3390/electronics13204013 - 12 Oct 2024
Viewed by 884
Abstract
In an AI-immersing age, scholars look for new possibilities of employing AI technology to their fields, and how to strengthen security and protect privacy is no exception. In a coverless data hiding domain, the embedding capacity of an image generally depends on the [...] Read more.
In an AI-immersing age, scholars look for new possibilities of employing AI technology to their fields, and how to strengthen security and protect privacy is no exception. In a coverless data hiding domain, the embedding capacity of an image generally depends on the size of a chosen database. Therefore, choosing a suitable database is a critical issue in coverless data hiding. A novel coverless data hiding approach is proposed by applying deep learning models to generate texture-like cover images or code images. These code images are then used to construct steganographic images to transmit covert messages. Effective mapping tables between code images in the database and hash sequences are established during the process. The cover images generated by a two-dimensional fractional Brownian motion (2D FBM) are simply called fractional Brownian images (FBIs). The only parameter, the Hurst exponent, of the 2D FBM determines the patterns of these cover images, and the seeds of a random number generator determine the various appearances of a pattern. Through the 2D FBM, we can easily generate as many FBIs of multifarious sizes, patterns, and appearances as possible whenever and wherever. In the paper, a deep learning model is treated as a secret key selecting qualified FBIs as code images to encode corresponding hash sequences. Both different seeds and different deep learning models can pick out diverse qualified FBIs. The proposed coverless data hiding scheme is effective when the amount of secret data is limited. The experimental results show that our proposed approach is more reliable, efficient, and of higher embedding capacity, compared to other coverless data hiding methods. Full article
(This article belongs to the Special Issue Novel Methods Applied to Security and Privacy Problems in Future Networking Technologies)
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24 pages, 1272 KiB  
Article
Leveraging Digital Twins and Intrusion Detection Systems for Enhanced Security in IoT-Based Smart City Infrastructures
by Mohammed El-Hajj
Electronics 2024, 13(19), 3941; https://doi.org/10.3390/electronics13193941 - 6 Oct 2024
Cited by 4 | Viewed by 2146
Abstract
In this research, we investigate the integration of an Intrusion Detection System (IDS) with a Digital Twin (DT) to enhance the cybersecurity of physical devices in cyber–physical systems. Using Eclipse Ditto as the DT platform and Snort as the IDS, we developed a [...] Read more.
In this research, we investigate the integration of an Intrusion Detection System (IDS) with a Digital Twin (DT) to enhance the cybersecurity of physical devices in cyber–physical systems. Using Eclipse Ditto as the DT platform and Snort as the IDS, we developed a near-realistic test environment that included a Raspberry Pi as the physical device and a Kali Linux virtual machine to perform common cyberattacks such as Hping3 flood attacks and NMAP reconnaissance scans. The results demonstrated that the IDS effectively detected Hping3-based flood attacks but showed limitations in identifying NMAP scans, suggesting areas for IDS configuration improvements. Furthermore, the study uncovered significant system resource impacts, including high Central Processing Unit (CPU) usage during SYN and ACK flood attacks and persistent memory usage after Network Mapper (NMAP) scans, highlighting the need for enhanced recovery mechanisms. This research presents a novel approach by coupling a Digital Twin with an IDS, enabling real-time monitoring and providing a dual perspective on both system performance and security. The integration offers a holistic method for identifying vulnerabilities and understanding resource impacts during cyberattacks. The work contributes new insights into the use of Digital Twins for cybersecurity and paves the way for further research into automated defense mechanisms, real-world validation of the proposed model, and the incorporation of additional attack scenarios. The results suggest that this combined approach holds significant promise for enhancing the security and resilience of IoT devices and other cyber–physical systems. Full article
(This article belongs to the Special Issue Novel Methods Applied to Security and Privacy Problems in Future Networking Technologies)
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42 pages, 1641 KiB  
Article
Attack-Aware Security Function Chaining
by Lukas Iffländer, Lukas Beierlieb and Samuel Kounev
Electronics 2024, 13(17), 3357; https://doi.org/10.3390/electronics13173357 - 23 Aug 2024
Viewed by 1484
Abstract
Cyberattacks have become more frequent and more violent in recent years. To date, defensive infrastructure has been relatively static, and security functions are usually placed in a common order that does not depend on the current situation. We propose the concept of attack-aware [...] Read more.
Cyberattacks have become more frequent and more violent in recent years. To date, defensive infrastructure has been relatively static, and security functions are usually placed in a common order that does not depend on the current situation. We propose the concept of attack-aware Security Service Function Chain reordering. The idea is to change the order of security functions depending on the malicious traffic observed. We present the basic idea, evaluate the impact of the function chain order, and introduce a framework for function chain reordering. Our evaluation shows that the order often has a significant impact on the performance of the security function chain and that there is no single order that outperforms all other orders in every situation. The proposed proof-of-concept framework successfully validates the feasibility of attack-aware security function chain reordering, and we propose additional extensions to eliminate the remaining deficiencies. Full article
(This article belongs to the Special Issue Novel Methods Applied to Security and Privacy Problems in Future Networking Technologies)
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20 pages, 2918 KiB  
Article
Mitigating Adversarial Attacks against IoT Profiling
by Euclides Carlos Pinto Neto, Sajjad Dadkhah, Somayeh Sadeghi and Heather Molyneaux
Electronics 2024, 13(13), 2646; https://doi.org/10.3390/electronics13132646 - 5 Jul 2024
Viewed by 1002
Abstract
Internet of Things (IoT) applications have been helping society in several ways. However, challenges still must be faced to enable efficient and secure IoT operations. In this context, IoT profiling refers to the service of identifying and classifying IoT devices’ behavior based on [...] Read more.
Internet of Things (IoT) applications have been helping society in several ways. However, challenges still must be faced to enable efficient and secure IoT operations. In this context, IoT profiling refers to the service of identifying and classifying IoT devices’ behavior based on different features using different approaches (e.g., Deep Learning). Data poisoning and adversarial attacks are challenging to detect and mitigate and can degrade the performance of a trained model. Thereupon, the main goal of this research is to propose the Overlapping Label Recovery (OLR) framework to mitigate the effects of label-flipping attacks in Deep-Learning-based IoT profiling. OLR uses Random Forests (RF) as underlying cleaners to recover labels. After that, the dataset is re-evaluated and new labels are produced to minimize the impact of label flipping. OLR can be configured using different hyperparameters and we investigate how different values can improve the recovery procedure. The results obtained by evaluating Deep Learning (DL) models using a poisoned version of the CIC IoT Dataset 2022 demonstrate that training overlap needs to be controlled to maintain good performance and that the proposed strategy improves the overall profiling performance in all cases investigated. Full article
(This article belongs to the Special Issue Novel Methods Applied to Security and Privacy Problems in Future Networking Technologies)
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49 pages, 18867 KiB  
Article
Enhancing Zero Trust Models in the Financial Industry through Blockchain Integration: A Proposed Framework
by Clement Daah, Amna Qureshi, Irfan Awan and Savas Konur
Electronics 2024, 13(5), 865; https://doi.org/10.3390/electronics13050865 - 23 Feb 2024
Cited by 16 | Viewed by 11524
Abstract
As financial institutions navigate an increasingly complex cyber threat landscape and regulatory ecosystem, there is a pressing need for a robust and adaptive security architecture. This paper introduces a comprehensive, Zero Trust model-based framework specifically tailored for the finance industry. It encompasses identity [...] Read more.
As financial institutions navigate an increasingly complex cyber threat landscape and regulatory ecosystem, there is a pressing need for a robust and adaptive security architecture. This paper introduces a comprehensive, Zero Trust model-based framework specifically tailored for the finance industry. It encompasses identity and access management (IAM), data protection, and device and network security and introduces trust through blockchain technology. This study provides a literature review of existing Zero Trust paradigms and contrasts them with cybersecurity solutions currently relevant to financial settings. The research adopts a mixed methods approach, combining extensive qualitative analysis through a literature review and assessment of security assumptions, threat modelling, and implementation strategies with quantitative evaluation using a prototype banking application for vulnerability scanning, security testing, and performance testing. The IAM component ensures robust authentication and authorisation processes, while device and network security measures protect against both internal and external threats. Data protection mechanisms maintain the confidentiality and integrity of sensitive information. Additionally, the blockchain-based trust component serves as an innovative layer to enhance security measures, offering both tamper-proof verification and increased integrity. Through analysis of potential threats and experimental evaluation of the Zero Trust model’s performance, the proposed framework offers financial institutions a comprehensive security architecture capable of effectively mitigating cyber threats and fostering enhanced consumer trust. Full article
(This article belongs to the Special Issue Novel Methods Applied to Security and Privacy Problems in Future Networking Technologies)
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19 pages, 2214 KiB  
Article
Employing Deep Reinforcement Learning to Cyber-Attack Simulation for Enhancing Cybersecurity
by Sang Ho Oh, Jeongyoon Kim, Jae Hoon Nah and Jongyoul Park
Electronics 2024, 13(3), 555; https://doi.org/10.3390/electronics13030555 - 30 Jan 2024
Cited by 7 | Viewed by 6895
Abstract
In the current landscape where cybersecurity threats are escalating in complexity and frequency, traditional defense mechanisms like rule-based firewalls and signature-based detection are proving inadequate. The dynamism and sophistication of modern cyber-attacks necessitate advanced solutions that can evolve and adapt in real-time. Enter [...] Read more.
In the current landscape where cybersecurity threats are escalating in complexity and frequency, traditional defense mechanisms like rule-based firewalls and signature-based detection are proving inadequate. The dynamism and sophistication of modern cyber-attacks necessitate advanced solutions that can evolve and adapt in real-time. Enter the field of deep reinforcement learning (DRL), a branch of artificial intelligence that has been effectively tackling complex decision-making problems across various domains, including cybersecurity. In this study, we advance the field by implementing a DRL framework to simulate cyber-attacks, drawing on authentic scenarios to enhance the realism and applicability of the simulations. By meticulously adapting DRL algorithms to the nuanced requirements of cybersecurity contexts—such as custom reward structures and actions, adversarial training, and dynamic environments—we provide a tailored approach that significantly improves upon traditional methods. Our research undertakes a thorough comparative analysis of three sophisticated DRL algorithms—deep Q-network (DQN), actor–critic, and proximal policy optimization (PPO)—against the traditional RL algorithm Q-learning, within a controlled simulation environment reflective of real-world cyber threats. The findings are striking: the actor–critic algorithm not only outperformed its counterparts with a success rate of 0.78 but also demonstrated superior efficiency, requiring the fewest iterations (171) to complete an episode and achieving the highest average reward of 4.8. In comparison, DQN, PPO, and Q-learning lagged slightly behind. These results underscore the critical impact of selecting the most fitting algorithm for cybersecurity simulations, as the right choice leads to more effective learning and defense strategies. The impressive performance of the actor–critic algorithm in this study marks a significant stride towards the development of adaptive, intelligent cybersecurity systems capable of countering the increasingly sophisticated landscape of cyber threats. Our study not only contributes a robust model for simulating cyber threats but also provides a scalable framework that can be adapted to various cybersecurity challenges. Full article
(This article belongs to the Special Issue Novel Methods Applied to Security and Privacy Problems in Future Networking Technologies)
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Review

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31 pages, 620 KiB  
Review
A Review of Generative Models in Generating Synthetic Attack Data for Cybersecurity
by Garima Agrawal, Amardeep Kaur and Sowmya Myneni
Electronics 2024, 13(2), 322; https://doi.org/10.3390/electronics13020322 - 11 Jan 2024
Cited by 17 | Viewed by 8527
Abstract
The ability of deep learning to process vast data and uncover concealed malicious patterns has spurred the adoption of deep learning methods within the cybersecurity domain. Nonetheless, a notable hurdle confronting cybersecurity researchers today is the acquisition of a sufficiently large dataset to [...] Read more.
The ability of deep learning to process vast data and uncover concealed malicious patterns has spurred the adoption of deep learning methods within the cybersecurity domain. Nonetheless, a notable hurdle confronting cybersecurity researchers today is the acquisition of a sufficiently large dataset to effectively train deep learning models. Privacy and security concerns associated with using real-world organization data have made cybersecurity researchers seek alternative strategies, notably focusing on generating synthetic data. Generative adversarial networks (GANs) have emerged as a prominent solution, lauded for their capacity to generate synthetic data spanning diverse domains. Despite their widespread use, the efficacy of GANs in generating realistic cyberattack data remains a subject requiring thorough investigation. Moreover, the proficiency of deep learning models trained on such synthetic data to accurately discern real-world attacks and anomalies poses an additional challenge that demands exploration. This paper delves into the essential aspects of generative learning, scrutinizing their data generation capabilities, and conducts a comprehensive review to address the above questions. Through this exploration, we aim to shed light on the potential of synthetic data in fortifying deep learning models for robust cybersecurity applications. Full article
(This article belongs to the Special Issue Novel Methods Applied to Security and Privacy Problems in Future Networking Technologies)
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25 pages, 792 KiB  
Review
An Overview of Safety and Security Analysis Frameworks for the Internet of Things
by Alhassan Abdulhamid, Sohag Kabir, Ibrahim Ghafir and Ci Lei
Electronics 2023, 12(14), 3086; https://doi.org/10.3390/electronics12143086 - 16 Jul 2023
Cited by 14 | Viewed by 4750
Abstract
The rapid progress of the Internet of Things (IoT) has continued to offer humanity numerous benefits, including many security and safety-critical applications. However, unlocking the full potential of IoT applications, especially in high-consequence domains, requires the assurance that IoT devices will not constitute [...] Read more.
The rapid progress of the Internet of Things (IoT) has continued to offer humanity numerous benefits, including many security and safety-critical applications. However, unlocking the full potential of IoT applications, especially in high-consequence domains, requires the assurance that IoT devices will not constitute risk hazards to the users or the environment. To design safe, secure, and reliable IoT systems, numerous frameworks have been proposed to analyse the safety and security, among other properties. This paper reviews some of the prominent classical and model-based system engineering (MBSE) approaches for IoT systems’ safety and security analysis. The review established that most analysis frameworks are based on classical manual approaches, which independently evaluate the two properties. The manual frameworks tend to inherit the natural limitations of informal system modelling, such as human error, a cumbersome processes, time consumption, and a lack of support for reusability. Model-based approaches have been incorporated into the safety and security analysis process to simplify the analysis process and improve the system design’s efficiency and manageability. Conversely, the existing MBSE safety and security analysis approaches in the IoT environment are still in their infancy. The limited number of proposed MBSE approaches have only considered limited and simple scenarios, which are yet to adequately evaluate the complex interactions between the two properties in the IoT domain. The findings of this survey are that the existing methods have not adequately addressed the analysis of safety/security interdependencies, detailed cyber security quantification analysis, and the unified treatment of safety and security properties. The existing classical and MBSE frameworks’ limitations obviously create gaps for a meaningful assessment of IoT dependability. To address some of the gaps, we proposed a possible research direction for developing a novel MBSE approach for the IoT domain’s safety and security coanalysis framework. Full article
(This article belongs to the Special Issue Novel Methods Applied to Security and Privacy Problems in Future Networking Technologies)
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