Search Results (573)

The integration of Software-Defined Networking (SDN), blockchain (BC), and machine learning (ML) has emerged as a promising approach to securing Internet of Things (IoT) and Industrial IoT (IIoT) networks. This paper conducted a comprehensive review of recent studies focusing on multi-layered security across device, control, network, and application layers. The analysis reveals that BC technology ensures decentralised trust, immutability, and secure access validation, while SDN enables programmability, load balancing, and real-time monitoring. In addition, ML/deep learning (DL) techniques, including federated and hybrid learning, strengthen anomaly detection, predictive security, and adaptive mitigation. Reported evaluations show similar gains in detection accuracy, latency, throughput, and energy efficiency, with effective defence against threats, though differing experimental contexts limit direct comparison. It also shows that the solutions’ effectiveness depends on ecosystem factors such as SDN controllers, BC platforms, cryptographic protocols, and ML frameworks. However, most studies rely on simulations or small-scale testbeds, leaving large-scale and heterogeneous deployments unverified. Significant challenges include scalability, computational and energy overhead, dataset dependency, limited adversarial resilience, and the explainability of ML-driven decisions. Based on the findings, future research should focus on lightweight consensus mechanisms for constrained devices, privacy-preserving ML/DL, and cross-layer adversarial-resilient frameworks. Advancing these directions will be important in achieving scalable, interoperable, and trustworthy SDN-IoT/IIoT security solutions. Full article

This paper introduces a cybersecurity framework that combines a deception-based ransomware detection system, called the Intrusion and Ransomware Detection System for Cloud (IRDS4C), with a blockchain-enabled Cyber Threat Intelligence platform (CTIB). The framework aims to improve the detection, reporting, and sharing of ransomware threats in cloud environments. IRDS4C uses deception techniques such as honeypots, honeytokens, pretender network paths, and decoy applications to identify ransomware behavior within cloud systems. Tests on 53 Windows-based ransomware samples from seven families showed an ordinary detection time of about 12 s, often quicker than tralatitious methods like file hashing or entropy analysis. These detection results are currently limited to Windows-based ransomware environments, and do not yet cover Linux, containerized, or hypervisor-level ransomware. Detected threats are formatted using STIX/TAXII standards and firmly shared through CTIB. CTIB applies a hybrid blockchain consensus of Proof of Stake (PoS) and Proof of Work (PoW) to ensure data integrity and protection from tampering. Security analysis shows that an attacker would need to control over 71% of the network to compromise the system. CTIB also improves trust, accuracy, and participation in intelligence sharing, while smart contracts control access to erogenous data. In a local prototype deployment (Hardhat devnet + FastAPI/Uvicorn), CTIB achieved 74.93–125.92 CTI submissions/min, The number of attempts or requests in each test was 100 with median end-to-end latency 455.55–724.99 ms (p95: 577.68–1364.17 ms) across PoW difficulty profiles (difficulty_bits = 8–16). Full article

The entire system collapses due to the issues of inadequate centralized storage capacity, poor scalability, low storage efficiency, and susceptibility to single point of failure brought on by huge power consumption data in the smart grid; thus, an alliance chain-driven multi-cloud storage and verifiable deletion method for smart grid data is proposed. By leveraging the synergy between alliance blockchain and multi-cloud architecture, the encrypted power data originating from edge nodes is dispersed across a decentralized multi-cloud infrastructure, which effectively mitigates the danger of data loss resulting from single-point failures or malicious intrusions. The removal of expired and user-defined data is guaranteed through a transaction deletion algorithm integrated into the indexed storage deletion chain and strengthens the flexibility and security of the storage architecture. Based on the Practical Byzantine Fault-Tolerant Consensus Protocol with Ultra-Low Storage Overhead (ULS-PBFT), by the hierarchical grouping of nodes, the system communication overhead and storage overhead are reduced. Security analysis proves that the scheme can resist tampering attacks, impersonation attacks, collusion attacks, double spend attacks, and replay attacks. Performance evaluation shows that the scheme improves compared to similar methods. Full article

We present an adaptive governance engine for blockchain-enabled Vehicular Ad Hoc Networks (VANETs) that regulates the latency–energy–coherence trade-off under rapid topology changes. The core contribution is an Ideal Information Cycle (an operational abstraction of information injection/validation) and a modular VANET Engine implemented as a real-time control loop in NS-3.35. At runtime, the Engine monitors normalized Shannon entropies—informational entropy S over active transactions and spatial entropy $H_{\mathrm{spatial}}$ over occupancy bins (both on $[0,1]$)—and adapts the consensus mode (latency-feasible PoW versus signature/quorum-based modes such as PoS/FBA) together with rigor parameters via calibrated policy maps. Governance is formulated as a constrained operational objective that trades per-block resource expenditure (radio + cryptography) against a Quality-of-Information (QoI) proxy derived from delay/error tiers, while maintaining timeliness and ledger-coherence pressure. Cryptographic cost is traced through counted operations, $E_{\mathrm{crypto}}=e_h{n}_{\mathrm{hash}}+e_{\mathrm{sig}}{n}_{\mathrm{sig}}$, and coherence is tracked using the LCP-normalized definition $D_{\mathrm{ledger}}\left(t\right)$ computed from the longest common prefix (LCP) length across nodes. We evaluate the framework under urban/highway mobility, scheduled partitions, and bounded adversarial stressors (Sybil identities and Byzantine proposers), using 600 s runs with 30 matched random seeds per configuration and 95% bias-corrected and accelerated (BCa) bootstrap confidence intervals. In high-disorder regimes ($S\gtrsim 0.8$), the Engine reduces total per-block energy (radio + cryptography) by more than 90% relative to a fixed-parameter PoW baseline tuned to the same agreement latency target. A consensus-first triggering policy further lowers agreement latency and improves throughput compared with broadcast-first baselines. In the emphasized urban setting under high mobility ($v=30$ m/s), the Engine keeps agreement/commit latency in the sub-100 ms range while maintaining finality typically within sub-150 ms ranges, bounds orphaning (≤10%), and reduces average ledger divergence below $0.07$ at high spatial disorder. The main evaluation is limited to $N\le 100$ vehicles under full PHY/MAC fidelity. PoW targets are intentionally latency-feasible and are not intended to provide cryptocurrency-grade majority-hash security; operational security assumptions and mode transition safeguards are discussed in the manuscript. Full article

The large-scale integration of Distributed Energy Resources (DERs) in smart grids creates challenges related to real-time optimization, system scalability, and operational security. This paper presents GridOpt, a hybrid edge–cloud framework designed to address these challenges through distributed intelligence and coordinated control. In GridOpt, edge nodes handle latency-sensitive tasks, while cloud resources support the processing of large-scale grid data. Security is addressed through the integration of homomorphic encryption and blockchain-based consensus, together with an interoperability layer that enables coordination among heterogeneous grid components. Simulation results show that GridOpt achieves an average latency of 76 ms and an energy consumption of 25 Joules under high-throughput conditions. The framework further maintains scalability beyond 10 requests per second with a resource utilization of 54% in dense deployment scenarios. Comparative analysis indicates that GridOpt outperforms ECCGrid, JOintCS, and EdgeApp across key performance metrics. Full article

Traditional Human Resource Management (HRM) systems are criticized for lacking transparency, being inefficient, and offering ample opportunities for fraud because of their centralized design and reliance on manual processes. This work proposes a blockchain-enabled framework for HRM that enhances the transparency, trust, and global mobility of talents by integrating distributed ledgers, consensus protocols, and smart contract networks into Human Resources (HR) functions. A four-layer theoretical model—data, consensus, smart contract, and application layers—is developed and comparatively examined against traditional HR systems to show how blockchain principles can be systematically mapped into HR processes. This study shows how blockchain-driven HRM can ensure tamper-evident employee records, automate contractual and payroll operations, and enhance auditability and compliance. By informing the framework with established technology adoption perspectives, this paper extends both the theoretical and managerial understanding of blockchain in HR. In comparison with previous studies that were limited to either recruitment or credential verification, this article presents an overarching, cross-layer synthesis that connects blockchain architectures with end-to-end HR functions, thus providing a clear conceptual foundation for its future enterprise adoption in the digital economy. Full article

The convergence of artificial intelligence (AI), machine learning (ML), blockchain, and big data analytics is transforming the governance, sustainability, and resilience of modern banking ecosystems. This study provides a multivariate bibliometric analysis using Principal Component Analysis (PCA) of research indexed in Scopus and Web of Science to explore how decentralized digital infrastructures and AI-driven analytical capabilities contribute to sustainable financial development, transparent governance, and climate-resilient digital societies. Findings indicate a rapid increase in interdisciplinary work integrating Distributed Ledger Technology (DLT) with large-scale data processing, federated learning, privacy-preserving computation, and intelligent automation—tools that can enhance financial inclusion, regulatory integrity, and environmental risk management. Keyword network analyses reveal blockchain’s growing role in improving data provenance, security, and trust—key governance dimensions for sustainable and resilient financial systems—while AI/ML and big data analytics dominate research on predictive intelligence, ESG-related risk modeling, customer well-being analytics, and real-time decision support for sustainable finance. Comparative analyses show distinct emphases: Web of Science highlights decentralized architectures, consensus mechanisms, and smart contracts relevant to transparent financial governance, whereas Scopus emphasizes customer-centered analytics, natural language processing, and high-throughput data environments supporting inclusive and equitable financial services. Patterns of global collaboration demonstrate strong internationalization, with Europe, China, and the United States emerging as key hubs in shaping sustainable and digitally resilient banking infrastructures. By mapping intellectual, technological, and collaborative structures, this study clarifies how decentralized intelligence—enabled by the fusion of AI/ML, blockchain, and big data—supports secure, scalable, and sustainability-driven financial ecosystems. The results identify critical research pathways for strengthening financial governance, enhancing climate and social resilience, and advancing digital transformation, which contributes to more inclusive, equitable, and sustainable societies. Full article

Zero-Touch Network (ZTN) represents a cornerstone approach of Next Generation Networks (NGNs), enabling fully automated and AI-driven network and service management. However, their distributed and multi-domain nature introduces critical security challenges, particularly regarding service identity and data integrity. This paper proposes a novel blockchain-based framework to enhance the security of ZTN through two complementary mechanisms: decentralized digital identity management and oracle-assisted network monitoring. First, a Decentralized Identity Management framework aligned with Zero-Trust Architecture principles is introduced to ensure tamper-proof authentication and authorization in a trustless environment among network components. By leveraging decentralized identifiers, verifiable credentials, and zero-knowledge proofs, the proposed Decentralized Authentication and Authorization component eliminates reliance on centralized authorities, while preserving privacy and interoperability across domains. Second, the paper investigates blockchain oracle mechanisms as a means to extend data integrity guarantees beyond the blockchain, enabling secure monitoring of Network Services and validation of Service-Level Agreements. We propose a four-dimensional framework for oracle design, based on qualitative comparison of oracle types—decentralized, compute-enabled, and consensus-based—to identify their suitability for NGN scenarios. This work proposes an architectural and design framework for Zero-Touch Networks, focusing on system integration and security-aware orchestration rather than large-scale experimental evaluation. The outcome of our study highlights the potential of integrating blockchain-based identity and oracle solutions to achieve resilient, transparent, and self-managed network ecosystems. This research bridges the gap between theory and implementation by offering a holistic approach that unifies identity security and data integrity in ZTNs, paving the way towards trustworthy and autonomous 6G infrastructures. Full article

Although DNS has been continuously extended to improve usability and security, its centralized, server-based architecture leaves fundamental limitations unresolved, including single points of failure (SPOF), susceptibility to censorship, and exposure to DDoS. This study examines blockchain-based DNS (BDNS) as an alternative proposed to mitigate these structural issues. We first synthesize prior research and systems on BDNS, and then conduct a comparative analysis using practical deployability as the primary criterion. Specifically, we selected seven representative BDNS projects, including Namecoin, Handshake, and Ethereum Name Service (ENS), and evaluated them under a common set of criteria: (i) the record model, finality, and TTL semantics; (ii) friction along real resolution paths involving resolvers, browsers, and gateways; and (iii) interoperability with the legacy DNS, including DNSSEC and DNS over TLS(DoT)/DNS over HTTPS(DoH), together with migration scenarios. The analysis indicates that many systems rely on gateways and client-side extensions, limiting native resolution paths. It further finds that finality latency, dependence on off-chain indexing and availability, and the interplay of key management and tokenomics design increase operational complexity and raise barriers to adoption. Building on these findings, the paper derives operational requirements and proposes a coexistence-first, five-layer migration framework that incrementally integrates BDNS while retaining the legacy DNS. This provides an incremental path toward a more resilient, inclusive, and secure global naming infrastructure. Full article

The construction industry continues to grapple with persistently high accident rates and fragmented workforce management systems, where manual record-keeping and siloed data impede effective safety compliance. While digital interventions exist, they often rely on centralized databases that are vulnerable to manipulation and opaque. This systematic literature review critically examines the application of blockchain technology as a decentralized infrastructure for enhancing safety compliance in construction. Adhering to the PRISMA 2020 guidelines, this study synthesizes findings from 115 peer-reviewed articles (2020–2025) retrieved from Scopus, Web of Science, IEEE Xplore, and Google Scholar. The analysis focuses on three core mechanisms: (1) the creation of immutable, timestamped safety logs to prevent retroactive data tampering; (2) the integration of IoT sensors for real-time, trustless hazard monitoring; and (3) the deployment of smart contracts to automate compliance verification and incentive distribution. The review juxtaposes theoretical frameworks with empirical evidence from global case studies, including pilot projects in North America and the Asia-Pacific, to quantify benefits such as reduced reporting latency and improved data integrity. Despite promising results, the analysis reveals significant barriers to widespread adoption, notably the “oracle problem,” scalability limitations of consensus protocols, and the lack of legal recognition for blockchain records. This paper concludes that while blockchain is not a panacea, it offers a necessary layer of trust and accountability absent in traditional Common Data Environments (CDEs). Future research directions are proposed to address interoperability with BIM standards (ISO 19650) and to develop energy-efficient consensus mechanisms suitable for resource-constrained construction sites. Full article

The proliferation of Internet of Things (IoT) applications in safety-critical domains, such as healthcare, smart transportation, and industrial automation, demands robust solutions for data integrity, traceability, and security that surpass the capabilities of centralized databases. This paper analyzes how blockchain technology can be integrated with core IoT service functions—including data management, security, device management, group coordination, and automated billing—to enhance immutability, trust, and operational efficiency. Our analysis identifies practical use cases such as consensus-driven tamper-proof storage, role-based access control, firmware integrity verification, and automated micropayments. These use cases showcase blockchain’s potential beyond traditional data storage. Building on this, we propose a novel framework that integrates a permissioned distributed ledger with a standardized IoT service layer platform through a Blockchain Interworking Proxy Entity (BlockIPE). This proxy dynamically maps IoT service functions to smart contracts, enabling flexible data routing to conventional databases or blockchains based on the application requirements. We implement a Dockerized prototype that integrates a C-based oneM2M platform with an Ethereum-compatible permissioned ledger (implemented using Hyperledger Besu) via BlockIPE, incorporating security features such as role-based access control. For performance evaluation, we use Ganache to isolate proxy-level overhead and scalability. At the proxy level, the blockchain-integrated path achieves processing latencies (≈86 ms) comparable to, and slightly faster than, the traditional database path. Although the end-to-end latency is inherently governed by on-chain confirmation (≈0.586–1.086 s), the scalability remains high (up to 100,000 TPS). This validates that the architecture secures IoT ecosystems with manageable operational overhead. Full article

As supply chains expand in scale and the number of participating nodes increases, existing consensus algorithms are increasingly showing limitations in scalability, communication complexity, and handling complex network environments. To address the shortcomings of blockchain consensus mechanisms in master node selection, scalability, and communication complexity in supply chain traceability scenarios, this paper proposes a blockchain hybrid consensus optimization algorithm named Node Rating-Based and Grouping Raft cluster Practical Byzantine Fault Tolerance (NG-RPBFT) for supply chain traceability. This algorithm builds a multi-index comprehensive rating model for nodes to comprehensively evaluate consensus nodes, reasonably groups consensus nodes, adopts an inter-group and intra-group dual consensus mechanism to achieve efficient data synchronization, and introduces Brotli data compression technology to optimize message load, effectively enhancing system performance. Experimental results confirm that this algorithm significantly improves the scalability of the consensus mechanism and performs exceptionally well in consensus efficiency, making it suitable for complex application scenarios such as supply chain traceability under CPS scenarios. Full article

Blockchain consensus mechanisms are fundamental to the security and decentralization of distributed ledgers. In Proof-of-Stake (PoS) systems, which are lauded for their energy efficiency, the fair and unpredictable selection of block proposers is paramount and relies heavily on secure random number generation. The RANDAO random number generation mechanism in the Gasper protocol is susceptible to hash collision attack, which can introduce adversarial bias in the block proposer selection process. From the perspective of resisting adversarial bias attacks, this paper examines the optimization of the Gasper consensus protocol, focusing on security issues such as vulnerabilities to hash collisions in RANDAO and high latency in asynchronous network environments. By analyzing the spatial–temporal distribution of historical block hashes, we propose a dual-round random number verification mechanism that enhances reliability through multiple validation models. We develop a dynamic game-theoretic model under incomplete information to analyze node strategy selection and interaction dynamics. Our experimental results demonstrate that the improved protocol (RABA-Gasper) offers superior resistance to attacks, fairness, and efficiency compared to conventional protocols. RABA-Gasper outperforms conventional ones, achieving a 6.8% attack success rate (vs. 32.7% for RANDAO and 18.2% for Two Look-Back) with 94.3% hash collision detection, a proposer Gini coefficient below 0.23, 2.3x higher throughput retention than RANDAO in asynchronous networks, and a slightly increased random number generation latency of 125 ms. Supported by a game-theoretic model, it guarantees security when honest nodes account for ≥2/3 of the total. Full article

Blockchain technology has rapidly evolved as a cornerstone of decentralized computing, transforming how trust, data integrity, and transparency are achieved in digital ecosystems. However, despite extensive adoption, significant gaps remain in understanding how key blockchain variables, such as block size, consensus mechanisms, and network latency, affect system vulnerabilities and susceptibility to cyberattacks. This survey addresses this gap by combining qualitative and quantitative analyses across multiple blockchain environments. Using simulation tools such as Ganache and Bitcoin Core, and reviewing peer-reviewed studies from 2016 to 2024, the research systematically maps blockchain parameters to cyberattack vectors including 51% attacks, Sybil attacks, and double-spending. Findings indicate that design choices like block size, block interval, and consensus type substantially influence resilience against attacks. The Blockchain Variable Quantitative Risk Framework (BVQRF) introduced here integrates NIST’s cybersecurity principles with quantitative scoring to assess risks. This framework represents a novel contribution by operationalizing theoretical security constructs into actionable evaluation metrics, enabling predictive modeling and adaptive risk mitigation strategies for blockchain systems. Full article

Blockchain systems have been widely adopted in today’s society, with consensus algorithms serving as their core component to ensure all participants in the network agree on a specific data state. Existing consensus algorithms such as Proof of Work (PoW), Proof of Stake (PoS), and the Practical Byzantine Fault-Tolerant Algorithm (PBFT) exhibit certain limitations in terms of scalability, security, and efficiency. To address these limitations, this paper proposes a novel Network-based Reputation Consensus (NRC) algorithm. The main research contributions of this work include the following: (1) An intelligent grouping mechanism that dynamically groups nodes based on network awareness, forming consensus groups with low internal latency and high bandwidth utilization, significantly reducing intra-group communication overhead. (2) A dynamic reputation system incorporating a “diminishing returns” reward function and a “multiplicative penalty” mechanism, effectively incentivizing honest node participation while preventing power monopoly. (3) A two-phase model of “intra-group BFT consensus + global communication committee ordering” that decomposes complex global consensus into parallel intra-group processing and coordination among a small set of elite nodes, thereby drastically improving efficiency. (4) Comprehensive simulations comparing the NRC algorithm with mainstream consensus algorithms, demonstrating its superior performance in communication overhead, throughput, latency, and tolerance to malicious nodes, thereby laying the foundation for large-scale applications. Full article