Search Results (222)

Using transaction cost economics (TCE) and agency theory, this paper examines how blockchain, smart contracts, and decentralized autonomous organizations (DAOs) reconfigure financial services across payments, wealth management, real estate, and corporate governance. Three research questions are addressed: (1) What are the quantifiable efficiency gains from blockchain-based real-time settlement compared with legacy systems? (2) How do blockchain technologies reduce intermediation and agency costs in wealth management and real estate? (3) Finally, to what extent do DAOs resolve or transform traditional corporate governance problems? By combining a present-value model calibrated to U.S. Automated Clearing House (ACH) data ($86.2 trillion in annual volume), comparative institutional analysis, and synthesis of empirical evidence from pilot implementations and on-chain governance metrics, this paper makes three principal contributions. First, real-time settlement yields approximately $12 billion in annual opportunity cost savings at the baseline 7.5% discount rate, with sensitivity analysis producing a range of $8–15 billion. The majority of gains accrue from moving to same-day or within-hour settlement. Second, tokenization and smart contract escrow substantially reduce real estate intermediation costs, blockchain-based digital identity streamlines wealth management onboarding, and a stablecoin taxonomy classifies fiat-collateralized, crypto-collateralized, and algorithmic designs by risk profile. Third, on-chain data reveal persistent governance token concentration (Gini > 0.98) and low voter participation (typically below 10%), exposing a gap between DAO theory and practice. Blockchain-specific risks are mapped to National Institute of Standards and Technology (NIST) Cybersecurity Framework 2.0, and mechanism design solutions, such as quadratic voting and AI-assisted proposal evaluation, are proposed to address whale dominance. Effective adoption requires hybrid architecture combining on-chain automation with off-chain structures for accountability and regulatory compliance. Full article

In increasingly interconnected tactical environments, cybersecurity, trust, and interoperability must evolve in tandem. Federated Coalition Networks (FCNs) enable multinational cooperation while preserving national sovereignty; however, the secure management of identities, policies, and configurations across coalition domains remains a critical challenge, particularly under adversarial and resource-constrained conditions. This paper proposes a blockchain-enabled management framework aligned with the defense-in-depth paradigm, focusing on management-plane functions such as policy enforcement, public key infrastructure (PKI) management, and auditable governance, rather than time-critical tactical communications. The solution relies on a permissioned blockchain architecture with Byzantine Fault Tolerant consensus, avoiding energy-intensive Proof-of-Work mechanisms and supporting operation under Disconnected, Intermittent, and Low-bandwidth (DIL) conditions. A coalition-level trust-and-governance model is introduced to prevent unilateral control while preserving national autonomy. A realistic use case and a proof-of-concept implementation demonstrate the feasibility of the approach, showing bounded latency, limited energy overhead, and sufficient throughput for FCN management. These results indicate that appropriately tailored blockchain solutions can effectively enhance resilience, trust, and compliance in federated defense networks. Full article

The integration of blockchain technology into Cyber–Physical Systems (CPS) offers decentralized resilience against data manipulation. This also introduces stochastic consensus latencies that threaten real-time control stability. We present a Stochastic-Aware Blockchain Predictive Control (SAB-PC) framework, which models blockchain-induced jitter as a state-dependent Markovian process, and embeds it within a Markovian Jump Linear System (MJLS) formulation. Using mode-dependent Linear Matrix Inequalities (LMIs), we derive Mean Square Stability (MSS) conditions, which capture the interaction between decentralized consensus dynamics and closed-loop control behavior. The framework is validated on the Tennessee Eastman Process (TEP) benchmark, using a calibrated stochastic delay model that reflects realistic blockchain congestion patterns. Our results show that standard blockchain-mediated control architectures become unstable under Practical Byzantine Fault Tolerance (PBFT)-induced quadratic latency growth, whereas SAB-PC maintains stable operation across decentralized networks up to 60 validator nodes. The predictive Safety Runway effectively masks long-tail delay distributions, ensuring real-time feasibility and preserving safe Reactor Pressure trajectories. Under coordinated False Data Injection (FDI) attacks, SAB-PC limits pressure deviations to only 1.2 kPa despite an 8.0 kPa adversarial bias, demonstrating cryptographic and control-theoretic resilience. Full article

Blockchain technology is redefining the foundations of cybersecurity by introducing decentralized, tamper-resistant mechanisms for data integrity, trust management, and malware intelligence sharing. Traditional detection systems, which are dependent on centralized control and opaque validation, remain vulnerable to data manipulation and systemic compromise. The integration of blockchain transforms these paradigms because it provides verifiable provenance, distributed consensus, and autonomous enforcement through smart contracts. This review synthesizes fifteen years of progress (2010–2025) at the intersection of blockchain and malware detection and discusses core architectures, consensus protocols, and cryptographic properties that underpin decentralized defenses. The review follows a structured literature review methodology, which focuses on blockchain architectures, consensus protocols, and malware-detection pipelines reported in the cybersecurity literature. It also analyzes blockchain detection pipelines, performance tradeoffs, and data protection mechanisms in distributed learning systems and artificial intelligence models. Special attention is given to scalability constraints, regulatory compliance, and interoperability challenges that shape adoption. The review identifies three dominant design patterns: (i) decentralized threat-intelligence sharing with provenance guarantees, (ii) consensus-driven validation of malware artifacts, and (iii) on-chain trust and reputation mechanisms for detector accountability. Through the union of blockchain, artificial intelligence, edge computation, and federated learning, cybersecurity attains an auditable and adaptive architecture resilient to adversarial threats. The study concludes that blockchain provides a verifiable trust infrastructure for malware detection, but its practical deployment requires faster transaction validation and stronger protection of sensitive data; future research should address performance optimization and regulatory compliance. Full article

Background: Blockchain technology has emerged as a transformative communication solution for securing distributed systems. However, several vulnerabilities exist during transactions, including latency and network congestion issues during mempool processing, topology weaknesses, cross-chain bridge exploits, and cryptographic weaknesses. These vulnerabilities have led to attacks that have threatened system integrity, including Block Extractable Value (BEV) attacks, Maximal Extractable Value (MEV) attacks, sandwich attacks, liquidation, and Decentralized Finance (DeFi) reordering attacks, among others. Thus, implementing a robust security framework based on the Confidentiality, Integrity, and Availability (CIA) triad remains critical for addressing modern blockchain technology threats. Objective: This paper examines blockchain technology, its various vulnerabilities, and attacks to determine how criminals exploit the system during transactions. Further, it evaluates its impact on users. Then, implement a blockchain attack in a “MasterChain” virtual environment to demonstrate how vulnerable spots can be practically exploited and discuss the application of the CIA security triad through modern cryptographic primitives. Methods: The approach considers Hevner’s design science framework, which emphasizes creating innovative artifacts that address identified problems while contributing to the knowledge base through rigorous evaluation. Furthermore, we developed a MasterChain tool using Python with Flask for distributed node communication, utilizing the Elliptic Curve Digital Signature Algorithm (ECDSA) with the Standards for Efficient Cryptography Prime 256-bit Koblitz curve 1 (secp256k1) for digital signatures and Secure Hash Algorithm 3 (SHA-3) (Keccak-256) hashing for block integrity. Results: show how the CIA has been implemented to provide secure communication through ECDSA-based transactions, SHA-3 chain integrity verification, and a multi-node distributed architecture, respectively. The performance analysis shows that ECDSA provides 256-bit security with 64-byte signatures compared to 2048-bit Rivest–Shamir–Adleman (RSA)’s 256-byte signatures, achieving a 75% reduction in bandwidth overhead. SHA-3 provides immunity to length extension attacks while maintaining equivalent collision resistance to SHA-256. Conclusions: The MasterChain framework provides a practical foundation for implementing blockchain security that addresses both classical and emerging vulnerabilities. The adoption of ECDSA and SHA-3 (Keccak-256) positions the system favourably for modern blockchain applications, while providing insights into the cryptographic trade-offs between performance, security, and compatibility. Full article

The rapid growth of cryptocurrencies and non-fungible tokens (NFTs) has expanded technological opportunities, but it has also increased the exposure surface to cyber threats, creating a need for a more precise understanding of the field’s scientific evolution. This study aims to systematically analyse academic output related to cybersecurity and cyber threats within cryptocurrency and NFT ecosystems, identifying central themes, the most influential authors, and emerging trends. A bibliometric methodology was employed, based on the PRISMA 2020 protocol and scientific mapping tools such as SciMAT (v1.1.06) and VOSviewer (v1.6.20), using a corpus of 337 articles published between 2014 and 2025. The findings indicate sustained growth in the literature, a marked geographical and editorial concentration, and the presence of motor themes such as blockchain, cybersecurity, emerging technologies and illegal mining, alongside emerging areas such as intrusion detection. The results also reveal a progressive integration of artificial intelligence techniques in the detection and prevention of attacks. In conclusion, this study provides a comprehensive overview of the state of the art, identifies critical gaps, and underscores the need for interdisciplinary approaches to strengthen security in decentralised environments. Full article

The rapid expansion of electric vehicles (EVs) has added complexity to the resilience and security challenges to the EV charging systems, especially owing to the exposure to the cyber–physical threats and the reliance on centrally coordinated systems. Although the previous literature has discussed the use of blockchain in the context of smart grids and mobility services; its implementation to improve the resilience of EV charging, particularly when integrated with cybersecurity systems, is still insufficiently synthesized. Despite these issues, critical gaps persist in terms of scalability, interoperability, and cybersecurity enforcement. This study presents an exploratory literature review that examines the intersection of blockchain and cybersecurity enabled applications and introduces a comparative framework evaluating the conventional security controls with blockchain based cybersecurity solutions to improve the resilience of EV charging infrastructure. The authors analyzed 70 studies published between 2018 and 2025 to determine the security weaknesses and map them to decentralized solutions. Reported threats, security mechanisms, architectural decisions, and levels of validation were grouped and reviewed critically in the patterns of limitations with respect to scalability, interoperability, and deployment maturity. Through the synthesis of fragmented results in cross disciplinary research, the paper finds the main gaps in research and comparative research results that could be used as a comprehensive reference in future studies and system design in resilient EV charging infrastructures. Full article

Background: Explainable Artificial Intelligence (XAI) is deployed in Internet of Things (IoT) ecosystems for smart cities and precision agriculture, where opaque models can compromise trust, accountability, and regulatory compliance. Objective: This survey investigates how XAI is currently integrated into distributed and federated IoT architectures and identifies systematic gaps in evaluation under real-world resource constraints. Methods: A structured search across IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, and Google Scholar targeted publications related to XAI, IoT, edge/fog computing, smart cities, smart agriculture, and federated learning. Relevant peer-reviewed works were synthesized along three dimensions: deployment tier (device, edge/fog, cloud), explanation scope (local vs. global), and validation methodology. Results: The analysis reveals a persistent resource–interpretability gap: computationally intensive explainers are frequently applied on constrained edge and federated platforms without explicitly accounting for latency, memory footprint, or energy consumption. Only a minority of studies quantify privacy–utility effects or address causal attribution in sensor-rich environments, limiting the reliability of explanations in safety- and mission-critical IoT applications. Contribution: To address these shortcomings, the survey introduces a hardware-centric evaluation framework with the Computational Complexity Score (CCS), Memory Footprint Ratio (MFR), and Privacy–Utility Trade-off (PUT) metrics and proposes a hierarchical IoT–XAI reference architecture, together with the conceptual Internet of Things Interpretability Evaluation Standard (IOTIES) for cross-domain assessment. Conclusions: The findings indicate that IoT–XAI research must shift from accuracy-only reporting to lightweight, model-agnostic, and privacy-aware explanation pipelines that are explicitly budgeted for edge resources and aligned with the needs of heterogeneous stakeholders in smart city and agricultural deployments. Full article

The rapid digital transformation of healthcare has improved clinical efficiency, patient engagement, and data accessibility, but it has also introduced significant cyber security and data privacy challenges. Healthcare IT systems increasingly rely on interconnected networks, electronic health records (EHRs), tele-medicine platforms, cloud infrastructures, and Internet of Medical Things (IoMT) devices, which collectively expand the attack surface for cyber threats. This scoping review maps and synthesizes recent evidence on cyber security risks in healthcare, including ransomware, data breaches, insider threats, and vulnerabilities in legacy systems, and examines key data privacy concerns related to patient confidentiality, regulatory compliance, and secure data governance. We also review contemporary security strategies, including encryption, multi-factor authentication, zero-trust architecture, blockchain-based approaches, AI-enabled threat detection, and compliance frameworks such as HIPAA and GDPR. Persistent challenges include integrating robust security with clinical usability, protecting resource-limited hospital environments, and managing human factors such as staff awareness and policy adherence. Overall, the findings suggest that effective healthcare cyber security requires a multi-layered defense combining technical controls, continuous monitoring, governance and regulatory alignment, and sustained organizational commitment to security culture. Future research should prioritize adaptive security models, improved standardization, and privacy-preserving analytics to protect patient data in increasingly complex healthcare ecosystems. Full article

The rapid expansion of health big data, encompassing genomic profiles and wearable device telemetry, has significantly escalated personal privacy risks. This systematic literature review (SLR) synthesizes 86 peer-reviewed studies (2014–2025) through the dual lens of the NIST Cybersecurity and Privacy Frameworks to evaluate emerging risks, mitigation technologies, and regulatory landscapes. Our analysis identifies unauthorized access as the predominant threat, while blockchain-based solutions comprise 22.1% of proposed interventions. However, a comparative evaluation reveals critical performance trade-offs: differential privacy mechanisms incur a 15–35% utility loss, whereas blockchain implementations impose a 40–50% computational overhead. Furthermore, an assessment of major regulatory frameworks (GDPR, HIPAA, PIPL, and emerging regional laws in Sub-Saharan Africa) elucidates significant cross-jurisdictional conflicts. To address these challenges, we propose the Bio-inspired Adaptive Healthcare Privacy (BAHP) framework, validated through retrospective case study analysis, offering a dynamic approach to securing sensitive health ecosystems. Full article

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

(1) Background: The convergence of Big Data and the Internet of Things (IoT) is transforming digital accounting from retrospective documentation into real-time operational intelligence. This systematic review examines how Industry 4.0 technologies—artificial intelligence (AI), blockchain, edge computing, and digital twins—transform accounting practices through intelligent automation, continuous compliance, and predictive decision support. (2) Methods: The study synthesizes 176 peer-reviewed sources (2015–2025) selected using explicit inclusion criteria emphasizing empirical evidence. Thematic analysis across seven domains—conceptual foundations, system evolution, financial reporting, fraud detection, audit transformation, implementation challenges, and emerging technologies—employs systematic bias-reduction mechanisms to develop evidence-based theoretical propositions. (3) Results: Key findings document fraud detection accuracy improvements from 65–75% (rule-based) to 85–92% (machine learning), audit cycle reductions of 40–60% with coverage expansion from 5–10% sampling to 100% population analysis, and reconciliation effort decreases of 70–80% through triple-entry blockchain systems. Edge computing reduces processing latency by 40–75%, enabling compliance response within hours versus 24–72 h. Four propositions are established with empirical support: IoT-enabled reporting superiority (15–25% error reduction), AI-blockchain fraud detection advantage (60–70% loss reduction), edge computing compliance responsiveness (55–75% improvement), and GDPR-blockchain adoption barriers (67% of European institutions affected). Persistent challenges include cybersecurity threats (300% incident increase, $5.9 million average breach cost), workforce deficits (70–80% insufficient training), and implementation costs ($100,000–$1,000,000). (4) Conclusions: The research contributes a four-layer technology architecture and challenge-mitigation framework bridging technical capabilities with regulatory requirements. Future research must address quantum computing applications (5–10 years), decentralized finance accounting standards (2–5 years), digital twins with 30–40% forecast improvement potential (3–7 years), and ESG analytics frameworks (1–3 years). The findings demonstrate accounting’s fundamental transformation from historical record-keeping to predictive decision support. 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

Integrating emerging Industry 4.0 technologies into smart factories has been widely discussed, particularly challenges regarding the practical use of a blockchain; one remaining challenge is the role of a blockchain beyond logistics and traceability, as well as its ability to support explicit trust measurement in real industrial environments. Existing studies often treat trust as a conceptual or cloud-oriented construction, without linking it to measurable production events. This study proposes a blockchain service-level agreement (SLA) to measure trust at an open-source frugal smart factory (SF). Trust is defined as a dynamic quantitative score derived from measurable process events, including estimated and response times, assembly correctness, and transaction outcomes; all of this is calculated through a smart contract implemented on a blockchain network. The approach is implemented in a tangram puzzle assembly process that integrates cyber-physical systems, edge computing, artificial intelligence, cloud computing, data analytics, cybersecurity, and the blockchain within a unified SF architecture. The framework was experimentally validated across four representative assembly scenarios: (i) the SF delivered the puzzle in time and was correctly assembled (${\lambda }_s$ = 0.1734), (ii) the puzzle was completed within tolerance time (${\lambda }_s$ = 0.0649), (iii) the puzzle was delivered on time and was incorrectly assembled (${\lambda }_s$ = 0.0005), and (iv) the puzzle was completed outside the tolerance time and was correctly assembled (${\lambda }_s$ = 4.91 × ${10}^{-5}$); demonstrating that the model accurately estimates expected assembly times and updates trust without manual intervention during a physical manufacturing task, addressing the limitations of prior conceptual and cloud-based approaches. The main research contributions include an operational SLA-based trust model, the demonstration of the feasibility of applying blockchain-based SLAs in a physical SF environment, and evidence that a blockchain can be justified as a mechanism for managing and measuring trust in SF, rather than solely for traceability or logistics. Full article

Background: Blockchain has been extensively discussed for enhancing transparency, traceability, and trust in general; however, there is fragmented empirical evidence available with respect to this issue within maritime logistics. The objective is to integrate and categorize peer-reviewed publications concerning applications of blockchain in maritime logistics and related supply chain domains. Methods: A systematic literature review with PRISMA 2020 was performed in Scopus database, and after a process of screening and eligibility, a total of 78 journal articles published mainly from September 2024 were incorporated. Descriptive and bibliometric analyses were conducted, and VOS viewer-based bibliographic coupling were employed to visualize thematic structure. Results: The review identifies seven research priorities for blockchain in maritime logistics: Technological Interoperability, Economic and Operational Impact, Cybersecurity and Privacy, Adoption and Scalability, Decision-Making and Trust, Environmental Sustainability, and Standardization and Regulatory Frameworks. Blockchain’s primary advantages are enhanced data integrity and visibility, whereas key challenges include interoperability, legal/regulatory uncertainty (e.g., e-doc recognition), high costs, scalability ceilings, integration with legacy systems, and data governance fears. Conclusions: The application of blockchain in maritime logistics depends on combined technical and institutional enabling conditions; an Integrated Blockchain Adoption Framework (IBAF) is suggested, and providing practical guides based on standardization, legal convergence, and hybrid governance modes. Full article