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: 15 July 2024 | Viewed by 6569

Special Issue Editors

Department of Computer Science, University of Bradford, Bradford BD7 1DP, UK
Interests: network security; cyber security; performance modeling of cloud and communication networks
Department of Computer Science, University of Bradford, Bradford BD7 1DP, UK
Interests: designing, analysing, and implementing cryptographic protocols with security and privacy guarantees using concepts of applied cryptography, distributed systems, game theory, and logic programming
Electric & Electronic Engineering, SCEDT Engineering, Teesside University, Middlesbrough TS1 3BX, UK
Interests: WLANs and WPANs (frequency, energy, interference management, among others); cross-layer optimisation; network security; 3GPP LTE-WLAN aggregation

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

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Keywords

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

Published Papers (4 papers)

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Research

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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
Viewed by 919
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
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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
Viewed by 818
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
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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
Viewed by 1599
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
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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 4 | Viewed by 2595
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
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