Blockchain Papers

McGirl, Timothy

Veil: Verified Encrypted Intelligence LayerA Censorship-Resistant Communication Protocol Using Blockchain-Derived Ephemeral Keys Overview The Bitcoin-Hashed Transport Protocol (BHTP) is a novel time-based obfuscation layer that renders encrypted network traffic statistically indistinguishable from random noise. By deriving ephemeral encryption keys from blockchain data, BHTP eliminates the cryptographic handshakes and traffic signatures exploited by Deep Packet Inspection (DPI) systems for protocol identification and censorship. Key Features Handshake-Free Encryption: Keys derived from publicly observable blockchain data—no key exchange to fingerprint Traffic Indistinguishability: AES-256-GCM ciphertext with standardized padding appears as random bytes Layered Security: "Russian Doll" architecture separates transport obfuscation from payload confidentiality Automatic Key Rotation: ~10-minute (Bitcoin) or ~5-second (Stellar) key lifecycle Synchronization Tolerance: Lookback window handles propagation latency Minimal Overhead: ~0.2ms computational cost per message Versions Version Entropy Source Key Rotation Smart Contracts Status v1.1 Bitcoin ~10 min No Current v2.0 Stellar ~5 sec Soroban Specified v3.0 Hybrid Oracle ~5 sec Soroban + VRF Planned Architecture ┌─────────────────────────────────────────────────────────┐ │ BHTP Message │ ├─────────────────────────────────────────────────────────┤ │ ┌───────────────────────────────────────────────────┐ │ │ │ Outer Layer (Transport) │ │ │ │ AES-256-GCM + BLAKE3(Blockchain) │ │ │ │ Key Lifetime: ~10 min / ~5 sec │ │ │ │ Purpose: Censorship Resistance │ │ │ │ ┌─────────────────────────────────────────────┐ │ │ │ │ │ Inner Layer (Payload) │ │ │ │ │ │ NIP-44 / XChaCha20-Poly1305 │ │ │ │ │ │ Key Lifetime: Indefinite │ │ │ │ │ │ Purpose: Confidentiality │ │ │ │ │ │ ┌───────────────────────────────────────┐ │ │ │ │ │ │ │ Original Message │ │ │ │ │ │ │ └───────────────────────────────────────┘ │ │ │ │ │ └─────────────────────────────────────────────┘ │ │ │ └───────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────┘ Quick Start Key Derivation (Bitcoin) use blake3::Hasher; pub fn derive_transport_key( block_hash: &[u8; 32], prev_hash: &[u8; 32], timestamp: u64, ) -> [u8; 32] { let mut hasher = Hasher::new(); hasher.update(block_hash); hasher.update(prev_hash); hasher.update(&timestamp.to_be_bytes()); *hasher.finalize().as_bytes() } Key Derivation (Stellar) pub fn derive_transport_key_stellar( ledger_sequence: u64, prev_ledger_hash: &[u8; 32], close_time: u64, vrf_output: Option<&[u8; 32]>, ) -> [u8; 32] { let mut hasher = blake3::Hasher::new(); hasher.update(&ledger_sequence.to_be_bytes()); hasher.update(prev_ledger_hash); hasher.update(&close_time.to_be_bytes()); if let Some(vrf) = vrf_output { hasher.update(vrf); } *hasher.finalize().as_bytes() } Applications Private AI Access BHTP enables invisible AI API communication: User ←→ BHTP Client ←→ [Random Noise] ←→ BHTP Relay ←→ AI Provider Access AI from censored regions Private AI usage in corporate environments No metadata about prompts or usage patterns Censorship-Resistant Messaging Standard Nostr messaging with transport obfuscation via Kind 10059 events. Event Structure { "kind": 10059, "created_at": 1702300800, "tags": [ ["h", "000000000000000000024bead8df69990852c202db0e0097c1a12ea637d7e96d"], ["e", "bitcoin"], ["p", "recipient_pubkey_hex"], ["iv", "random_nonce_hex"] ], "content": "base64_encoded_ciphertext...", "pubkey": "sender_pubkey_hex", "sig": "schnorr_signature_hex" } Security Model Property Outer Layer Inner Layer Algorithm AES-256-GCM XChaCha20-Poly1305 Key Source BLAKE3(Blockchain) ECDH (secp256k1) Key Lifetime ~10 min / ~5 sec Indefinite Provides Obfuscation Confidentiality Recoverable By Anyone (public chain) Private key holder only Hardening Roadmap Phase Features v1.1 Core protocol, Bitcoin entropy v1.2 Timing jitter, rate limiting, Noise Protocol v2.0 Stellar entropy, 5-sec rotation, Soroban v3.0 Hybrid VRF oracle, constant-rate shaping, Nym mixnet Requirements Rust [dependencies] blake3 = "1.5" aes-gcm = "0.10" bitcoin = "0.31" # For v1.1 soroban-sdk = "20.0.0" # For v2.0+ JavaScript npm install blake3 @noble/ciphers bitcoinjs-lib stellar-sdk Documentation BHTP_Specification_v1.1.md - Full protocol specification BHTP_Stellar_Specification_v2.0.md - Stellar-based specification BHTP_Soroban_Contract_Architecture.md - Smart contract details Citation @techreport{mcgirl2025bhtp, author = {McGirl, Timothy}, title = {The Bitcoin-Hashed Transport Protocol: A First-Principles Approach to Metadata-Resistant Communication}, year = {2025}, month = {December}, institution = {Independent Research}, type = {Technical Specification}, version = {1.1} } To strengthen the decoy strategy, implement an automated traffic generation module that produces fake, padded events indistinguishable from legitimate traffic1. Configure the system to support a variable decoy-to-real ratio (e.g., defaulting to 0 but allowing up to 10:1 for high-security contexts) to flood relays with noise2. Future iterations should integrate deterministic traffic shaping, where clients transmit fixed-size buckets at constant intervals (e.g., every 8 seconds), ensuring that 90% of the stream is decoy data to eliminate volume-based fingerprinting entirely. To eliminate all government and corporate spying, one must achieve a state of "Zero-Trust Sovereignty" where no data leaves your control without mathematically unbreakable encryption and total metadata obfuscation. This requires running all software on open-source, user-audited hardware (such as RISC-V) to eliminate supply-chain backdoors, and routing all network traffic through a multi-hop, mixnet-integrated transport layer (like the proposed BHTP-Stellar architecture) to render communication statistically indistinguishable from background noise. Ultimately, 100% privacy demands the complete decoupling of identity from infrastructure: using distinct, ephemeral cryptographic keys for every interaction, funding operations solely through private decentralized ledgers (e.g., Monero), and physically isolating critical endpoints in Faraday environments to prevent hardware-level signal exfiltration. ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- The Bitcoin-Hashed Transport Protocol A First-Principles Approach to Metadata-Resistant Communication Technical Specification v1.1 — Proposed NIP Timothy McGirl • Independent Researcher • December 2025 Abstract Modern encrypted communication protocols achieve strong content confidentiality but systematically fail to protect communication metadata. Deep Packet Inspection (DPI) systems can identify, track, and block encrypted communications without decrypting payload content. This paper presents the Bitcoin-Hashed Transport Protocol (BHTP), a novel time-based obfuscation layer that leverages the Bitcoin blockchain as a globally synchronized source of cryptographic entropy. By deriving ephemeral AES-256-GCM encryption keys from Bitcoin blockchain data using BLAKE3, BHTP eliminates the cryptographic handshakes and traffic signatures that enable DPI systems to identify and block encrypted protocols. The protocol implements a "Russian Doll" architecture: an outer transport layer providing censorship resistance through time-based obfuscation (~10-minute key rotation), and an inner payload layer (NIP-44) providing end-to-end confidentiality through XChaCha20-Poly1305. This specification includes complete cryptographic construction, formal security analysis, threat model evaluation, padding schemes for anti-fingerprinting, lookback windows for synchronization tolerance, failure mode handling, performance benchmarks (~0.2ms overhead), and reference implementation in Rust. Proposed as a Nostr Implementation Possibility (NIP) using event kind 10059. Keywords: traffic analysis, censorship resistance, metadata protection, Bitcoin, Nostr, ephemeral encryption, deep packet inspection, protocol obfuscation 1. Introduction The fundamental promise of cryptography is confidentiality: the assurance that only intended recipients can access protected information. Modern encryption algorithms fulfill this promise with remarkable effectiveness—AES-256, ChaCha20-Poly1305, and elliptic curve cryptography provide computational security guarantees that render brute-force attacks infeasible. Yet despite these achievements, encrypted communications remain systematically vulnerable to traffic analysis, a class of attacks that bypass cryptographic protections entirely by exploiting metadata: who communicates with whom, when, how frequently, and data volume exchanged. The metadata problem is not theoretical. DPI systems deployed at national firewalls identify and selectively block encrypted protocols based on traffic signatures. The Great Firewall of China, Iran's filtering infrastructure, and similar systems exploit handshake patterns, packet size distributions, timing correlations, and protocol-specific headers. Former NSA Director Michael Hayden's statement "We kill people based on metadata" accurately reflects the operational value sophisticated adversaries extract from communication patterns. 1.1 Limitations of Existing Solutions Existing approaches to metadata protectio

McGirl, Timothy

The Bitcoin-Hashed Transport Protocol: A First-Principles Approach to Metadata-Resistant Communication Technical Specification v1.1 — Proposed Nostr Implementation Possibility (NIP) Overview Modern encrypted communication protocols achieve strong content confidentiality but systematically fail to protect communication metadata. Deep Packet Inspection (DPI) systems deployed at national firewalls and network chokepoints can identify, track, and selectively block encrypted communications without ever decrypting payload content—exploiting handshake patterns, packet size distributions, timing correlations, and protocol-specific signatures. The Bitcoin-Hashed Transport Protocol (BHTP) addresses this fundamental limitation through a novel approach: deriving ephemeral transport encryption keys from the Bitcoin blockchain, a globally synchronized and publicly observable source of cryptographic entropy. By eliminating key exchange negotiations entirely, BHTP renders encrypted traffic statistically indistinguishable from random noise to any observer not synchronized with the blockchain. Technical Architecture The Russian Doll Model BHTP implements a layered security architecture providing defense in depth: Outer Layer (Transport Obfuscation): AES-256-GCM encryption with keys derived via BLAKE3 from Bitcoin block hashes. Keys rotate approximately every 10 minutes with each new block. Provides censorship resistance by defeating real-time traffic analysis. Inner Layer (Payload Confidentiality): Standard NIP-44 encryption using XChaCha20-Poly1305 with keys derived from ECDH between Nostr identity key pairs. Provides true cryptographic confidentiality independent of transport layer security. This separation reflects that censorship resistance and confidentiality are orthogonal concerns with different security requirements and threat models. Key Derivation Function Kₙ = BLAKE3( Hₙ ‖ Hₙ₋₁ ‖ Tₙ ) Where: Hₙ: Current block hash (32 bytes) Hₙ₋₁: Previous block hash (32 bytes) Tₙ: Block timestamp (8 bytes, big-endian) The 72-byte input produces a 256-bit AES key. Including both current and previous hashes prevents edge-case failures during block propagation and increases entropy. Protocol Specification Event Structure (Kind 10059) json { "kind": 10059, "created_at": <unix_timestamp>, "tags": [ ["h", "<block_hash_hex>"], ["p", "<receiver_pubkey>"], ["iv", "<aes_gcm_nonce_hex>"] ], "content": "<base64_ciphertext>", "pubkey": "<sender_pubkey>", "sig": "<schnorr_signature>" } Anti-Fingerprinting Measures Standardized Padding: ISO/IEC 7816-4 padding to bucket sizes (1 KiB, 16 KiB, 256 KiB, 1 MiB) prevents size-based traffic analysis Lookback Window: Decryption attempts against Hₙ, Hₙ₋₁, Hₙ₋₂ accommodate block propagation latency and minor reorganizations Timestamp Validation: Events rejected if created_at exceeds 20 minutes from referenced block timestamp Failure Mode Handling Missing block headers: Queue messages until consensus reestablished (MUST NOT fallback to cleartext) Decryption failure: Retain temporarily for potential reorganization; discard after 1 hour Inner layer failure: Discard silently (message not intended for recipient) Security Analysis Threat Model Assumes adversary with: network observation at backbone level, sophisticated DPI capabilities, active probing, historical traffic recording ("harvest now, decrypt later"), and full blockchain access. Bounded by: no endpoint compromise, no private key access, no blockchain manipulation capability. Security Properties Traffic Indistinguishability: AES-256-GCM ciphertext is computationally indistinguishable from random bytes; bucket padding eliminates size-based fingerprinting Cost Asymmetry: Legitimate users: ~0.2ms per message. Mass surveillance adversary: O(N × B) decryption attempts for N packets across B blocks Layer Independence: Transport layer compromise reveals only NIP-44 ciphertext; inner layer security unaffected The Permanent Record Threat: Explicitly acknowledged—outer layer provides temporal obfuscation, not long-term secrecy. Inner NIP-44 layer provides actual confidentiality. Performance Characteristics Operation Time Throughput BLAKE3 (72 bytes) ~50 ns 1.4 GB/s AES-256-GCM (1 KB) ~150 ns 6.6 GB/s Total per message ~0.2 ms 5,000 msg/s Bandwidth overhead: ~3x for small messages (dominated by padding), decreasing proportionally for larger payloads. Implementation Bitcoin Header Acquisition Options: Full node (most trustworthy, ~500 GB storage) SPV client (~50 MB headers with proof-of-work validation) Multi-API queries (lightweight, trust assumptions) Library Requirements: Rust: blake3, aes-gcm, bitcoin crates JavaScript: blake3, @noble/ciphers, bitcoinjs-lib Python: blake3, cryptography, python-bitcoinlib Reference implementation provided in Rust demonstrating complete encryption/decryption flow. Contributions Complete cryptographic construction for time-based transport obfuscation using Bitcoin block hashes Layered "Russian Doll" security architecture separating censorship resistance from confidentiality Full protocol specification with data structures, procedures, padding, and failure handling Formal security analysis with proofs for indistinguishability, cost asymmetry, and layer independence Performance benchmarks and implementation guidance Keywords traffic analysis, censorship resistance, metadata protection, Bitcoin, Nostr, ephemeral encryption, deep packet inspection, protocol obfuscation, BLAKE3, AES-256-GCM, NIP-44, decentralized communication

Rayya Ruwa'im Nafie, Anggraini Puspita Sari, Achmad Junaidi

Bitcoin’s extreme volatility continues to challenge accurate forecasting and risk management. Traditional econometric approaches struggle with the nonlinear and shifting dynamics of cryptocurrency markets, while deep learning models such as the Gated Recurrent Unit (GRU) often lack interpretability and adaptability to regime changes. To address these limitations, this study introduces a hybrid Gaussian Hidden Markov Model–Gated Recurrent Unit (HMM-GRU) framework for Bitcoin price forecasting. The HMM identifies latent market regimes from four years of daily closing prices and integrates these states as auxiliary features for the GRU network. Experimental results show that the hybrid model consistently surpasses the standalone GRU in predictive accuracy. Under the optimal configuration, HMM-GRU achieves a Mean Absolute Error (MAE) of 1,557.33 and a Mean Absolute Percentage Error (MAPE) of 1.42%, compared with 1,713.30 and 1.57% for GRU, representing an approximate 9% improvement in both absolute and relative error performance. The inclusion of regime-based features enables the model to better capture market transitions and mitigate overfitting to short-term noise. Beyond performance gains, the proposed approach enhances interpretability by linking forecasts to identifiable market regimes. These findings highlight the value of combining statistical regime detection with deep learning for volatile financial assets, providing practical insights for both investors and researchers in time-series forecasting.

Viljami Vasku

The aim of this thesis is to examine the pricing and efficiency of Bitcoin options. It reviews theories of market efficiency and considers how effectively these frameworks apply to cryptocurrency markets. The thesis examines multiple option pricing models by comparing their performance for pricing Bitcoin options. Bitcoin’s high volatility and the relatively young age of its market development highlight the need to analyze how these characteristics influence both option pricing and overall market efficiency. In addition, the thesis examines the characteristics of Bitcoin options. The study provides guidelines for future research and market development, helping to build trust and support the integration of cryptocurrency derivatives into the broader financial system. Tämän opinnäytetyön tavoitteena on tarkastella Bitcoin-optioiden hinnoittelua ja markkinoiden tehokkuutta. Työssä käydään läpi markkinatehokkuuden teorioita ja arvioidaan, kuinka hyvin nämä viitekehykset soveltuvat kryptovaluuttamarkkinoihin. Opinnäytetyössä tarkastellaan useita optioiden hinnoittelumalleja vertailemalla niiden toimi- vuutta Bitcoin-optioiden hinnoittelussa. Bitcoinin korkea volatiliteetti ja sen markkinoiden suhteellisen varhaisessa kehitysvaiheessa oleva tila korostavat tarvetta analysoida, miten nämä ominaisuudet vaikuttavat sekä optioiden hinnoitteluun että markkinoiden yleiseen tehokkuuteen. Lisäksi työssä tarkastellaan Bitcoin-optioiden erityispiirteitä. Tutkimus tarjoaa suuntaviivoja tu- levalle tutkimukselle ja markkinoiden kehittämiselle, ja sen tavoitteena on lisätä luottamusta sekä tukea kryptovaluuttajohdannaisten integroitumista laajempaan finanssijärjestelmään.

Mohammad Sharif Karimi, Omar Esqueda, Naveen Mahasen Weerasinghe

This study employs a quantile-on-quantile connectedness approach to analyze the asymmetric, distribution-dependent, and time-varying spillovers between FinTech indices and traditional financial markets. The results show that spillovers are concentrated in the distribution tails, with FinTech indices exhibiting strong co-movements with equities and Bitcoin under extreme conditions, while linkages with U.S. Treasury bonds are weaker and often inverse. Net connectedness analysis reveals that the S&amp;P 500 and Bitcoin act as the primary transmitters of shocks into FinTech indices, whereas Treasuries generally serve as receivers, except during stress episodes when safe-haven flows or heightened credit risk reverse the direction of spillovers. The dynamic ∆TCI (Difference between the total direct connectedness and the reverse total connectedness) further demonstrates that FinTech indices serve as net transmitters in stable markets but become receivers during crises such as the COVID-19 pandemic, the Federal Reserve’s tightening cycle of 2022–2023, and the FTX-driven crypto collapse. Segmental heterogeneity is also evident: distributed ledger firms are highly sensitive to cryptocurrency dynamics, alternative finance providers respond strongly to both equity and bond markets, and digital payments firms are primarily influenced by equity spillovers. Overall, the findings underscore FinTech’s dual role—transmitting shocks during tranquil periods but amplifying systemic vulnerabilities during crises. For investors, diversification benefits are state-dependent and largely disappear under adverse conditions. For regulators and policymakers, the results highlight the systemic importance of FinTech–equity and crypto–ledger linkages and the need to integrate FinTech exposures into macroprudential surveillance to contain volatility spillovers and safeguard financial stability.

Zijiang Yang

This paper will propose a novel machine learning based portfolio management method in the context of the cryptocurrency market. Previous researchers mainly focus on the prediction of the movement for specific cryptocurrency such as the bitcoin(BTC) and then trade according to the prediction. In contrast to the previous work that treats the cryptocurrencies independently, this paper manages a group of cryptocurrencies by analyzing the relative relationship. Specifically, in each time step, we utilize the neural network to predict the rank of the future return of the managed cryptocurrencies and place weights accordingly. By incorporating such cross-sectional information, the proposed methods is shown to profitable based on the backtesting experiments on the real daily cryptocurrency market data from May, 2020 to Nov, 2023. During this 3.5 years, the market experiences the full cycle of bullish, bearish and stagnant market conditions. Despite under such complex market conditions, the proposed method outperforms the existing methods and achieves a Sharpe ratio of 1.01 and annualized return of 64.26%. Additionally, the proposed method is shown to be robust to the increase of transaction fee.

Imen Ben Achour1, Jihed Majdoub2

Industry 4.0 and digital transformation have accelerated the emergence of virtual assets such as cryptocurrencies. Among them, Bitcoin, a virtual currency, has captured significant attention from both finance theorists and practitioners, achieving the highest market capitalization to date. The objective of this study is to examine the behavior and interrelationships between Bitcoin and several traditional financial assets within the framework of an international diversification strategy that combines conventional and crypto assets. In this context, Bitcoin is considered as a potential new asset class for portfolio diversification. To explore this relationship, we analyze the links between Bitcoin and a selection of major currencies—EUR, GBP, and JPY—as well as certain commodities. The study employs the Value at Risk (VaR) approach using three empirical methods, complemented by Conditional Value at Risk (CVaR) as a robustness measure, given its ability to capture tail risk more effectively than VaR. Using daily data from October 29, 2016, to October 23, 2020, the findings reveal that including Bitcoin in a diversified portfolio can significantly enhance risk–return characteristics. These results provide new insights for portfolio managers and investors seeking optimal diversification strategies in the context of digital finance.

Sony Hartono, Roby Syaiful Ubed, Riko Riandoko, Irfan Nabhani

The fair value assessment of Bitcoin has become a pivotal concern in contemporary finance because to its significant volatility, constrained supply, and increasing institutional acceptance. In contrast to conventional assets influenced by cash flows, Bitcoin's value is determined by scarcity, community trust, network utility, and regulatory considerations. This research utilizes a qualitative grounded theory methodology by examining interviews and podcasts featuring prominent Indonesian cryptocurrency specialists. Coding analysis identifies four fundamental paradigms: legitimacy and decentralization as value sources, regulation and market infrastructure as value determinants, speculative risk and centralization as value detractors, and investment strategies and price cycles as moderating influences. Research indicates that Bitcoin's fair value depends on value base, value transfer, risk discount, and investor strategy. The research underscores the imperative for unconventional valuation models that incorporate on-chain data, regulatory frameworks, and market dynamics to improve transparency, accountability, and confidence in the digital financial ecosystem.

Lalan Kumar, S H Manjula

Blockchain systems, such as Bitcoin and Ethereum 2.0, face vulnerabilities under bandwidth-constrained partitions, where throughput collapses and latency increases. In addition, adversaries can exploit inconsistencies to launch double-spending attacks. This study presents a lightweight dual-layer countermeasure that integrates a robust freezing threshold ( ) with multi-signal disconnection proofs to enhance performance and security without altering consensus rules. Controlled simulation experiments on Bitcoin (PoW) and Ethereum 2.0 (PoS) show throughput gains exceeding 1000% in Ethereum and over 100% in Bitcoin, with inconsistency reduced by up to 64% and latency bounded within 5-6 blocks/s. These results confirm that attacker-aware thresholds and multi-signal validation substantially improve blockchain resilience under partitioned network conditions.

Alejandro Rabinovich

Purpose: To investigate Bitcoin’s cyclic price behavior around scheduled halving events, develop a technical‑analysis‑based active investment strategy tailored to these cycles, and rigorously assess its performance relative to a passive buy‑and‑hold benchmark. Design/Methodology/Approach: This research employs historical daily BTC /USD price series (June 2012–May 2025), applies a suite of technical indicators to define systematic, halving‑anchored entry and exit rules, and then conducts rigorous statistical evaluations to test whether Bitcoin’s protocol‑driven supply cycles yield reproducible, actionable investment signals. Findings: Over thirteen overlapping sample windows, the active strategy outperforms passive BTC holding in ten, with positive “alpha” coefficients that are statistically significant at the conventional 5% level in each of those windows (and, in most cases, with p-values below 2.5%). It captures outsized gains in post-halving bull runs (e.g. 2013, 2017, 2021) and meaningfully limits drawdowns in bear phases (e.g. 2014, 2018, 2022). Equity curve simulations demonstrate compounded account growth that markedly surpasses passive returns. Practical Implications: Crypto asset managers and individual investors can implement the halving‑centric strategy using readily available charting tools and API‑accessible price feeds to automate buy/sell signals, thereby enhancing return potential and mitigating drawdowns without requiring deep on‑chain analytics expertise. This framework also provides a transparent risk‑management overlay—leveraging predefined exit rules—that can be calibrated to varying risk tolerances and seamlessly integrated into broader multi‑asset portfolios. Originality/Value: This study is among the first to integrate Bitcoin’s protocol‑driven halving schedule with a multi‑indicator technical framework and to validate its efficacy through extensive statistical tests over four market cycles (including the 2024 halving). It offers practitioners a replicable, data‑driven strategy for navigating crypto’s unique cyclical dynamics.

Sithumi Wickramasinghe, Bikramjit Das, Dorien Herremans

Bitcoin mining hardware acquisition requires strategic timing due to volatile markets, rapid technological obsolescence, and protocol-driven revenue cycles. Despite mining's evolution into a capital-intensive industry, there is little guidance on when to purchase new Application-Specific Integrated Circuit (ASIC) hardware, and no prior computational frameworks address this decision problem. We address this gap by formulating hardware acquisition as a time series classification task, predicting whether purchasing ASIC machines yields profitable (Return on Investment (ROI) >= 1), marginal (0 < ROI < 1), or unprofitable (ROI <= 0) returns within one year. We propose MineROI-Net, an open-source Transformer-based architecture designed to capture multi-scale temporal patterns in mining profitability. Evaluated on data from 20 ASIC miners released between 2015 and 2024 across diverse market regimes, MineROI-Net outperforms recurrent, convolutional, and attention-based baselines, achieving 83.2% accuracy and 83.5% macro F1-score. The model demonstrates strong economic relevance, achieving 97.8% precision in detecting unprofitable periods and 81.5% precision in detecting profitable ones, while avoiding misclassifying profitable scenarios as unprofitable and vice versa. These results indicate that MineROI-Net offers a practical, data-driven tool for timing mining hardware acquisitions, potentially reducing financial risk in capital-intensive mining operations.

Sara Abossedgh, Ali Yeganeh, Arne Johannssen

Crypto analysts have to deal with a variety of challenges, with the most important area being the price volatility of cryptocurrencies. Due to uncertain market trends, many studies have been conducted on forecasting techniques, and some of these techniques have been integrated with advanced analytical tools, including machine learning (ML) techniques. Making reliable predictions of the speculative behavior of financial assets, especially in non-stationary and highly volatile environments such as the cryptocurrency market, is a challenging task. In this study, ML techniques are used to identify influential features that affect the prices of cryptocurrencies, especially for Bitcoins. In addition, multivariate control charts are utilized for signal detection, allowing for a structured approach to develop trading strategies for seasonal market conditions. Unlike other studies that do not take seasonality into serious consideration when analyzing market fluctuations, the proposed approach explicitly accounts for it. The developed strategy is tested across various market conditions, including the final days of each year from 2019 to 2024, and demonstrates strong and consistent performance in all cases. By systematically identifying key on-chain features and analyzing them by means of control charts, this study develops a structured approach to anomaly-based trading strategies in Bitcoins. These discoveries address an extensive discussion on automated trading systems, demonstrating that feature selection, technical indicators, market seasonality, and halving impacts are important components in hinting at successful cryptocurrency exchange strategies.

Antonio Martínez Raya, Alejandro Segura de la Cal, Javier Espina Hellín

Since its launch in 2009, Bitcoin has become a market disruptor due to its primary function as a virtual currency supported by blockchain technology and the high volume of economic transactions it facilitates. This article examines the key theoretical principles that have contributed to Bitcoin’s recognition as a cryptocurrency. It assesses whether Bitcoin meets the criteria for being considered a form of money and evaluates its importance as a financial asset. This analysis of Bitcoin from 2014 to 2025 reveals that it does not sufficiently fulfill all the typical functions of money, such as serving as an internationally accepted means of payment, a unit of account, a securities depository, and a standard for deferred payments. Despite its usual close correlation with stock indices in financial markets, a decentralized digital currency like this still does not meet the requirements of fundamental analysis. In practice, this leads to its exclusion as a currency, since it does not fulfill the functions of money nor fully qualify as a crypto asset, as its value is primarily based on investors’ expectations of high returns. Apart from a lack of foundation in tangible goods or services that justifies their value and dependence on new investors, the findings do not indicate conditions typical of a developed pyramidal model. Nevertheless, this does not prevent future technological innovations from responding positively to the functions of money or from offering real money services, especially those related to service innovation and the digital economy.

Hae Sun Jung, Haein Lee

This study conducts a bibliometric review of Bitcoin research in the Business and Economics domains, using VOSviewer to visualize network structures and Bidirectional Encoder Representations from Transformers Topic (BERTopic) to derive semantically coherent topic clusters. The analysis identifies five major research themes: (1) Diversification, hedging, and safe-haven properties; (2) Market dynamics, efficiency, and investor behavior; (3) Bitcoin price and volatility prediction attempts; (4) Environmental impact of Bitcoin; and (5) Financial impact of Central Bank Digital Currency (CBDC). Based on these themes, the study recommends further investigation into the influence of Exchange-Traded Fund (ETF) approvals, regulatory frameworks, and institutional investor participation on Bitcoin’s safe-haven potential; the role of market dynamics and regulatory interventions; early detection of herding behavior and price bubbles; the integration of machine learning and deep-learning models for price prediction; the environmental costs associated with mining; and the evolving regulatory and implementation challenges of CBDCs. Overall, this review synthesizes existing scholarship and outlines future research directions for the rapidly evolving cryptocurrency ecosystem.

Matteo Bonato, Rıza Demirer, Rangan Gupta, Abeeb Olaniran

This paper explores the role of mining activity, proxied by growth rates of electricity consumption and cost of mining, as a driver of pricing inefficiencies in Bitcoin. Utilizing alternative measures of crash risk proxied by the realized negative coefficient of skewness and realized down-to-up volatility, derived from 5-minute intraday Bitcoin data, causality tests, along with sign analysis, captured by the estimates of partial average derivatives, provide evidence that mining activity can, in general, predict an increase in the entire conditional distribution of crash risk, with the strongest impact associated over the normal (median) to moderately high (upper quantiles) levels of risk. Despite the emergence of cryptocurrencies in international transactions and as an investment vehicle, our results suggest that decentralized mining process can contribute to inefficiencies in the pricing of Bitcoin, putting further doubt into the role of these assets as a medium of exchange, alternative to conventional assets.

Jose E. Puente, C. de la Puente

Public companies and institutional investors that hold Bitcoin face increasing pressure to show solvency, manage risk, and satisfy regulatory expectations without exposing internal wallet structures or trading strategies. This paper introduces the Treasury Proof Ledger (TPL), a Bitcoin-anchored logging framework for multi-domain Bitcoin treasuries that treats on-chain and off-chain exposures as a conserved state machine with an explicit fee sink. A TPL instance records proof-of-reserves snapshots, proof-of-transit receipts for movements between domains, and policy metadata, and it supports restricted views based on stakeholder permissions. We define an idealised TPL model, represent Bitcoin treasuries as multi-domain exposure vectors, and give deployment-level security notions including exposure soundness, policy completeness, non-equivocation, and privacy-compatible policy views. We then outline how practical, restricted forms of these guarantees can be achieved by combining standard proof-of-reserves and proof-of-transit techniques with hash-based commitments anchored on Bitcoin. The results are existence-type statements: they show which guarantees are achievable once economic and governance assumptions are set, without claiming that any current system already provides them. A stylised corporate-treasury example illustrates how TPL could support responsible transparency policies and future cross-institution checks consistent with Bitcoin's fixed monetary supply.

Rafał Skowroński

Blockchain networks face a critical but understudied threat: wave attacks that exploit difficulty adjustment algorithms through strategic mining participation. Adversaries cyclically withdraw and re-enter mining to create oscillations that degrade network liveness and destabilize honest miners’ revenue. We present the first production-ready framework that maintains network responsiveness while enabling robust, post hoc threat detection. The framework employs a statistically rigorous pipeline featuring controller-aligned anomaly detection, transitive collusion grouping via union-find, and Benjamini–Hochberg False Discovery Rate control. We formally prove the economic viability of this architecture: when penalties on unvested rewards are enabled by governance, wave attacks become asymptotically unprofitable for rational adversaries. Evaluated on a 128-node distributed testbed simulating Bitcoin, Ethereum Classic, and Monacoin networks over 30 independent runs, our framework achieves 92.7% F1-score in detecting attacks, significantly outperforming baseline methods (74.7%). This work provides a complete, theoretically-grounded solution for securing proof-of-work blockchains against difficulty manipulation, forming the foundation for the adaptive AI-driven enhancements presented in our companion paper (Part II).

Simran Erica Mathias, Satyaban Sahoo

Abstract This study explores the dynamic volatility spillovers and interconnectedness between cryptocurrency and traditional futures markets. Using a multi-method approach that integrates wavelet coherence analysis, TVP-VAR connectedness, and DCC-GARCH modeling, the research identifies notable shifts in spillover patterns during crises, such as the COVID-19 pandemic, the FTX collapse, and the Russia-Ukraine conflict. The results reveal that the correlations between Bitcoin futures and traditional asset classes depend on the market conditions and intensify during crises. The connectedness analysis shows that Bitcoin futures play a dual role, acting as a transmitter of long-term shocks and a receiver of short-term shocks during periods of crisis. Equity futures emerged as the primary long-term transmitters of shocks, whereas other assets acted as shock receivers during the pandemic. Furthermore, the study evaluates hedge ratios and portfolio weights using the DCC-GARCH model. The portfolio analysis reveals that Bitcoin futures require a minimal allocation within diversified portfolios, suggesting their limited effectiveness as a hedge and safe-haven asset. These results aim to inform portfolio managers in developing efficient hedging strategies and assist regulators in monitoring financial market stability. This study fills gaps in the existing literature by understanding how decentralized financial instruments interact with financial markets and providing insights into risk management in modern markets.

Osama Mustafa

It is worth noting that the topic of cryptocurrencies is characterized by modernity, and the resulting vacuum exists for many of them, and this is entirely the result of the failure of the vast majority of countries and international organizations to analyze them, to distinguish the topic as virgin, especially since it has been digital, so it pushes modern and innovative has become at the forefront. Details list Controversy around the world, as cryptocurrencies represented a dangerous stage in the development of currencies that we witness today in different eras, especially in light of the noticeable spread of these currencies, whether in the present or in the future One of the most important problems resulting from dealing in cryptocurrencies has become the lack of legislative texts that address disputes arising from the trading of digital currency in most countries and international organizations. We also did not find legal legislation for cryptocurrencies in Iraqi legislation that regulates them, and the issue of Research is considered a virgin topic in private international law. Being a complex and thorny subject that includes technical rules, technical complexities and multiple inter-related relationships involving multiple legal systems, which requires an integrated legal system.

JOSIP HORVAT, ELVIS MUJAČEVIĆ

Purpose – The purpose of this research is to explore the opportunities and barriers related to the use of cryptocurrencies in tourism from the local community’s perspective. Cryptocurrencies are increasingly accepted worldwide, yet their use in tourism consumption remains limited. Evaluating the attitudes and readiness of residents in urban areas, particularly in Zagreb, is essential for assessing the sustainability of digital payment technologies in tourism. Methodology – The research was conducted in Zagreb and its surroundings, with a sample of 484 respondents. A structured questionnaire was used to assess knowledge, perceived security, intention to use, and perceived barriers and incentives regarding cryptocurrency usage in tourism. Data analysis involved descriptive statistics and Pearson’s Chi-square test to examine relationships between key variables and sociodemographic factors. Findings – The results indicate limited awareness about cryptocurrencies, with more than 75% of respondents being completely unfamiliar or only superficially familiar with the topic. A small percentage currently uses cryptocurrencies, but there is substantial conditional willingness for future usage, particularly if regulatory, educational, and security issues are addressed. Statistically significant gender differences were observed in perceived awareness and trust in Bitcoin systems, with men exhibiting higher levels of awareness and trust compared to women. Contribution – This study provides valuable insights into local community readiness for cryptocurrency usage in tourism, highlighting the significance of education, trust, and regulatory frameworks. The findings can serve as a foundation for policymakers, tourism stakeholders, and digital innovators to develop strategies for the effective integration of cryptocurrencies into tourism economies.

sarah Osama anis, Mohammed Mabrouk Morsey, Mostafa Aref

In the rapidly evolving landscape of cryptocurrency, gaining a deep understanding of public sentiment has become increasingly essential, especially given the significant impact of social media platforms on market perceptions and trends. This paper introduces a sophisticated sentiment classification model that utilizes a Bi-LSTM architecture to analyse over one million tweets related to Bitcoin. By integrating Explainable AI techniques, particularly LIME (Local Interpretable Model-agnostic Explanations) framework, our model not only achieves an impressive test accuracy of 98% but also offers valuable insights into its decision-making process, making the results more interpretable for users Our findings highlight robust performance metrics across precision, recall, and F1-scores, which collectively underscore the model's reliability and effectiveness in real-world applications. Furthermore, we delve into the opaque nature of the Bi-LSTM model through the application of LIME, which sheds light on how particular words and phrases have a strong impact on sentiment predictions. This research equips future investigations with conceptual frameworks and analytical tools that can be customized to study a broader range of cryptocurrencies. Through this work, we aim to foster a more nuanced comprehension of how public sentiment shapes market behaviour and decision-making in the digital currency space.

Melo, Lisana Hildegard

Resumo / Abstract : Este artigo pretende demonstrar como o Bitcoin se insere na construção de uma cultura de paz. Apresenta a evolução do conceito, de “não guerra” para “não violência”, e caracteriza a cultura de paz como uma dinâmica social de colaboração. Pontua que as transações não mediadas por terceiros possibilitam que os indivíduos escapem da influência econômica que acentua a assimetria de poder. Reconhece que a dinâmica que recompensa e incentiva a integridade da rede bitcoin privilegia a colaboração. Ao final, conclui que estamos diante de uma infraestrutura monetária que possibilita aquilo que queremos ver acontecer. This paper aims to demonstrate how Bitcoin fits into the construction of a culture of peace. It presents the evolution of the concept, from "non-war" to "non-violence," and characterizes the culture of peace as a social dynamic of collaboration. It points out that transactions not mediated by third parties allow individuals to escape the economic influence that accentuates power asymmetry. It recognizes that the dynamic that rewards and encourages the integrity of the Bitcoin network prioritizes collaboration. In conclusion, it states that we are facing a monetary infrastructure that enables what we want to see happennig.

Athapol Ruangkanjanases, Taqwa Hariguna

This study aims to examine the relationship between Bitcoin trading volume and key technical indicators using data-mining techniques to better understand how trading activity influences momentum and volatility in blockchain markets. The methodology involves analyzing a historical dataset of Bitcoin’s daily trading records from 2018 to 2023, which includes the Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Simple and Exponential Moving Averages (SMA, EMA), and the Average True Range (ATR). Pearson correlation analysis was applied to identify linear associations between trading volume and these technical indicators. The results show significant positive correlations between trading volume and momentum or trend measures such as the 7-day RSI (r = 0.45, p &lt; 0.05), SMA (r = 0.38, p &lt; 0.05), EMA (r = 0.41, p &lt; 0.05), and ATR (r = 0.48, p &lt; 0.05), indicating that higher participation accompanies stronger market momentum and greater price variability. Conversely, the weak and non-significant correlation with MACD (r = –0.12, p = 0.15) suggests that volume has limited influence on lagging trend-reversal signals. The novelty of this study lies in integrating volume-based behavior into technical indicator analysis, extending the traditional volume–price–volatility framework to cryptocurrency markets and providing practical insights for momentum-driven trading strategies and volatility-aware risk management.

Supriya P., Rubah Sheriff, Shreya Padaki, Suchi V. Yadav · 5 authors

In today&rsquo;s technologically advancing world, many fields from finance to healthcare and education are shifting toward a digital and decentralized format. A significant transformation is underway with the currency of the masses. Blockchain-based cryptocurrencies like Bitcoin and Ethereum allow users to generate fungible tokens anonymously through smart contracts. However, these features also facilitate illicit transactions and cybercrimes like fraud, phishing, and money laundering. The proposed work explores the identification of suspicious transactions on the Ethereum blockchain by leveraging advanced machine-learning techniques. An Extreme Gradient Boosting (XGBoost) classifier is optimized for spotting unauthorized or malicious transactions, exploring features like transaction patterns and value anomalies. Feature scaling and log transformations normalize skewed distributions, while rigorous model training and hyperparameter tuning enhance the system&#039;s precision, recall, and overall accuracy. Other aids, such as feature importance rankings, precision-recall curves, and diagnostic statistics, provide useful information on fraud patterns. Evaluation of the model shows that integrating cost-sensitive learning significantly reduces false positives, from 51 to 44, representing a 13.7% decrease, which enhances practical usability by minimizing false alerts and manual verification efforts. Although there was a slight increase in false negatives (from 14 to 15), the overall classification accuracy improved. The model demonstrated strong performance in managing class imbalance which is common in fraud detection contexts.