Oleksandr Pidpalyi, Олександр Романов, Larysa Globa, Антон Романов · 6 authors
The subject matter of the article is the iTZBEI (Integrated Trust–ZTA–Blockchain SDN Efficiency Index) – a novel composite metric for quantitative security assessment of software-defined networks (SDN) integrating Zero Trust Architecture (ZTA) and Blockchain technologies. The relevance of the research is determined by the fact that the centralized SDN control model generates critical vulnerabilities, including DDoS attacks, unauthorized routing manipulation, and insider threats – for which no unified quantitative evaluation framework currently exists. The study introduced a formalized aggregated security metric that enables continuous monitoring and comparative assessment across all components of the SDN–ZTA–Blockchain architecture. The tasks to be solved include: (1) identification of principal SDN attack vectors; (2) formalization of a transaction-processing algorithm covering the full access lifecycle; (3) definition of nine local security indicators; and (4) construction of the iTZBEI index with justified weighting coefficients. The methods used combine mathematical formalization of access control processes, cryptographic transaction verification, and experimental emulation of attack scenarios in a Mininet–OpenDaylight–Hyperledger Fabric environment. Conclusions. The obtained results of the article consist in the development of a functional algorithm that performs dynamic verification of user requests, makes adaptive authorization decisions according to the principles of least privilege, and records these decisions in an immutable distributed ledger. A metrics system is proposed, including local indicators such as the Continuous Authorisation Integrity Score (CAIS), the Blockchain Audit Integrity Score (BAIS), and the Local Policy Integrity (LPI). On this basis, the generalized Integrated Trust and Zero-Trust Blockchain Evaluation Index (iTZBEI) is described as an aggregated metric for comparative evaluation and continuous monitoring of the network’s security state. Scientific novelty. This study introduces a unified SDN + ZTA + Blockchain framework for network security, formalizes a transaction-level algorithm that directly links access decisions with distributed audit procedures, and proposes the iTZBEI metric as the first integral indicator for evaluating the integration’s effectiveness in dynamic network environments.
Dileep Kumar Murala, G. Siva Krishna, P. N. V. M. Syamala Rao, Madhusudana Rao Nalluri · 5 authors
Integrating blockchain technology with cloud computing has enabled Blockchain as a Service (BaaS), a cloud-based paradigm that allows users to design, deploy, and manage customized blockchain applications, including smart contracts and domain-specific business functionalities. BaaS providers manage infrastructure provisioning, maintenance, and scalability while ensuring quality of service (QoS) compliance through service level agreements (SLAs). However, existing resource management approaches often struggle to satisfy dynamic performance requirements in a cost-efficient manner without increasing operational overhead or reducing provider efficiency. This paper presents a structured Adaptive SLA-based assistance framework for deploying Hyperledger Fabric on cloud platforms. The framework integrates automated performance monitoring using Hyperledger Caliper, SLA violation detection through programmed SLA chaincode, and automated VM scaling via the OpenStack4J API within a 3E (effective–efficient–economical) verification methodology. A greedy multi-objective scheduling mechanism guides parameter optimization by selecting scaling actions according to marginal performance gain per unit cost. Experiments were conducted on the Nectar Research Cloud using Hyperledger Fabric 2.5 to evaluate the impact of VM size, block size, peer count, and storage configuration on throughput (TPS) and average latency. Results demonstrate that the framework consistently achieves performance targets of 50%, 100%, and 200% above the baseline configuration through adaptive resource reconfiguration. Among evaluated strategies, Comb2, which combines dual block size adjustment with VM scaling, emerged as the optimal balanced configuration in terms of performance and cost efficiency. While automated monitoring, SLA enforcement, and VM scaling are fully implemented, continuous online scheduling under dynamically changing workloads remains future work. The proposed framework establishes a practical foundation for SLA-driven blockchain optimization in cloud environments and supports future extensibility to additional BaaS platforms.
The rapid growth of AI-driven workloads, IoT ecosystems, and distributed digital services has exposed fundamental limitations in existing cloud and edge infrastructures, particularly in guaranteeing robust data privacy under emerging quantum threats. Current blockchain-based systems provide integrity and decentralization but rely predominantly on computational cryptography and access-control mechanisms, leaving them vulnerable to future quantum adversaries and large-scale inference attacks. In this paper, we introduce Data Communities as a novel paradigm for privacy-preserving, blockchain-enabled cooperative digital infrastructures, formalized within the Cooperative Digital Infrastructure (CDI) framework. Our approach integrates three complementary privacy protection layers: (i) MuReQua, a quantum-resilient blockchain consensus mechanism leveraging CQKD for cryptographic robustness against Shor-type attacks; (ii) DeSSE, an information-theoretically secure distributed storage model based on n × m fragmentation, ensuring zero information leakage below reconstruction thresholds; and (iii) a multi-tier data sovereignty model (C0–C3) enforcing policy-driven data locality and regulatory compliance across federated nodes. We formalize privacy guarantees through an adversarial model encompassing classical, quantum, insider, and governance-level threats, and demonstrate that the proposed architecture achieves information-theoretic confidentiality, forward secrecy, and attack-resilient distributed governance. A privacy leakage analysis shows that the probability of data reconstruction under sub-threshold compromise is identical to zero, outperforming conventional blockchain storage models based on encryption alone. Simulation and case study results indicate that Data Communities achieve up to 99.999% service availability, 55% reduction in external data exposure, and 22–35% carbon-aware optimization, while maintaining strict privacy guarantees across distributed environments. Compared with existing blockchain systems (e.g., Ethereum, Hyperledger Fabric), the proposed framework shifts privacy protection from access-control and pseudonymity to structural, information-theoretic privacy by design. Overall, the results establish Data Communities as a scalable and quantum-resilient foundation for next-generation privacy-preserving blockchain infrastructures, bridging distributed AI, secure storage, and cooperative governance under a unified formal model.
The construction industry faces several difficulties in warehouse management along with construction industry&s;s supply chain that is characterized by complex, multi-tiered interactions involving material suppliers, transporters, contractors, and on-site project managers. Traditional management systems suffer from delayed information exchange, lack of transparency, and vulnerability to fraud or errors, often leading to cost overruns and schedule delays. The purpose of the current paper is to suggest an Artificial Intelligence of Things (AIoT) and blockchain-based supply chain management model to be used in the construction industry. AIoT involves the use of IoT devices or RFID tags, GPS trackers, and environmental sensors along with AI algorithmic methods to conduct predictive analytics, anomaly detection, and automated decision-making in the edge or the cloud. Blockchain technology offers the benefit of immutable and transparent records that cannot be altered and is tamper resistant, which facilitates trust among the distributed stakeholders and automates the workflows of the contract through the use of smart contracts. The architecture that is proposed has three layers: 1 AIoT real-time data acquisition sensing and analytics, 2 Secure data storage blockchain ledger and smart contract execution, and 3 A stakeholder application dashboard. In order to test our framework, we conducted a simulation of a scenario with prefabricated steel parts as supply. We determined the effectiveness of the system in tracking items, recording events as swiftly as possible, the security of the process and the efficiency of the whole process. The findings were also staggering: the accuracy of the tracking increased by 92 percent, the reporting is 58 percent quicker, and the prevention of fraud is much more robust than the traditional ERP systems. Such results demonstrate that the convergence of the AIoT and blockchain technologies can contribute to the solution of current issues in the supply chain in construction, which will result in the improved and more data-driven project management. Second, we will experiment with this approach through real life projects and how it could be used with Building Information Modelling (BIM) platforms.
Murugeshwari B., Kayalvizhi S, Daya Florance D, Saranya S
To deploy deep learning-based diagnostic systems in a clinical setting, they need to have not only a high degree of predictive accuracy, but also an unbroken cryptographic chain to prove that the model parameters have not been changed from one inference to the next. This incompatibility arises because softmax, GELU activation, and layer normalization involve transcendental operations to produce the final output. Transcendental operations cannot be represented over the prime finite fields that are necessary when utilizing Rank-1 Constraint Systems (R1CS). The work here provides a mathematically sound approach to resolving the incompatibility by providing three formally defined polynomial approximations: a degree-3 Taylor series softmax approximation (ε ≤ 2.47E-03 per weight); a degree-5 composite polynomial GELU (ε ≤ 1.80E-03); and a squared witness hint reformulation of layer normalization that results in exact constraint satisfaction. The accumulating polynomial approximation errors across 12 transformer encoder blocks have a total approximation error defined as (ε total ≤ 0.0512) and results in a loss of less than one-fifth of a percent in accuracy (94.1% vs. 94.3%) on the RSNA Pneumonia Detection benchmark. The entire ViT-B/16 inference model was compiled into an R1CS form of approximately 2.3×10⁸ R1CS constraints and instantiated as a Groth 16 zk-SNARK. The integrity of the model is confirmed by hashing the parameters using SHA-256 onto an immutable Polygon zkEVM smart contract, allowing for on-chain verification of inference without revealing any of the proprietary model weights. Additionally, this system produces an area under the receiver operating characteristic curve (AUC-ROC) of 0.961, a mean latency for proof generation of 2.84 seconds, an end-to-end verification time of 5.07 seconds, and an average cost for on-chain verification of 0.012ETH, all within the operational constraints of typical radiology workflows.
Abstract Online Education in Higher Education is rapidly evolving through the integration of Large Language Model (LLM)-powered intelligent systems, which enable personalized tutoring, dynamic content generation, and automated assessment. However, the widespread adoption of LLMs in education is hampered due to their inherent limitations, including susceptibility to hallucinations, insufficient domain-specific knowledge validation, and output inconsistency. These deficiencies can lead to misleading or erroneous content, potentially causing significant negative learning outcomes. A core challenge lies in ensuring that such errors are immutably logged and traceable, thereby establishing a mechanism for accountability among the entities deploying these LLM services. To address these challenges, this paper proposes a novel framework that Integrates LLM with consortium blockchain for personalized and verifiable online education. Our design features a synergistic architecture in which LLM based services provide the intelligent educational interface, while a permissioned consortium blockchain serves as a secure and tamper proof ledger. This blockchain infrastructure records critical educational transactions ranging from learning process data and academic credentials to the outputs generated by the LLMs. This integration not only secures academic credentials but also establishes a fully auditable trail, making it possible to trace responsibility for educational deficiencies caused by AI errors. Collectively, this work demonstrates a robust and accountable framework for leveraging LLMs in education, effectively mitigating the risks of AI inaccuracies through the verifiable and immutable nature of consortium blockchain.
Open access
Artificial Intelligence in Healthcare and Education
Online Learning and Analytics
Intelligent Tutoring Systems and Adaptive Learning
Kapil K. Jajulwar, Priya Dasarwar, Uma Yadav, Bhakti Prasad Thakre · 6 authors
Blockchain consensus mechanisms are important to ensure the safe validation of transactions. However, the limitations of high computational complexity, energy consumption, and mining latency restrict the scalability of blockchain in large-scale IP-based and wireless network environments. Current methods mainly rely on single optimization methods without jointly optimizing miner selection and hash computation, resulting in inferior performance under dynamic network conditions. To fill this gap, this study presents a new hybrid bioinspired optimization framework for efficient blockchain mining, integrating Genetic Algorithm (GA), Firefly optimization, and Particle Swarm Optimization (PSO) into a unified architecture to take advantage of their complementary strengths. The proposed method uses both historical and real-time performance metrics to determine the best mining nodes. The Firefly algorithm is used to optimize the selection of hash ranges to reduce CPU workload. PSO is used to select high-performance neighboring nodes for collaborative mining. The model is implemented using the NS-2 simulator and tested over a network of 1000 wireless nodes under different consensus protocols. The experimental results illustrate 4.3% decrease in computational complexity, 4% decrease in energy consumption, and 5% decrease in mining delay. The model further reduces mining complexity by up to 34.2% and reduces the energy utilization from 24.5% to 16.6%, demonstrating its effectiveness for scalable and energy-efficient blockchain deployment.
This perspective examines whether nuclear fusion can provide a scalable, low-carbon power source for rapidly growing AI-driven data center demand. As large language models, cloud computing, and cryptocurrency mining accelerate electricity consumption growth, data centers are projected to account for a substantially larger share of U.S. and global electricity use in the coming decades, creating significant pressure on grid reliability and decarbonization goals. We evaluate the technical and economic alignment between data center load profiles and nuclear power, particularly fusion, through a comparative analysis of capacity factors, levelized cost of electricity, grid interconnection constraints, and deployment pathways. Unlike intermittent renewables, nuclear fission and fusion offer high-capacity-factor, firm baseload generation suited to AI training and inference workloads that require continuous, reliable power. Preliminary techno-economic analysis suggests that several Nth-of-a-kind fusion concepts, particularly magnetic confinement systems, may become cost-competitive with firmed renewable systems and advanced fission for hyperscale data center applications. Co-location of fusion plants with data centers further reduces transmission bottlenecks, improves resilience, and aligns with emerging hyperscaler procurement strategies. We also assess recent regulatory developments and argue that fusion's favorable safety profile and reduced waste burden improve its long-term social and political viability relative to fission. We conclude that fusion represents a strategically important pathway for sustainably powering next-generation computing infrastructure and should be prioritized in both policy and industrial deployment planning.
The Triadic Stress Index (TSI) takes a network index whose four factors were first observed in soil microbiome co-occurrence networks and applies it, without alteration, to the correlation network of financial assets. We test it on five markets spanning 2006-2026 (equities including banking crises and the AI sector, cryptocurrencies, commodities, foreign exchange and sovereign debt), against three independent definitions of a crisis episode, at a fixed alarm budget, out of sample, with block-bootstrap intervals and a Holm correction across the family of tests. The benchmarks are the Absorption Ratio, the industry standard used by MSCI and central banks; the effective rank and the Vendi score, the sharpest spectral measures available; Ollivier-Ricci curvature; and the global and local balance indices of signed correlation networks. Three comparisons favour the index. It carries a per-node decomposition, diag(A^3), naming which asset is carrying the concentration with no parameter to select, and scores 0.97-0.99 against 0.33-0.84 for the only published per-node alternative, whereas spectral attribution must first choose how many components to read and collapses under a standard but wrong choice. Its alarms are the cleanest of anything tested, 4.0% of them with no matching episode against 14.7% for the effective rank and roughly 59% for the Absorption Ratio. And it beats the Absorption Ratio on detection by 0.273 in F1 out of sample, p<0.0005. The remaining comparisons are ties. Against the effective rank and the Vendi score the index ties in every scheme and both samples, and the margin over the Absorption Ratio narrows under the strictest labelling. On real matrices the far simpler node degree reproduces the attribution. A lead-lag analysis puts the peak cross-correlation at zero lag: this is a coincident state index, not a forecast.
This study investigates whether the macroscopic statistical maturity of cryptocurrencies implies dynamical equivalence with traditional equity markets. We analyze high-frequency data (2020--2025) using the Complexity--Entropy Causality Plane (CECP) and directed horizontal visibility graphs (directed HVG) to uncover complex temporal patterns and time-directed structures in the return series. While conventional stylized facts show striking convergence across all assets, structural diagnostics reveal a compelling paradox: cryptocurrencies appear more locally random than the equity benchmark during ordinary periods, yet exhibit significantly stronger directional time-irreversibility around high-visibility return events. The absolute-return results show that large cryptocurrency fluctuations tend to begin abruptly and remain elevated afterward. Separate analyses of positive returns and negative-return magnitudes show that this pattern is shared across cryptocurrencies on the upside but varies across assets on the downside. We conclude that statistical maturity is only skin-deep; the underlying dynamical processes of mature cryptocurrencies remain fundamentally distinct from traditional benchmarks.
Volatility control converts risk estimates into portfolio exposure, yet existing approaches often rely on a fixed volatility estimator or a pre-defined control rule that may not adapt to changing market conditions. We propose VolRouter, a modular framework that formulates volatility control as state-conditioned routing over estimator-controller pairs. VolRouter first summarizes market conditions into a control-relevant state profile and then performs routing through three stages: state inference, switch review, and pair selection. The Router can be implemented using rule-based, learnable, or LLM-based decision modules, while portfolio actions remain generated by predefined control policies. We evaluate VolRouter across S&P 500, Multi-Asset, Bitcoin, and USDT volatility-control settings. VolRouter achieves the highest Sharpe ratio in three of four settings. On S&P 500, it improves Sharpe from 0.952 for RV + Naive Scaling to 1.222 while reducing maximum drawdown from 15.10% to 12.58% and daily CVaR from 1.76% to 1.32%. On Multi-Asset, it improves Sharpe from 1.498 to 1.540 and reduces CVaR from 1.56% to 1.18%. Bitcoin shows similar improvements in risk-adjusted performance, while USDT provides a boundary case where simpler state-aware selectors remain competitive. Ablation and sensitivity analyses show that the improvement comes from relative policy evaluation and selective persistent switching rather than simply expanding the policy library. These results suggest that volatility control can be viewed as a policy-selection problem when risk management requirements vary across market states.
Artificial intelligence is rapidly evolving from isolated models into persistent autonomous infrastructures capable of continuous learning, coordination, adaptation, and real-world operation. As autonomy increases, the central engineering challenge shifts from improving model capabilities to ensuring that autonomous systems remain governable, verifiable, accountable, and trustworthy throughout their operational lifecycle. This paper introduces Governance Guarantees, a formal framework that defines Governed Autonomous Infrastructure (GAI) through implementation-independent operational properties rather than architectural components. It presents a collection of original concepts, including Governance Integrity, Governance Capacity, Governance Saturation, Governance Pressure, Operational Heat, Governance Drift, Governance Continuity, Governance Surface, and Verifiable Governance, providing a unified vocabulary for engineering and evaluating autonomous infrastructures. The paper further proposes Bounded Governance Proof Objects and an incremental verification methodology that enables infrastructures to generate continuous governance evidence without requiring complete formal verification of the entire system. Together, these concepts establish a practical foundation for observable, reproducible, and evidence-based governance of persistent autonomous intelligence. Finally, the paper interprets the AETHELIS ecosystem as the first operational implementation intentionally designed to satisfy these Governance Guarantees, demonstrating how governance can become an intrinsic infrastructural property rather than an external supervisory mechanism. The work argues that future autonomous intelligence should be evaluated not only by its capabilities, but by its ability to continuously preserve governance throughout operation.
Kangkang Li, Huan Yu, Han Wang, Shirong Hu · 7 authors
Landslide susceptibility mapping in alpine regions with limited data has two persistent obstacles: too few recorded landslide cases and the questionable reliability of non-landslide samples. We address these issues with a hybrid generative framework—a Variational Autoencoder (VAE) performs latent-space interpolation to produce additional positive samples, while a Conditional Diffusion Model (CDM) generates counterfactual negative samples. To validate generation quality, we used Principal Component Analysis (PCA) scatterplots, convex hull containment analysis, and covariance structure checks. The generated samples were then evaluated across five classifiers (Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and The Convolutional Neural Network-Transformer-Long Short-Term Memory-Graph Convolutional Network (CTLGNet)). VAE-CDM consistently outperformed the original dataset, Synthetic Minority Over-sampling Technique (SMOTE), and Conditional Tabular Generative Adversarial Network (CTGAN), with XGBoost achieving the best results—Area Under the Curve (AUC) of 0.9337, recall of 0.91, and F1 of 0.87. DeLong’s test confirmed the AUC gain was statistically significant (p = 0.043). After augmentation, the very-high-susceptibility capture rate rose from 68.5% to 89.4%, while cumulative capture curves reflected a 4.9% improvement in spatial targeting efficiency. Shapley Additive Explanations (SHAP) analysis highlighted distance to roads, elevation, and the Freeze-Thaw Index (FTI) as the dominant controlling factors. Application to Southeastern Xizang shows the framework offers a viable pathway for susceptibility mapping in data-limited environments, although the generated samples show moderate diversity reduction (0.53) due to the interpolation-based generation strategy.
Z. P. Zhu, Clarence Lam, Alexander Frolov, Ian Miers · 5 authors
We present zk-Analytics, a distributed cloud analytics system that enables publicly verifiable analytics without revealing raw logs or relying on trusted hardware in analytics providers' infrastructure. Today's cloud analytics are largely self-assertive: providers collect telemetry, perform aggregation, and report results, leaving external parties unable to verify correctness without access to sensitive data or trusted execution environments. zk-Analytics addresses this gap by augmenting analytics pipelines with lightweight append-only log commitments and verifiable aggregation and query execution using zero-knowledge proofs. The system cleanly separates online log commitment from offline, distributed batch aggregation and query verification, enabling scalability while keeping online overhead low. We implement zk-Analytics using a zkVM-based execution environment and evaluate it on real-world and synthetic workloads, demonstrating that verifiable, privacy-preserving cloud analytics is feasible for real-world cloud workloads. zk-Analytics is open-sourced at https://github.com/Froot-NetSys/zk-Analytics.
Decentralized Finance (DeFi) refers to an open financial ecosystem built on blockchain technology that does not require the participation of centralized institutions. The technology and operational mechanisms it employs represent a significant "paradigm mismatch" with the current financial regulatory framework. This paper examines the comprehensive impact of DeFi on existing financial regulation from multiple perspectives, including the blurring of regulatory authority and a lack of accountability; the difficulty in identifying regulatory targets and the ambiguity in determining their nature; the ineffectiveness of regulatory rules and the absence of relevant provisions; overlapping jurisdictions, and difficulties in enforcement. Through a comparative study of regulatory experiences in the United States, Europe, and other regions, this paper proposes solutions such as shifting the existing regulatory philosophy toward functional regulation, embedding compliance requirements into the underlying technology at the institutional level, and strengthening international cooperation at the operational level, while also discussing the specific context in China. This paper identifies a threefold paradigm mismatch between decentralized finance and traditional financial regulation, giving rise to multiple regulatory challenges such as difficulties in holding entities accountable, ambiguity in defining regulatory targets, ineffective regulatory rules, and obstacles to cross-border enforcement. A comparison of regulatory practices in the U.S. and Europe reveals that it is difficult for any single country to independently manage the risks associated with globalized DeFi.
Ira Nath, Sanjukta Chatterjee, Rangan Nath, Rumpa Paul · 5 authors
CryptC provides users with secure wallet services to protect their digital cryptocurrency assets as a modern cryptocurrency solution. Multiple platforms can adopt CryptC through the React Native interface while users can enjoy easy access using authentication from Firebase and Firestore for data and security features. Ganache with ethers.js enables the wallet to perform safe blockchain transactions while operating on a local Ethereum blockchain through its Ganache access. Secure compliance requirements are achieved by the platform through its transaction logging system and scalable functionality and biometric asset security measures, and balance update capabilities. The platform features an interface that combines professional and beginner user capabilities through an interactive dashboard, together with horizontal list presentation and user-focused design execution. The DeFi (Decentralized finance) ecosystem tool CryptC provides real-time operation capabilities that outperforms conventional wallet features like PIN base verification, Real-time Ethereum (ETH) transaction, minimalistic mobile-friendly UI etc. Our work provides comprehensive information about CryptC, along with its unique design specification through android App and security protocols, while validating the platform for payments at multiple operational levels.
Biometric authentication offers enhanced usability for digital payments, but traditional centralized architectures suffer from single points of failure. While blockchain integration promises decentralized trust, existing solutions often store biometric helper data (e.g., fuzzy commitments) directly on-chain. We demonstrate that such transparency, even in permissioned settings, exposes low-entropy biometric inputs to offline brute-force attacks and identity-linkage risks if the immutable ledger is accessed by malicious nodes. To resolve this transparency–privacy paradox, we propose a threshold-based decentralized authentication framework. Unlike prior works, our protocol keeps all sensitive Biometric-Enhanced Key Derivation (BEKD) tokens entirely off-chain, using the blockchain solely for freshness enforcement. We provide a game-based security analysis of brute-force resistance, unforgeability, and unlinkability. Our experimental results demonstrate that our scheme’s gas cost is acceptable, offering a robust solution for self-sovereign biometric identity.
What is real has always been something we find , not something we make —or so philosophy has assumed. This paper argues otherwise. Characterizing reality through resistance rather than substance (the ways the world refuses a subject’s mastery), I distinguish three modalities correlative to epistemic, judgmental, and practical mastery: Substrate (matter’s resistance to representation), Contingency (the forceful givenness of experience that resists revision by judgment), and the Inexorable (structures’ resistance to intervention). Treating virtual environments, AI agents, and blockchain smart contracts not as proofs but as revelatory cases, I show that technology now extends the latter two modalities, Contingency and the Inexorable, artificially. The result is the paper’s central concept, Artificial Externality : human-made structures whose resistance to intervention is deliberately engineered to be practically insurmountable, even for their creators, and that thereby acquire an externality once attributed only to nature. Absoluteness, traditionally found, can now be produced. I close by drawing out the stakes for consciousness: our criteria for what counts as real quietly shape our criteria for what counts as conscious.
This paper examines how Bitcoin returns interact with macroeconomic and financial drivers—specifically inflation, industrial production, money supply, stock market returns, the wholesale price index, and financial conditions—using monthly data from April 2015 to March 2025. Methodologically, we apply Augmented Dickey-Fuller (ADF) tests, ordinary least squares (OLS) regression, and vector autoregression (VAR) modelling. Because not all variables are stationary at levels, the VAR model is estimated with differencing. The OLS results indicate that traditional macroeconomic factors do not effectively explain Bitcoin returns. However, the VAR analysis reveals that inflation significantly Granger-causes Bitcoin returns, whereas financial conditions and equity markets show negligible predictive power. Impulse response functions confirm that macroeconomic shocks hit Bitcoin only in the short term, and variance decomposition shows that over 84% of Bitcoin’s volatility is driven by its own innovations. We conclude that Bitcoin remains a largely decoupled, self-driven asset with minimal integration into traditional macroeconomic fundamentals, despite a modest predictive link to inflation. JEL classification numbers: G12, E31, E44. Keywords: Cryptocurrency, macroeconomic, VAR model, ADF test.