Search Results (3,737)

Global decarbonization and the rapid growth of distributed energy resources increase the need for information-centric mechanisms that can support secure, scalable, cross-border coordination under heterogeneous technical and regulatory conditions. This paper proposes a Global Virtual Prosumer (GVP) framework that integrates IoT sensing, multi-agent coordination, and permissioned blockchain smart contracts to operationalize cross-border energy services as auditable service commitments rather than physical power exchange. Building on prior work that validated MAS-based power management and blockchain-secured operation within individual Virtual Prosumers, the present contribution lies in the cross-border coordination layer and its associated contractual and evaluation mechanisms, not in the constituent technologies themselves. A layered IoT–AI–blockchain architecture is introduced, where off-chain optimization produces allocations and admissibility indicators and on-chain contracts enforce identity, feasibility guards, delegation and partner-assignment rules, oracle verification, and settlement time compliance outcomes. The contractual lifecycle is formalized through four smart-contract algorithms covering trade registration, conditional delegation, cooperative fulfillment, and cross-border settlement with explicit failure semantics and event-based audit trails. The framework is evaluated on a global case study with seven Virtual Prosumers and quantified using contract-centric KPIs that capture registration time rejections, settlement success versus non-compliance, oracle-driven failure attribution, and full lifecycle traceability. The results demonstrate internal consistency of the proposed lifecycle and the practical value of KPI-driven accountability for cross-border energy service coordination. At the same time, the evaluation is based on synthetic parameterization and an emulated contract environment; realistic deployment constraints—including consensus latency, cross-region communication reliability, and regulatory overlap—are discussed as explicit limitations and directions for future empirical validation. Full article

The rapid diffusion of industry 4.0 technologies has substantially transformed the maritime transportation sectors by enabling data-driven operations, enhanced connectivity, and more intelligent decision-making processes. Digital technologies such as the Internet of Things (IoT), simulation systems, and advanced data analytics are increasingly reshaping operational structures in maritime logistics, positioning technological transformation as a strategic priority for firms. However, the weighting and prioritization of components emerging with industry 4.0 technologies remain an underexplored area in the literature. The primary motivation of this study is to determine the weights of these industry 4.0 components using the Bayesian Best Worst Method (BWM) and to reveal their corresponding credal ranking levels. In this context, the present study aims to evaluate and prioritize the critical industry 4.0 components influencing technological transformation processes using the Bayesian BWM. Bayesian BWM is preferred over alternative Multi Criteria Decision Making (MCDM) approaches due to its ability to explicitly model uncertainty within a probabilistic framework, generate more consistent weighting results, and flexibly incorporate decision-makers’ judgments. The findings reveal that safety and security (0.2945) constitute the most influential main component, underscoring the necessity of robust digital infrastructures and reliable systems within highly digitalized operational environments. Among the sub-components, data privacy (0.1301) demonstrates the highest global weight, highlighting the growing importance of safeguarding sensitive information in data-intensive digital systems. The results further indicate that autonomous operation and coordination play significant roles in facilitating efficient digital operations, particularly through real-time equipment monitoring and IoT-based operational visibility. Moreover, sustainability (0.1968) emerges as the second most important component, suggesting that organizations increasingly assess technological investments not only in terms of operational efficiency but also with respect to long-term resilience. Within this dimension, continuous training (0.0614) is identified as the most influential component, indicating that the success of digital transformation depends not only on technological infrastructure but also on the development of human capabilities. With the increasing digitalization of the maritime industry, protection against cyber threats has become essential for ensuring operational continuity and safeguarding data integrity. In this regard, adopting proactive cybersecurity strategies and continuously monitoring and updating systems are of critical importance. In the digital transformation of maritime transportation, integrating sustainability considerations is essential to ensure long-term operational efficiency and environmental responsibility. These practical implications are particularly relevant for policymakers, port authorities, and shipping companies seeking to enhance both digital capabilities and sustainable performance. Full article

The proliferation of Internet of Things (IoT) devices has created unprecedented challenges in cybersecurity, as billions of interconnected devices generate, process, and transmit sensitive data across diverse networks. This study addresses critical security vulnerabilities in IoT ecosystems, focusing on the development of a comprehensive security framework that encompasses device authentication, an attribute access control mechanism, and privacy preservation. This work introduces HADA, a proposed hybrid authentication method that combines the validation of unique credentials and trust value. For the authentication of the data owner and user, the following credentials are validated: identity, certificate, reconfigurable physical unclonable function (PUF), and trust. Differential privacy is used to secure the credentials during information exchange. Then, the newly developed dynamic attribute access control method selects the number of attributes and matches the attributes; these two processes are performed using the Bi-Fuzzy logic and graph neural network (GNN) algorithms, respectively. After matching the data, the user is allowed to access them from the cloud server. For data encryption, the lightweight SKINNY algorithm is implemented in Hyperledger Fabric blockchain. The proposed system performs better than existing methods in terms of throughput, latency, and resource utilization. Full article

Smart vehicles integrated with the Internet of Things (IoT) provide rich data for traffic management, safety, and liability services; however, existing blockchain-enabled vehicular architectures still struggle with consensus scalability, heavy centralized validation, limited interaction-based corroboration, incomplete attack coverage, and rapid ledger growth. In particular, many schemes either optimize single-layer consensus or embed detailed reputation information into every transaction, while pushing most validation to central servers. This leads to bottlenecks under dense traffic and leaves replay, Sybil-assisted 51% attacks on roadside units (RSUs), and man-in-the-middle tampering only partially addressed. In this context, this paper proposes a novel hierarchical blockchain for vehicular IoT (HBV-IoT) model to address the above challenges. An independent transaction for periodic vehicle status reporting and an interaction-based transaction for corroborating data between vehicles in proximity are presented. Three smart contracts are designed to automate the validation and processing of transactions, and to identify compromised or malicious vehicles within the HBV-IoT network. Algorithms for distributed consensus to accept transactions into the blockchain and for vehicle reputation management to enforce edge-level filtering and down-weighting of malicious nodes are implemented. Simulation results demonstrate significant improvements compared to conventional vehicular blockchain approaches, with performance gains validated by 95% confidence intervals. The model supports practical applications, including real-time traffic monitoring, automated e-challan issuance, intelligent insurance claim processing, and blockchain-based vehicle registration. Full article

This paper investigates the feasibility of using Large Language Models (LLMs) for the structural analysis of flow-based network data. This analysis is carried out in the presence of a structural difference between the multidimensional numerical space of IoT features and the symbolic space in which LLMs operate. The primary objective was the development of a formal framework that enables the controlled transformation of numerical data into linguistically analyzable semantic representations, without resorting to classification or machine learning mechanisms. We propose the Semantic Flow Encoding (SFE) mechanism, a deterministic method for robust discretization and behavioral abstraction that converts the numerical characteristics of Internet of Things (IoT) flows into structural semantic descriptions using the Canadian Institute for Cybersecurity Internet of Things Device Identification and Anomaly Detection (CIC IoT-DIAD) 2024 dataset. Through formal informational measures, it is demonstrated that the existence of an intrinsic structural difference between benign and DDoS traffic in the analyzed dataset. In the validation stage, we evaluated whether these informational differences are reflected at the level of linguistic abstraction through controlled inference experiments in IBM WatsonX. The present paper suggests that LLMs may support semantic auditing of distributional structure when guided by a formal encoding layer. In this manner, a reproducible framework for integrating numerical security data into language-model-based analysis is suggested. Full article

The proliferation of sensor networks in critical infrastructure, healthcare monitoring, and smart city applications demands robust privacy-preserving mechanisms for data verification. Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) offer a promising cryptographic primitive that enables data integrity verification without revealing sensitive sensor readings. However, the practical feasibility of deploying zk-SNARKs in resource-constrained sensor network environments remains insufficiently characterized. This paper presents a systematic benchmarking study of the Groth16 zk-SNARK protocol across eight representative circuit types spanning six orders of magnitude in computational complexity, from basic arithmetic operations (1 constraint) to ECDSA signature verification (1,510,185 constraints). Using an automated open-source benchmarking framework built on the Circom-snarkjs toolchain, we conducted 160 statistically controlled measurements (20 iterations per circuit) with cold/warm separation, collecting proof generation time, verification time, proof size, memory consumption, and witness generation overhead. Our results demonstrate that Groth16 proofs maintain a constant size of $804.7\pm 1.7$ bytes and near-constant verification time of $0.662\pm 0.032$ s regardless of circuit complexity, with coefficients of variation below 5% across all circuit types. Proof generation time exhibits sub-linear scaling ($\alpha =0.256$, $R^2=0.608$), with statistically significant differences between circuit categories confirmed by one-way ANOVA ($F=355.0$, $p<{10}^{-79}$, ${\eta }^2=0.94$). We identify three operational deployment tiers for sensor network architectures and estimate energy budgets for battery-powered devices. These findings provide actionable guidance for the design of privacy-preserving data verification systems in next-generation sensor networks. Full article

As industries deploy more connected devices in factories, warehouses, and smart facilities, the need for artificial intelligence (AI) systems that can operate securely in distributed, data-intensive environments is growing. Traditional centralized learning and online education platforms struggle when students and systems have to process real-time streams (sensors, video, text) with strict latency and privacy requirements. To address this challenge, a blockchain-secured, edge-enabled multimodal federated learning framework tailored for Industrial IoT (IIoT) environments is proposed. The model integrates four key layers: (i) a blockchain layer that provides credentialing, transparency, and token-based incentives; (ii) a multimodal community layer that supports group formation, peer consensus, and cross-modal learning across text, images, audio, and sensor data; (iii) an edge computing layer that enables low-latency task offloading and secure training within Intel SGX enclaves; and (iv) a data layer that applies pre-processing, differential privacy, and synthetic augmentation to safeguard sensitive information. Experiments on industrial multimodal datasets demonstrate 42% faster model aggregation, 78.9% multimodal accuracy, and 1.9% accuracy loss under ε = 1.0 differential privacy. This shows a scalable and practical path for decentralized AI training in next-generation IIoT systems, confirming the possibility of technical support for educational processes. However, the conducted research requires a validation of pedagogical effectiveness. Full article

Slowloris is a well-known application-layer Denial of Service (DoS) attack that is challenging to detect due to its low-rate nature, allowing it to blend with legitimate traffic and remain unnoticed. Our hypothesis is that deliberate prolongation of the pre-authentication stage in stateful protocols induces unnecessary CPU utilization. In this study, we repurpose Slowloris as an energy-oriented (E-DoS) attack that exploits pre-authentication statefulness of the most prevalent remote access protocols, the Secure Shell Protocol (SSH) and File Transfer Protocol (FTP). We employ a Raspberry Pi-based experimental setup with different software implementations of the mentioned protocols to validate our hypothesis. Our experiments confirm the susceptibility of SSH and FTP to Slowloris E-DoS attacks, and we quantify the consequential impact on power consumption. We find that the Slowloris E-DoS attack exhibits an asymmetrical nature, causing a disproportionate computational demand on victim systems compared to the resources invested by the attacker. The results of this study indicate that battery-powered single-board computers (SBCs) are critically affected by these attacks due to their limited power availability. This research demonstrates the importance of understanding and mitigating Slowloris E-DoS vulnerabilities in the SSH and FTP protocols, offering valuable insights for enhancing security measures. Our findings show that millions of SBCs worldwide may be at risk and highlight a deeper structural weakness: the stateful design of widely deployed protocols can turn service availability into an energy liability. This systemic risk extends beyond SSH and FTP, with implications for IoT devices and backends that depend on stateful communication protocols. Full article

Large-scale smart city Internet of Things (IoT) infrastructures must simultaneously provide strong cybersecurity protection, real-time anomaly detection, and energy-efficient operation despite the strict resource limitations of sensing devices. The current body of research typically addresses secure data management, edge intelligence, or energy optimization in isolation, leaving a practical gap in unified frameworks that jointly optimize these objectives. This paper proposes a jointly co-designed energy-aware cybersecurity framework that integrates lightweight secure sensing, hybrid edge-based anomaly detection, Practical Byzantine Fault Tolerance (PBFT)-enabled blockchain integrity, and Grey Wolf Optimization (GWO)-driven edge deployment within a single end-to-end architecture. The practical contribution of the proposed framework lies in enabling tamper-evident trusted sensing, real-time detection of both data and energy anomalies, and communication-efficient operation suitable for scalable smart city deployments. The simulation results demonstrate that the proposed method achieves strong operational efficiency, reaching up to 234.6 transactions per second while maintaining end-to-end latency of approximately 140–194 ms and reducing total energy consumption to about 1.68 J under high-load conditions. In addition, the hybrid anomaly detection mechanism achieves an F1-score of 0.985 and ROC-AUC of 0.992, confirming strong detection capability under realistic sensing and attack scenarios. Full article

Smart-city IoT ecosystems depend on a large number of devices with limited resources, which often lack built-in security mechanisms. While traditional cloud-based or gateway-centric intrusion detection systems (IDSs) offer essential security, they are still characterized by high detection latency, considerable bandwidth demand, and a lack of precise monitoring of single device actions. This study proposes the Edge AI Bridge, a novel micro-computing security layer positioned between IoT devices and the gateway to enable early-stage threat interception. The architecture integrates embedded AI hardware with a hybrid pipeline, utilizing unsupervised anomaly detection for behavioral profiling and a lightweight signature-matching module to minimize false positives. System operations—including localized traffic inspection, protocol parsing, and feature extraction—are performed before data aggregation, which preserves device-level privacy and reduces the computational burden on the IoT gateway. The contemporary CIC-IoT-2023 dataset, which captures a wide range of smart-city protocols and attack vectors, is used to evaluate the architecture. The Edge AI Bridge leads to a significant reduction in detection latency—≈50 ms on average as opposed to the 500 ms of cloud-based solutions—while the resource footprint is kept low to about 20% CPU utilization. The Edge AI Bridge demonstrates a potential solution that is scalable, modular, and can preserve privacy while improving the cyber resilience of the smart-city infrastructures that are large, heterogeneous, and difficult to manage. Full article

Smart cities are increasingly dependent on interconnected transportation systems; however, this connectivity exposes smart mobility networks to significant cybersecurity risks. Traditional Intrusion Detection Systems are ill-equipped for this environment, as they are designed for isolated systems or fixed network boundaries. Thus, they struggle to secure the complex and heterogeneous smart mobility networks, where various protocols and resource-constrained edge devices require more adaptive solutions. To address this limitation, we propose a novel hybrid deep learning framework that combines convolutional neural networks for spatial feature extraction, long short-term memory networks for temporal pattern recognition, and an attention mechanism for adaptive feature weighting, together forming a context-aware Intrusion Detection System. Our approach is evaluated across six benchmark datasets spanning vehicular networks, IoT ecosystems, cloud computing, and 5G environments—VeReMi Extension, CICIoV2024, Edge-IIoTset, UNSW-NB15, Car Hacking, and 5G-NIDD—a deliberately diverse selection that represents the heterogeneous nature of real-world smart mobility networks. Empirical evaluation using three different random seeds reveals the proposed framework achieves detection accuracy exceeding 98% on each dataset, a mean F1 score of 98.94%, and an inference latency of just 4.96 ms per sample. Our results show that the proposed model achieves consistently high detection performance across six heterogeneous benchmark datasets, making it a potentially robust candidate for real-time intrusion detection in smart mobility systems. Full article

Classical approaches to cryptography exhibit several limitations when applied to scenarios involving more than two users. The One-Time User Key (OTUK) meta-cryptographic model addresses these limitations by enabling multi-user encryption that is flexible, applicable to any cryptographic algorithm, and designed for systematic deployment without compromising system security. Each user possesses an individual key from which One-Time keys are derived; these keys feed a secret-sharing function ($\omega$) that establishes the multi-user encrypted channel. In this paper, we present a polynomial-based implementation of the $\omega$ function under a $(1,n)$ threshold model. The generated polynomial has roots at points corresponding to valid user keys and is mapped to the real encryption key. We provide a formal threat model, pseudocode for the complete protocol, and a detailed computational analysis across the numerical domains $\mathbb{N}$, $\mathbb{Z}$, and $\mathbb{R}$. Furthermore, we present experimental benchmarks measuring encryption/decryption speed, scalability up to 30 users, parameter sensitivity, and a comparative evaluation against Shamir’s Secret Sharing scheme. A systematic security analysis examines partial-information attacks, derivative-root distance margins, and brute-force resistance, demonstrating that the effective security margin remains above 245 bits for configurations of up to 30 users with 256-bit keys. The proposed method offers a concrete, efficient, and secure foundation for multi-user encrypted communication in domains such as IoT, public administration, and e-health. Full article

The pervasive adoption of Internet of Things (IoT) devices has profoundly reshaped digital connectivity by enabling real-time data exchange and autonomous interactions on a global scale. While this transformation presents substantial operational benefits, it simultaneously introduces significant security challenges, especially in terms of Identity and Access Management (IAM) for non-human entities, such as sensors, devices, machine agents, and service accounts. Historically, traditional perimeter-based security models, which depend on static trust boundaries and implicit trust for internal actors, have been applied to human identities. However, these models prove inadequate for managing non-human identities. This inadequacy has spurred interest in Zero Trust Architecture (ZTA), an advanced security paradigm based on the principle of “never trust, always verify.” This paper examines the application of ZTA in safeguarding IoT ecosystems, with a particular emphasis on managing non-human identities. The study delves into ZTA’s fundamental principles, such as least privilege, micro-segmentation, continuous monitoring, and identity-centric access control, and evaluates their effective implementation in resource-constrained IoT settings. The research identifies critical implementation challenges and considerations for applying identity-based ZTA within IoT contexts. The findings of this paper underscore that ZTA, when meticulously implemented, provides a robust framework for mitigating the cyber risks inherent in IoT ecosystems. Furthermore, the paper delineates prospective research avenues aimed at integrating ZTA into IoT environments. Ultimately, this study contributes to the expanding body of scholarly knowledge by endorsing Zero Trust as a foundational strategy for contemporary IoT security. Full article

Quantum computing challenges the long-term security assumptions of blockchain systems that rely on classical public-key cryptography, motivating the adoption of post-quantum cryptography and quantum key distribution (QKD). This review maps research fronts at the intersection of blockchain and quantum-safe security, linking threat assumptions to post-quantum mechanisms, blockchain layers, and QKD positioning. Records were retrieved from Scopus and Web of Science using a two-block query and filtered through a PRISMA-guided workflow for bibliometric mapping. The final corpus comprises 648 journal articles and shows accelerated publication growth after 2023, with scientific production concentrated in a small set of leading countries. Keyword structures indicate that IoT-centric deployments dominate the semantic backbone, where authentication and intelligent methods co-occur with blockchain security primitives, while post-quantum and privacy-preserving constructs form a cohesive technical stream. QKD appears as a distinct but more specialized theme, typically discussed at the system level and shaped by infrastructure and scalability constraints. Overall, the literature is moving from conceptual risk articulation toward engineering integration; however, progress is limited by inconsistent reporting of threat models, post-quantum parameter sets, and ledger-level cost trade-offs, highlighting the need for auditable and reproducible evaluation. Full article

The development of effective Intrusion Detection Systems (IDS) for Internet of Things (IoT) environments is constrained by the absence of realistic, large-scale datasets, particularly for the Message Queuing Telemetry Transport (MQTT) protocol, which is prevalent in industrial IoT. Existing datasets are frequently limited in scope, imbalanced, or do not capture MQTT-specific attack patterns, thereby impeding the training of accurate machine learning models. To address this gap, the extensible Message Queuing Telemetry Transport (eMQTT) Traffic Generator is introduced as a modular platform capable of simulating both legitimate MQTT communication and targeted denial-of-service (DoS) attacks. The framework features a scalable and reproducible architecture that incorporates protocol-aware attack modeling, automated traffic labeling, and direct export of datasets suitable for machine learning applications. The system produces standardized, configurable, repeatable, and publicly accessible datasets, thereby facilitating reproducible research and scalable experimentation. Experimental validation demonstrates that the simulated traffic aligns with established DoS behavior models. Two high-volume datasets were generated: one representing normal MQTT traffic and another emulating CONNECT-flooding attacks. Machine learning classifiers trained on these datasets exhibited strong performance, with gradient boosting models achieving over 95% accuracy in distinguishing benign from malicious traffic. This work offers a practical solution to the scarcity of datasets in IoT security research. By providing a controlled, extensible, and reproducible traffic-generation platform alongside validated datasets, eMQTT enables systematic experimentation, supports the advancement of IDS solutions, and enhances MQTT security for critical IoT infrastructures. Full article