BEiNG WISE

The transition from fifth-generation (5G) to sixth-generation (6G) networks is driving significant advancements in network slicing, fueled by the growing demand for next-generation applications and services. However, managing these advancements within the constraints of finite resources creates the opportunity for open resource marketplaces, which introduces technical and business challenges. To address these, we propose TokenNet, the first blockchain-based architecture that represents network resources as non-fungible tokens (NFTs) in the context of network slicing. TokenNet facilitates secure, decentralized resource trading, ownership traceability, and management, optimizing resource allocation through auctioning, brokering, and trust mechanisms. It offers more granular, flexible, and trustworthy control over network resources compared to existing state-of-the-art systems. Our prototype implementation demonstrates its effectiveness, outperforming baseline models in cost efficiency, reducing delays, and improving minting performance. These advantages position TokenNet as a promising solution for future network management.

Industry 4.0 and 5.0 offer a promising framework for connecting electro-mechanical systems to cyberspace, enabling real-time access, telecontrol, human-machine collaboration, and intelligent automation of industrial operations. While horizontal and vertical interoperability serve as critical enablers of this ecosystem, heterogeneity among entities and the lack of standardized governance in interoperability allow cybercriminals to exploit structural vulnerabilities. These weaknesses and unknown bugs provide avenues for cyber-attackers to breach systems, conduct espionage, sabotage assets, and extort organizations, threatening IT and OT infrastructures, finances, reputations, and even human lives. This survey paper discusses cybersecurity and privacy threats within the Industry 4.0 and 5.0 ecosystems, their potential impact on industrial processes and peripherals, and the security challenges associated with the transition from Industry 4.0 to 5.0. To identify research gaps and vulnerabilities, we examine the architecture and components of diverse industrial frameworks and establish functional mappings using IIRA and RAMI models. Following a comprehensive threat modeling approach, we present a layered taxonomy of cyber-threats, classified based on their nature, behavior, and execution characteristics. To assist network administrators and security professionals, we propose a threat prioritization framework based on likelihood, detectability, impact severity, and operational consequences. Furthermore, we outline perspective-based cybersecurity challenges that expose deficiencies in current protective measures. As countermeasures, we advocate for AI-driven, blockchain-enabled, edge-computing-based, and privacy-preserving security solutions to defend against threats and mitigate potential damages. We also elaborate on key standardization initiatives, nation-specific privacy regulations, and ongoing research efforts focused on safeguarding the security and privacy of Industry 4.0/5.0.

Human error remains one of the most significant contributors to cybersecurity threats, leading to data breaches, system vulnerabilities, and financial loss. Thus, this paper examines causes and manifestations of human errors such as susceptibility to phishing attacks, analyzes prominent case studies in terms of their consequences and the operational, financial, and reputational risks associated with long recovery periods in addition to data loss. In response, we propose a structured framework for understanding and mitigating the impact of human error in cybersecurity systems. The framework is supported by mitigation strategies such as training, AI-assisted threat detection, and user-centered design, emphasizing the need for a holistic and layered approach to cybersecurity resilience.

Traditional perimeter-based security models, which implicitly trust internal networks, are vulnerable to modern cyber threats such as ransomware and credential misuse, allowing lateral movement and large-scale breaches. Zero Trust Architecture (ZTA) mitigates these risks by enforcing continuous authentication and least-privilege access for all devices. However, organizations face challenges in adopting ZTA without disrupting operations or incurring high costs, and existing research lacks actionable guidance. Through extensive literature analysis, semi-structured interviews with diverse organizational staff, and evaluation of Zero Trust maturity across seven pillars, this article proposes a practical, phased roadmap tailored to the needs of an organization. The approach prioritizes operational continuity and minimizes implementation barriers, enabling robust and sustainable cybersecurity.

The advancement of wireless networks towards sixth-generation (6G) and pervasive artificial intelligence (AI) underscores critical barriers to trustworthy cybersecurity, notably algorithmic bias, workforce underrepresentation, and the neglect of intersectional gender perspectives. Current cybersecurity research predominantly emphasizes algorithmic performance, often overlooking broader societal impacts such as equity and inclusivity. This paper addresses this gap by synthesizing interdisciplinary lessons from emerging technology deployments, critically examining risks inherent in AI-driven 6G security, and articulating the urgent need for an “Equity by Design” approach. We introduce a comprehensive roadmap to embed intersectional equity into cybersecurity processes—from policy-making and technical development to real-world deployment. Utilizing the BEiNG-WISE COST Action as a best-practice case study, we demonstrate how explicit gender-inclusive and participatory methodologies can effectively operationalize equity, transparency, and diversity. Our findings illustrate that integrating social and technical dimensions is essential for building secure, resilient, and equitable digital futures.

This paper gives a comprehensive analysis of effective cybersecurity education strategies for young internet users, with a focus on enhancing their awareness and ability to respond to online threats. By systematically examining a variety of educational programs from different institutions, this research identifies key pedagogical approaches that significantly improve cybersecurity knowledge and behaviors. In addition, the research emphasizes the importance of age-appropriate educational strategies that adapt to the cognitive development of learners, as well as the critical role of parental involvement in implementing stronger digital safety practices at home. The study also addresses challenges such as the rapidly evolving threat landscape and limited resources in educational institutions, while offering recommendations to overcome these barriers and foster a safer online environment for young users.

Reconfigurable production lines empowered by an intent-based approach and next-generation wireless networks can be a cornerstone of the transformative Industry 5.0 paradigm. This article presents a novel framework for developing an intent-based reconfigurable production line enriched with blockchain technology. By using blockchain as an enabler for decentralization, the proposed approach aims to integrate security and efficiency into the fabric of industrial automation. Experimental evaluations confirm the effectiveness of the proposed approach (in terms of scalability and latency) and highlight its strengths and potential for improvement. To be more precise, the blockchain systems are 85 to 95 seconds faster than the cloud service for 50 simultaneous transactions and offer around 69% lower latency at maximum utilization with 10 000 events. Important challenges such as integration into the existing infrastructure, security, standardization and energy efficiency are highlighted and recommendations for overcoming these hurdles are also given at the end of the article.

As the world prepares for the advent of 6G networks, quantum technologies are becoming critical enablers of the next generation of communication systems. This survey paper investigates the convergence of quantum technologies and 6G networks, focusing on their applications, opportunities and challenges. We begin with an examination of the motivations for integrating quantum technologies into 6G, investigating the potential to overcome the limits of classical computing and cryptography. We then highlight key research gaps, particularly in quantum communication, quantum computing integration and security enhancement. A comprehensive overview of quantum technologies relevant to 6G, including quantum communication devices, quantum computing paradigms, and hybrid quantum-classical approaches is provided. A particular focus is on the role of quantum technologies in enhancing 6G Radio Access Networks (RAN), 6G core and edge network optimization, and 6G security. The survey paper also explores the application of quantum cryptography with a focus on Quantum Key Distribution (QKD), Quantum Secure Direct Communication (QSDC) and quantum-resistant cryptographic algorithms and assesses their implementation challenges and potential impact on 6G networks. We also discuss the significant challenges associated with integrating quantum technologies into existing communications infrastructures, including issues of technological maturity, standardization, and economic considerations. Finally, we summarize the lessons learned from current research and outline future research directions to guide the ongoing development of quantum-enabled 6G networks.

The shift from fifth generation (5G) to sixth generation (6G) networks is anticipated to significantly advance network slicing. This progress is driven by the growing demand for next-generation applications and services. However, these advancements must be managed within the constraints of limited resources. This evolution opens up opportunities for resource sharing through emerging marketplaces, yet it introduces various business and technical complexities that need to be addressed. Additionally, finding cost-effective solutions is also essential for the future of networks. In this paper, we propose a blockchain-based architecture that utilizes non-fungible tokens (NFTs) for the trading of network resources within the 6G network slicing. To the best of our knowledge, this is the first study to represent network resources as NFTs within the context of network slicing. The architecture employs NFTs to authenticate and manage various network resources, providing a decentralized platform for their secure creation, management, and exchange. Using resource NFTs, our system ensures more granular and flexible control over network resources than existing state-of-the-art systems, where NFTs are tied to network slices. We implemented a prototype of this system to validate its viability. Our performance evaluation confirms that the proposed approach is efficient and cost-effective compared to baseline models in managing network resources within network slicing. These findings highlight the potential of our system to transform network management practices and effectively meet the demands of future networks.

As 6G networks introduce increasingly diverse and complex applications, network slicing is a key enabling technology for partitioning network resources to meet these dynamic demands. However, efficiently managing and allocating these finite resources has become vital. This necessity drives the adoption of an open marketplace model. To address the business and technical complexities associated with such open marketplaces, this paper presents the demonstration of a non-fungible token (NFT)-enabled resource trading marketplace tailored for 6G network slicing. The proposed solution is implemented on an Ethereum-based blockchain system to assess its viability.

Industrial automation is expected to benefit from the capabilities of Beyond 5G (B5G)/6G, particularly in improving real-time decision making and control in information-driven scenarios, supporting improved efficiency, precision and safety in connected manufacturing environments. While not all aspects of industrial automation are equally affected, certain areas such as real-time monitoring, wireless control and predictive maintenance can benefit significantly from the high reliability, ultra-low latency and scalability of next-generation wireless systems. Motivated by the increasing convergence between operational technology (OT) and advanced communication infrastructures, this paper surveys how B5G/6G can serve as an enabler for data-centric industrial ecosystems. The main contribution lies in analysing evolving industrial architectures, the integration of OT systems and application domains including digital twins, cloud robotics and smart devices. At the end of the paper, we also offer strategic insights into enabling technologies and future challenges, providing a comprehensive overview of how B5G/6G can support the evolution of Industry 5.0.

The Open Radio Access Network (RAN) represents a significant advancement in the ongoing evolution of mobile networks, transitioning from proprietary physical hardware to virtualised network functions. Open RAN advocates for a disaggregated RAN utilising commercial off-the-shelf (COTS) hardware. The O-RAN Alliance is the preeminent organisation in the Open RAN initiative, guiding the industry towards a vendor-neutral radio access network characterised by open interfaces and protocols. The introduction of RAN Intelligent Controllers (RICs) and the ability to deploy third-party services on these RICs expedite the innovation within the RAN. The two RICs, non-real-time RIC and near-real-time RIC, enhance the operation of RAN by facilitating the deployment of third-party services, either as an rApp for non-real-time RIC or as an xApp for near-real-time RIC. However, this new disaggregated and open RAN expands the threat surface and introduces novel security and privacy challenges that were previously absent, and these issues remain unaddressed. The introduction of new stakeholders, such as third-party application providers and cloud service providers, into the RAN ecosystem presents potential vulnerabilities. This paper proposes a hierarchical management strategy to tackle security challenges in Open RAN, enabling authorisation, authentication, and monitoring for third-party applications. Experimental evaluations across multiple configurations demonstrate that the proposed framework is scalable and imposes minimal overhead, making it a practical solution for securing next-generation RAN deployments.

In this paper, we experimentally demonstrate and analyze the performance of software-defined radio (SDR)-based hybrid optical wireless communication (OWC) links for indoor Internet-of-Things (IoT) networks. We propose all-optical hybrid visible light communication (VLC) and optical camera communication (OCC) using a single light emitting diode (LED) as a transmitter and photodiode (PD) and image sensor (IS) based receivers for simultaneous high-and low-speed data transmission. We present a testbed using an SDR platform, i.e., the ADALM-PLUTO, to transmit and receive high-and low-speed signals in the very-high frequency band and a media converter as part of an OWC link. We evaluate the performance of the proposed hybrid system employing a chip LED modulated with quadrature phase shift keying signal in terms of the bit error rate and the reception success rate Rrs. We show that the proposed scheme can provide an independent link performance, irrespective of the significant differences between the operating data rates of VLC and OCC links. The results show that for a link span of 1 m, we achieve (i) error-free detection for high-speed PD-based VLC; and (ii) 100 % Rrs at varying modulation frequencies for low-speed IS-based OCC.

Wearable communication is one of the key drivers in the Internet of Things (IoT) and body area networks for transmitting vital sensory data. Wearable devices must be lightweight, flexible, and have low energy consumption. In this context, optical wireless communication offers several significant advantages, such as immunity to the radio frequency-induced electromagnetic interference, broad unlicensed spectrum, and enhanced physical layer security. Wearable devices employing light-emitting diodes coupled to side-emitting optical fibers form energy-efficient, lightweight, flexible, and omnidirectional distributed optical transmitters, which are ideal for IoT applications. In this paper, we propose an optical camera communication (OCC)–based wearable system with a unique image processing algorithm capable of detecting and recovering data from distributed transmitters of arbitrary shapes. Experimental results demonstrate the feasibility of the proposed system, where for distances up to 4 m, the bit error rate (BER) performance is well below the forward error correction BER limit with values as low as 5.5×10−5. The presented results demonstrate the potential of OCC-based systems using side-emitting fibers for IoT applications.

This research presents a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model developed for malware classification from IoT devices in the SCADA system and for detecting anomalies in the network. The developed model identifies complex attacks in the network by taking advantage of the strengths of CNNs that reveal spatial features and LSTMs that detect temporal dependency. CICIoT 2023 is used as the dataset. ADAM optimization algorithm with cross-entropy loss is used to eliminate overfitting and training is performed. Within the scope of the study, the proposed model is compared by applying it with six deep learning architectures (hybrid CNN-LSTM, Non-Local Neural Network (NLNN), Residual Attention Network (RAN), Dual Attention Network (DANet), Transformer-CNN and Attentional CNN). The obtained results show that the proposed CNN-LSTM model outperforms other complex architectures and achieves a high test accuracy of 99.23%. It has demonstrated remarkable performance according to precision, recall and F1 evaluation metrics in detecting distributed denial of service (DDoS) and denial of service (DoS) attacks. The proposed model successfully identifies Mirai botnet variants and fragmentation-based attacks. Although other models, Transformer-CNN (98.81%) and DANet (98.07%), provide high performance, they fall behind the superior temporal modeling capabilities of CNN-LSTM. When the obtained findings are examined, they highlight the relative strengths of various deep learning approaches for IoT security applications. The performances of the implemented deep learning models reached accuracies exceeding 96%, demonstrating the importance of IoT-based SCADA systems against evolving cyber threats. The study revealed the superior successes of deep learning-based approaches for IoT security.

The rapid adoption of 5G networks, space networks, and Internet of Things (IoT) technologies in healthcare has significantly expanded the attack surface for cybersecurity threats. This evolving landscape demands robust defense mechanisms that can anticipate and neutralize sophisticated cyber-attacks. Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) technologies are pivotal in developing such advanced cybersecurity solutions. A critical factor influencing the effectiveness of these AI/ML models is the quality and diversity of the datasets used in their training. This paper presents a systematic review of various datasets used for AI/ML-based cybersecurity model training across multiple domains, with a focus on 5G networks, IoT healthcare, and space networks. By employing a structured Goal-Question-Metric (GQM) methodology and Quasi-Gold Standard (QGS) validation, we assessed the characteristics, applications, and limitations of real, synthetic, and hybrid datasets in enhancing cybersecurity measures. The review identifies key trends, gaps, and future research directions, highlighting the need for more diverse datasets, standardized benchmarks, and privacy-preserving techniques. Our findings offer insights into improving the resilience of AI/ML models for cybersecurity, guiding the development of more effective and adaptable defense strategies across emerging network technologies.

The main objective of this paper is to highlight the research directions and explain the main roles of current Artificial Intelligence (AI)/Machine Learning (ML) frameworks and available cloud infrastructures in building end-to-end ML lifecycle management for healthcare systems and sensitive biomedical data. We identify and explore the versatility of many genuine techniques from distributed computing and current state-of-the-art ML research, such as building cognition-inspired learning pipelines and federated learning (FL) ecosystem. Additionally, we outline the advantages and highlight the main obstacles of our methodology utilizing contemporary distributed secure ML techniques, such as FL, and tools designed for managing data throughout its lifecycle. For a robust system design, we present key architectural decisions essential for optimal healthcare data management, focusing on security, privacy and interoperability. Finally, we discuss ongoing efforts and future research directions to overcome existing challenges and improve the effectiveness of AI/ML applications in the healthcare domain.

The rapid evolution of heterogeneous applications signifies the requirement for network slicing to cater to diverse network requirements. Network Functions (NFs), which are the essential elements of network slices, are required to communicate with each other securely to facilitate network services. Certificates are the established method to authenticate each other. However, dynamic certificate management while allowing NFs to communicate in a multi-operator environment is arduous. Also, sharing NFs between network slices originates authorization-related security challenges such as unauthorized service utilization, deceptive Denial of Service attacks, and data leakages from network slices. In this paper, we develop a novel framework to address the security challenges related to authentication and authorization in 5G network slicing systems. A blockchain-based multi-party distributed certificate management framework with secure communication protocols is developed using elliptic curve cryptography to facilitate certificate services for multi-operator environments. Also, we propose a blockchain-based NF authorization framework to mitigate the security vulnerabilities in NF sharing between network slices. We implement the proposed framework using Hyperledger Fabric blockchain with Java chain codes and perform comprehensive experiments to show the significance of our framework.The Ability to mitigate the single point of failure with respect to state-of-the-art, including traditional certificate authorities and blockchain-based certificate authorities, time analysis for certificate generation, and the potential to eliminate the mentioned authorization attacks are some of the experiments conducted.Also, we have shown that our framework is secure using informal and formal (using Real-Or-Random (ROR) logic and Scyther Validation tool) security verification mechanisms.

The significant increase in the number of IoT devices has also brought with it various security concerns. The ability of these devices to collect a lot of data, including personal information, is one of the important reasons for these concerns. The integration of machine learning into systems that can detect security vulnerabilities has been presented as an effective solution in the face of these concerns. In this review, it is aimed to examine the machine learning algorithms used in the current studies in the literature for IoT network security. Based on the authors’ previous research in physical layer security, this research also aims to investigate the intersecting lines between upper layers of security and physical layer security. To achieve this, the current state of the area is presented. Then, relevant studies are examined to identify the key challenges and research directions as an initial overview within the authors’ ongoing project.