Search Results (261)

Autonomous vehicles (AVs) have emerged as a promising sustainable urban mobility solution, expected to lead to enhanced road safety, smoother traffic flows, less traffic congestion, improved accessibility, better energy utilization and environmental performance, as well as more efficient passenger and freight transportation, in terms of time and cost, due to better fleet management and platooning. However, challenges also arise, mostly related to data privacy, security and cyber-security, high acquisition and infrastructure costs, accident liability, even possible increased traffic congestion and air pollution due to induced travel demand. This paper presents the results of a survey conducted among 654 residents who experienced an autonomous bus (AB) service in the city of Trikala, Greece, in order to assess their willingness to use (WTU) and willingness to pay (WTP) for ABs, through testing a range of factors based on a literature review. Results useful to policy-makers were extracted, such as that the intention to use ABs was mostly shaped by psychological factors (e.g., users’ perceptions of usefulness and safety, and trust in the service provider), while WTU seemed to be positively affected by previous experience in using ABs. In contrast, sociodemographic factors were found to have very little effect on the intention to use ABs, while apart from personal utility, users’ perceptions of how autonomous driving will improve the overall life standards in the study area also mattered. Full article

The integration of digital twins (DTs) with intelligent traffic systems (ITSs) holds strong potential for improving real-time management in smart cities. However, securing digital twins remains a significant challenge due to the dynamic and adversarial nature of cyber–physical environments. In this work, we propose TwinFedPot, an innovative digital twin-based security architecture that combines honeypot-driven data collection with Zero-Shot Learning (ZSL) for robust and adaptive cyber threat detection without requiring prior sampling. The framework leverages Inverse Federated Distillation (IFD) to train the DT server, where edge-deployed honeypots generate semantic predictions of anomalous behavior and upload soft logits instead of raw data. Unlike conventional federated approaches, TwinFedPot reverses the typical knowledge flow by distilling collective intelligence from the honeypots into a central teacher model hosted on the DT. This inversion allows the system to learn generalized attack patterns using only limited data, while preserving privacy and enhancing robustness. Experimental results demonstrate significant improvements in accuracy and F1-score, establishing TwinFedPot as a scalable and effective defense solution for smart traffic infrastructures. Full article

The evolution of Industry 4.0 and the emerging paradigm of Industry 5.0 have introduced disruptive technologies that are reshaping modern manufacturing environments. Among these, Digital Twin (DT) and Industrial Metaverse (IM) technologies are increasingly recognized for their potential to enhance Occupational Health and Safety (OHS). However, a comprehensive understanding of how these technologies integrate to support OHS in manufacturing remains limited. This study systematically explores the transformative role of DT and IM in creating immersive, intelligent, and human-centric safety ecosystems. Following the PRISMA guidelines, a Systematic Literature Review (SLR) of 75 peer-reviewed studies from the SCOPUS and Web of Science databases was conducted. The review identifies key enabling technologies such as Virtual Reality (VR), Augmented Reality (AR), Extended Reality (XR), Internet of Things (IoT), Artificial Intelligence (AI), Cyber-Physical Systems (CPS), and Collaborative Robots (COBOTS), and highlights their applications in real-time monitoring, immersive safety training, and predictive hazard mitigation. A conceptual framework is proposed, illustrating a synergistic digital ecosystem that integrates predictive analytics, real-time monitoring, and immersive training to enhance the OHS. The findings highlight both the transformative benefits and the key adoption challenges of these technologies, including technical complexities, data security, privacy, ethical concerns, and organizational resistance. This study provides a foundational framework for future research and practical implementation in Industry 5.0. Full article

Cyber threat intelligence (CTI) has become critical in enhancing cybersecurity measures across various sectors. This systematic review aims to synthesize the current literature on the effectiveness of CTI strategies in mitigating cyber attacks, identify the most effective tools and methodologies for threat detection and prevention, and highlight the limitations of current approaches. An extensive search of academic databases was conducted following the PRISMA guidelines, including 43 relevant studies. This number reflects a rigorous selection process based on defined inclusion, exclusion, and quality criteria and is consistent with the scope of similar systematic reviews in the field of cyber threat intelligence. This review concludes that while CTI significantly improves the ability to predict and prevent cyber threats, challenges such as data standardization, privacy concerns, and trust between organizations persist. It also underscores the necessity of continuously improving CTI practices by leveraging the integration of advanced technologies and creating enhanced collaboration frameworks. These advancements are essential for developing a robust and adaptive cybersecurity posture capable of responding to an evolving threat landscape, ultimately contributing to a more secure digital environment for all sectors. Overall, the review provides practical reflections on the current state of CTI and suggests future research directions to strengthen and improve CTI’s effectiveness. Full article

Large Language Models (LLMs) have emerged as powerful tools in cyber security, enabling automation, threat detection, and adaptive learning. Their ability to process unstructured data and generate context-aware outputs supports both operational tasks and educational initiatives. Despite their growing adoption, current research often focuses on isolated applications, lacking a systematic understanding of how LLMs align with domain-specific requirements and pedagogical effectiveness. This highlights a pressing need for comprehensive evaluations that address the challenges of integration, generalization, and ethical deployment in both operational and educational cyber security environments. Therefore, this paper provides a comprehensive and State-of-the-Art review of the significant role of LLMs in cyber security, addressing both operational and educational dimensions. It introduces a holistic framework that categorizes LLM applications into six key cyber security domains, examining each in depth to demonstrate their impact on automation, context-aware reasoning, and adaptability to emerging threats. The paper highlights the potential of LLMs to enhance operational performance and educational effectiveness while also exploring emerging technical, ethical, and security challenges. The paper also uniquely addresses the underexamined area of LLMs in cyber security education by reviewing recent studies and illustrating how these models support personalized learning, hands-on training, and awareness initiatives. The key findings reveal that while LLMs offer significant potential in automating tasks and enabling personalized learning, challenges remain in model generalization, ethical deployment, and production readiness. Finally, the paper discusses open issues and future research directions for the application of LLMs in both operational and educational contexts. This paper serves as a valuable reference for researchers, educators, and practitioners aiming to develop intelligent, adaptive, scalable, and ethically responsible LLM-based cyber security solutions. Full article

The rapid rise of AI-driven cybercrime and deepfake fraud poses complex organisational challenges for US law enforcement, particularly the Federal Bureau of Investigation (FBI). Applying Maguire’s (2003) police organisation theory, this qualitative single-case study analyses the FBI’s structure, culture, technological integration, and inter-agency collaboration. Findings underscore the organisational strengths of the FBI, including a specialised Cyber Division, advanced detection tools, and partnerships with agencies such as the Cybersecurity and Infrastructure Security Agency (CISA). However, constraints, such as resource limitations, detection inaccuracies, inter-agency rivalries, and ethical concerns, including privacy risks associated with AI surveillance, hinder operational effectiveness. Fragmented global legal frameworks, diverse national capacities, and inconsistent detection of advanced deepfakes further complicate responses to this issue. This study proposes the establishment of agile task forces, public–private partnerships, international cooperation protocols, and ethical AI frameworks to counter evolving threats, offering scalable policy and technological solutions for global law enforcement. Full article

Data privacy is a crucial concern for individuals using cloud storage services, and cloud service providers are increasingly focused on meeting this demand. However, privacy breaches in the ever-evolving cyber landscape remain a significant threat to cloud storage infrastructures. Previous studies have aimed to address this issue but have often lacked comprehensive coverage of privacy attributes. In response to the identified gap in privacy-preserving techniques for cloud computing, this research paper presents a novel and adaptable framework. This approach introduces a multi-layer encoding storage arrangement combined with the implementation of a one-time password authorization approach. By integrating these elements, the proposed approach aims to enhance both the flexibility and efficiency of data protection in cloud environments. The findings of this study are anticipated to have significant implications, contributing to the advancement of existing techniques and inspiring the development of innovative research-driven solutions. Continuous research efforts are required to validate the effectiveness of the proposed framework across diverse contexts and assess its performance against evolving privacy vulnerabilities in cloud computing. Full article

Cyber threats are growing in scale and complexity, outpacing the capabilities of traditional security systems. Machine learning (ML) models offer enhanced detection accuracy but often rely on centralized data, raising privacy concerns. Federated learning (FL), by contrast, enables decentralized model training but suffers from scalability and latency issues. Hybrid AI models, which integrate ML and FL techniques, have emerged as a promising solution to balance performance, privacy, and scalability in cybersecurity. This systematic review investigates the current landscape of hybrid AI models, evaluating their strengths and limitations across five key dimensions: accuracy, privacy preservation, scalability, explainability, and robustness. Findings indicate that hybrid models consistently outperform standalone approaches, yet challenges remain in real-time deployment and interpretability. Future research should focus on improving explainability, optimizing communication protocols, and integrating secure technologies such as blockchain to enhance real-world applicability. Full article

We introduce TrustShare, a novel blockchain-based framework designed to enable secure, privacy-preserving, and trust-aware cyber threat intelligence (CTI) sharing across organizational boundaries. Leveraging Hyperledger Fabric, the architecture supports fine-grained access control and immutability through smart contract-enforced trust policies. The system combines Ciphertext-Policy Attribute-Based Encryption (CP-ABE) with temporal, spatial, and controlled revelation constraints to grant data owners precise control over shared intelligence. To ensure scalable decentralized storage, encrypted CTI is distributed via the IPFS, with blockchain-anchored references ensuring verifiability and traceability. Using STIX for structuring and TAXII for exchange, the framework complies with the GDPR requirements, embedding revocation and the right to be forgotten through certificate authorities. The experimental validation demonstrates that TrustShare achieves low-latency retrieval, efficient encryption performance, and robust scalability in containerized deployments. By unifying decentralized technologies with cryptographic enforcement and regulatory compliance, TrustShare sets a foundation for the next generation of sovereign and trustworthy threat intelligence collaboration. Full article

Optimizing ambulance deployment is a critical task in emergency medical services (EMS), as it directly affects patient outcomes and system efficiency. This study proposes a cyber-secure, machine learning-based framework for predicting region-specific ambulance allocation and response times across South Korea. The model integrates heterogeneous datasets—including demographic profiles, transportation indices, medical infrastructure, and dispatch records from 229 EMS centers—and incorporates real-time IoT streams such as traffic flow and geolocation data to enhance temporal responsiveness. Supervised regression algorithms—Random Forest, XGBoost, and LightGBM—were trained on 2061 center-month observations. Among these, Random Forest achieved the best balance of accuracy and interpretability (MSE = 0.05, RMSE = 0.224). Feature importance analysis revealed that monthly patient transfers, dispatch variability, and high-acuity case frequencies were the most influential predictors, underscoring the temporal and contextual complexity of EMS demand. To support policy decisions, a Lasso-based simulation tool was developed, enabling dynamic scenario testing for optimal ambulance counts and dispatch time estimates. The model also incorporates the coefficient of variation (CV) of workload intensity as a performance metric to guide long-term capacity planning and equity assessment. All components operate within a cyber-secure architecture that ensures end-to-end encryption of sensitive EMS and IoT data, maintaining compliance with privacy regulations such as GDPR and HIPAA. By integrating predictive analytics, real-time data, and operational simulation within a secure framework, this study offers a scalable and resilient solution for data-driven EMS resource planning. Full article

Mobility as a Service (MaaS) is anticipated to revolutionize transport by integrating conventional public transport with on-demand and shared services. This innovation promises enhanced convenience, flexibility, and sustainability in urban mobility, drawing interest from both researchers and industry. However, those systems heavily rely on the collection and sharing of personal data among various stakeholders, introducing security and privacy risks. To understand the scale and scope of cyber security and privacy concerns and risks associated with MaaS, we conducted a systematic literature review (SLR) covering 87 relevant research papers published between 2017 and April 2025. Our review represents the most comprehensive examination focusing on cyber security and privacy issues of MaaS systems. Our findings reveal three themes discussed within the MaaS literature: (i) cyber security and privacy risks inherent to MaaS systems, alongside proposed solutions to mitigate such risks; (ii) users’ concerns about these risks and how they affect MaaS adoption; and (iii) laws and policies that govern cyber security and privacy aspects of MaaS systems and solutions. As such, our research serves to not only inform MaaS service providers and users but also advise policymakers and legislators on the potential risks involved and the regulatory measures required to address them. Full article

The growing number of cyber threats and the implementation of stringent privacy regulations have revealed significant shortcomings in traditional centralized machine learning models, especially in distributed systems like the Internet of Things (IoT). This study presents a Federated Learning (FL) framework designed for intrusion detection and malware classification. This framework enables decentralized model training while preserving data locality and minimizing communication overhead. The proposed architecture incorporates lightweight, privacy-preserving techniques, including gradient clipping, differential privacy, and encrypted model aggregation, to ensure secure and efficient collaboration across heterogeneous clients. Experimental results on two widely adopted cybersecurity benchmarks demonstrate that the framework achieves detection accuracies above 90%, maintains privacy loss below 5%, and improves communication efficiency by over 25%. These results confirm the viability of FL as a scalable, privacy-compliant approach for next-generation cybersecurity systems in highly distributed infrastructures. Full article

Cyber threats that target Internet of Things (IoT) and edge computing environments are growing in scale and complexity, which necessitates the development of security solutions that are both robust and scalable while also protecting privacy. Edge scenarios require new intrusion detection solutions because traditional centralized intrusion detection systems (IDSs) lack in the protection of data privacy, create excessive communication overhead, and show limited contextual adaptation capabilities. This paper introduces the SecFedDNN framework, which combines federated deep learning (FDL) capabilities to protect edge–cloud environments from cyberattacks such as Distributed Denial of Service (DDoS), Denial of Service (DoS), and injection attacks. SecFedDNN performs edge-level pre-aggregation filtering through Layer-Adaptive Sparsified Model Aggregation (LASA) for anomaly detection while supporting balanced multi-class evaluation across federated clients. A Deep Neural Network (DNN) forms the main model that trains concurrently with multiple clients through the Federated Averaging (FedAvg) protocol while keeping raw data local. We utilized Google Cloud Platform (GCP) along with Google Colaboratory (Colab) to create five federated clients for simulating attacks on the TON_IoT dataset, which we balanced across selected attack types. Initial tests showed DNN outperformed Long Short-Term Memory (LSTM) and SimpleNN in centralized environments by providing higher accuracy at lower computational costs. Following federated training, the SecFedDNN framework achieved an average accuracy and precision above 84% and recall and F1-score above 82% across all clients with suitable response times for real-time deployment. The study proves that FDL can strengthen intrusion detection across distributed edge networks without compromising data privacy guarantees. Full article

As cyber–physical systems are applied not only to crucial infrastructure but also to day-to-day technologies, from industrial control systems through to smart grids and medical devices, they have become very significant. Cyber–physical systems are a target for various security attacks, too; their growing complexity and digital networking necessitate robust cybersecurity solutions. Recent research indicates that deep learning can improve CPS security through intelligent threat detection and response. We still foresee limitations to scalability, data privacy, and handling the dynamic nature of CPS environments in existing approaches. We developed the CPS ShieldNet Fusion model as a comprehensive security framework for protecting CPS from ever-evolving cyber threats. We will present a model that integrates state-of-the-art methodologies in both federated learning and optimization paradigms through the combination of the Federated Residual Convolutional Network (FedRCNet) and the EEL-Levy Fusion Optimization (ELFO) methods. This involves the incorporation of the Federated Residual Convolutional Network into an optimization method called EEL-Levy Fusion Optimization. This preserves data privacy through decentralized model training and improves complex security threat detection. We report the results of a rigorous evaluation of CICIoT-2023, Edge-IIoTset-2023, and UNSW-NB datasets containing the CPS ShieldNet Fusion model at the forefront in terms of accuracy and effectiveness against several threats in different CPS environments. Therefore, these results underline the potential of the proposed framework to improve CPS security by providing a robust and scalable solution to current problems and future threats. Full article

This scoping review examines the evolving landscape of European Union (EU) legislation, as it pertains to the implementation of artificial intelligence (AI) in smart grid systems. By outlining the current regulatory landscape, including the General Data Protection Regulation (GDPR), the EU Artificial Intelligence Act, the EU Data Act, the EU Data Governance Act, the ePrivacy framework, the Network and Information Systems (NIS2) Directive, the EU Cyber Resilience Act, the EU Network Code on Cybersecurity for the electricity sector, and the EU Cybersecurity Act, it highlights both constraints and opportunities for stakeholders, including energy utilities, technology providers, and end-users. The analysis delves into regulatory barriers such as data protection requirements, algorithmic transparency mandates, and liability concerns that can limit the scope and scale of AI deployment. Technological challenges are also addressed, ranging from the integration of distributed energy resources and real-time data processing to cybersecurity and standardization issues. Despite these challenges, this review emphasizes how compliance with EU laws may ultimately boost consumer trust, promote ethical AI usage, and streamline the roll-out of robust, scalable smart grid solutions. The paper further explores stakeholder benefits, including enhanced grid stability, cost reductions through automation, and improved sustainability targets aligned with the EU’s broader energy and climate strategies. By synthesizing these findings, the review offers insights into policy gaps, technological enablers, and collaborative frameworks critical for accelerating AI-driven innovation in the energy sector, helping stakeholders navigate a complex regulatory environment while reaping its potential rewards. Full article