As vehicular ad hoc networks (VANETs) increase in size and complexity, ensuring secure, flexible, and privacy-preserving vehicle-to-infrastructure (V2I) authentication remains a major challenge. Existing protocols often focus solely on identity verification, overlooking the need for access control based on vehicle attributes. Furthermore, vehicles must obtain authentication credentials from various trusted entities, including automakers, regulators, and government agencies. However, the absence of a unified credential issuance mechanism introduces fragmentation and inconsistencies during the registration process. To address these issues, we propose a V2I authentication protocol, called PriV2I, that integrates distributed credential issuance, attribute-based access control, and strong anonymity guarantees. During vehicle registration, our approach uses Shamir’s Secret Sharing with a threshold t of n across multiple certification authorities (CAs) to consolidate credentials. A vehicle credential can only be issued by a predefined threshold number of CAs, enhancing security and flexibility. Within the authentication protocol, Pointcheval-Sanders (PS) signatures enable fine-grained access control based on vehicle attributes such as type and role. Meanwhile, noninteractive zero-knowledge proofs protect identity privacy by allowing vehicles to prove credential possession and policy compliance without revealing sensitive information. The proposed scheme also supports batch authentication at Roadside Units (RSUs) to efficiently handle high-density environments and includes a comprehensive revocation mechanism to trace and revoke malicious vehicles promptly and securely. In our implementation, the computation cost during the authentication phase is 75.58 ms. The communication overhead per authentication exchange is 992 bytes across two messages. Overall, the protocol provides a secure, scalable, and privacy-preserving solution tailored to modern VANET environments.
Piotr Fiszeder, Witold Orzeszko, Radosław Pietrzyk, Grzegorz Dudek
This study advances the understanding of Bitcoin volatility forecasting by analysing an extensive set of 62 explanatory variables, including cryptocurrency market behaviour, Google search trends, financial indices, and economic indicators. We employ Bayesian Model Averaging (BMA), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest (RF) methods to assess variable importance and forecast accuracy. Our research demonstrates that LASSO and RF models incorporating exogenous variables significantly improve both daily and weekly Bitcoin variance forecasts compared to models using only lagged Bitcoin volatilities. Key factors influencing Bitcoin volatility include lagged realised variances, trading volume, and Google search intensity. The study reveals that the impact of these variables on Bitcoin volatility is time-varying, reflecting its evolving relationship with broader economic indicators and market sentiment. Our findings contribute to the literature by providing a comprehensive analysis of Bitcoin volatility drivers, evaluating the effectiveness of variable transformations, and comparing the performance of advanced forecasting methods in handling the cryptocurrency's extreme volatility. These insights are valuable for researchers, investors, portfolio managers, and policymakers navigating the dynamic cryptocurrency market.
Financial crime detection faces unparalleled challenges as criminal networks exploit digital payment channels, cryptocurrency platforms, and cross-border transaction systems outside traditional monitoring frameworks. In this respect, AFCI introduces a novel framework for federated machine learning, regulatory reasoning engines, and real-time risk propagation analytics to build unified global privacy-preserving anti-crime intelligence ecosystems. The framework lets organizations train collaborative models with decentralized institutions, safely aggregating information from multiple parties without sharing sensitive transaction data by means of secure aggregation protocols and differential privacy mechanisms. Large language models coupled with knowledge graphs automate the processes of regulatory interpretation and rule generation, and graph neural networks enable the detection of coordinated criminal activities on a large scale in transaction networks through temporal message passing mechanisms. Reinforcement learning agents continuously optimize detection policies to balance the identification of genuine threats against the goal of minimizing false alarms. The framework bridged critical gaps in cross-border compliance coordination and empowered institutions to develop shared detection capabilities in support of data localization requirements and an array of diverse regulatory frameworks. Long-term security of privacy-preserving federated computation would be guaranteed with post-quantum cryptography. This convergence of advanced technologies allows next-generation financial crime prevention systems to remain effective against evolving criminal methodologies while preserving fundamental privacy rights.
Demand volatility, logistical interruptions, and linked worldwide networks define the remarkable complexity of modern supply chains. Classic centralized management solutions find difficulty in offering real-time solutions to changing operational problems. For designing distributed, intelligent, and self-organizing supply chain ecosystems, artificial intelligence agents combined with Model-Control-View (MCV) architectures provide transformational possibilities. These autonomous computational entities span three functional layers: view interfaces enable monitoring and interaction, control mechanisms govern decision-making and optimization, and model components represent digital twins of supply chain entities. Multi-agent coordination enables decentralized yet coherent operations through the negotiation and collaboration of agents representing suppliers, production, logistics, and retail, all of which adhere to standardized protocols. Applications include demand forecasting, intelligent logistics, stock optimization, supplier partnering, and flexible disruption response. While reducing reliance on centralized control systems, the framework enhances resilience, scalability, openness, and operational efficiency. Challenges in implementation include organizational adaptation needs, cybersecurity vulnerabilities, and data integration complexity. Future advances in autonomous and cooperative supply chain systems will include explainable artificial intelligence, quantum-enhanced optimization, edge computing powers, and blockchain-enabled trust mechanisms.
How may digital platforms be redesigned to better serve the interests of the artists whose creative work gives them value? An artist- and user-owned streaming platform is proposed that would decentralize control and redistribute revenue from corporations to creators. Using Web3 infrastructure, the model enables direct artist payment through blockchain-based transactions that scale based on user consumption, minimizing fees and ensuring transparency. The design also emphasizes community governance and localized music discovery to encourage the regrowth of music culture. By reducing reliance on profit-driven intermediaries, the system aims to create a sustainable environment where independent artists can thrive. Spotify exemplifies how a platform’s designed-in incentives can perpetuate exploitation. The social construction of technology framework suggests that Spotify’s ownership model, pro- rata payment system, and algorithmic design prioritize shareholder value over fairness. Spotify’s supposed mission to “unlock the potential of human creativity” is undermined by its own architecture, which locks artists into dependency. Together, these projects show that achieving fairness in a digital music economy requires not only reforming compensation models but rethinking the infrastructures that define creative labor itself.
Mutiullah Shaikh, Uffe Kock Wiil, Ali Ebrahimi, Yumna Memon
Blockchain technology has revolutionized digital systems by ensuring trust, transparency, decentralization, and security. However, in the democratic nature of blockchain networks, there is a huge underlying dependency on consensus mechanisms, but the challenges associated with these, such as energy costs, network attacks, preservation of privacy, centralization, and limited scalability, hinder miners and stakeholders from adopting appropriate consensus mechanisms. In this paper, we present a conceptual literature overview of most consensus mechanisms by highlighting potential areas of exploration and considerations before adopting blockchain technology for various applications. This exploration turned our focus toward analyzing three prominent underlying aspects of consensus mechanisms, i.e. energy consumption, security, and decentralization. A simulation-based comparative analysis of five prominent blockchain consensus mechanisms, such as Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPoS), Proof of Authority (PoA), and Proof of Capacity (PoC), is presented in various network load scenarios to further evaluate their performance metrics. The simulated metrics were cross-validated using empirical data from real blockchain networks (e.g., Ethereum, Bitcoin, VeChain, and Chia) collected between 2022 and 2025, ensuring alignment between theoretical performance models and observed on-chain behavior across diverse consensus mechanisms. Results overall indicate that PoW excels in decentralization and security while costing the highest energy, making it less scalable for high-throughput scenarios. PoS balances energy efficiency and moderate decentralization, while DPoS achieves scalability at the expense of decentralization. PoA and PoC are shown to be energy-efficient alternatives, but vary in their levels of centralization and security. Our findings constitute a comprehensive guide for researchers, miners, and practitioners aiming to optimize blockchain performance for diverse applications.
Public blockchains inherently offer low throughput and high latency, motivating off-chain scalability solutions such as Payment Channel Networks (PCNs). However, existing PCNs suffer from liquidity fragmentation-funds locked in one channel cannot be reused elsewhere-and channel depletion, both of which limit routing efficiency and reduce transaction success rates. Multi-party channel (MPC) constructions mitigate these issues, but they typically rely on leaders or coordinators, creating single points of failure and providing only limited flexibility for inter-channel payments. We introduce Hypergraph-based Multi-Party Payment Channels (COALESCE), a new off-chain construction that replaces bilateral channels with collectively funded hyperedges. These hyperedges enable fully concurrent, leaderless intra- and inter-hyperedge payments through verifiable, proposer-ordered DAG updates, offering significantly greater flexibility and concurrency than prior designs. Hence our, design eliminates routing dependencies, avoids directional liquidity lock-up, and does not require central monitoring services such as watchtowers. Our implementation on a 150-node intra-hyperedge achieves a transaction success rate of approximately 94% under heavy load (larger payment sizes), while full hyperedge evaluation over a 15,000-node network sustains success rates in the range of 85% to 95%, without HTLC expiry or routing failures, highlighting the robustness of COALESCE.
Decentralized lending protocols, exemplified by Aave V3, have transformed financial intermediation by enabling permissionless, multi-chain borrowing and lending without intermediaries. Despite managing over $10 billion in total value locked, empirical research remains severely constrained by the lack of standardized, cross-chain event-level datasets. This paper introduces the first comprehensive, event-driven data infrastructure for Aave V3 spanning six major EVM-compatible chains (Ethereum, Arbitrum, Optimism, Polygon, Avalanche, and Base) from respective deployment blocks through October 2025. We collect and fully decode eight core event types -- Supply, Borrow, Withdraw, Repay, LiquidationCall, FlashLoan, ReserveDataUpdated, and MintedToTreasury -- producing over 50 million structured records enriched with block metadata and USD valuations. Using an open-source Python pipeline with dynamic batch sizing and automatic sharding (each file less than or equal to 1 million rows), we ensure strict chronological ordering and full reproducibility. The resulting publicly available dataset enables granular analysis of capital flows, interest rate dynamics, liquidation cascades, and cross-chain user behavior, providing a foundational resource for future studies on decentralized lending markets and systemic risk.
Alan J. McNamara, Sara Shirowzhan, Samad M.E. Sepasgozar
Purpose This study identifies and validates opportunities for automation of problematic construction contract administrative tasks and processes. Through the evaluation of identified automation opportunities, system features are proposed and prioritised to form a development roadmap for future intelligent contract (iContract) creation and evolution. Design/methodology/approach This study applies a qualitative approach to draw on experienced construction practitioners with direct knowledge of contract administration practices. Thematic mapping and co-occurrence analysis of interview data identify “Contract Process Automation Opportunities” (CPAOs) which are then evaluated and prioritised to inform a development roadmap. Findings The study establishes ten evaluation criteria, specific to contract processes and identifies eight novel CPAOs. Ten iContract system features, along with the technological and environment requirements to facilitate development, are then synthesised into the novel iContract development roadmap. Research limitations/implications An “iContract system requirements identification model” is developed by adapting established process automation theoretical frameworks. This guided the structured selection of suitable automatable contractual processes, based on both theoretical and practical insights. The roadmap offers a practical guide for iContract developers for an initial artefact and future researchers aiming to overcome the evolutionary challenges highlighted. Practical implications The roadmap offers a practical guide for iContract developers for an initial artefact and future researchers aiming to overcome the evolutionary challenges highlighted. Originality/value This study contributes a unique and founding iContract system development roadmap, in an embryonic field, that has been borne and validated by industry practitioners. It identifies the initial functions to successfully develop an iContract artefact and highlights the evolution of the concept towards an autonomous solution.
Traditional e-learning and academic administration systems face persistent challenges, including credential fraud, inefficient verification processes, and a lack of transparency in financial aid distribution. This paper proposes a blockchain-integrated e-learning platform designed to address these issues by creating a secure, transparent, and automated ecosystem for academic records and financial aid. We present a hybrid architecture that leverages a high-performance permissioned blockchain, Purechain, to manage trust-critical functions while retaining conventional databases for dynamic content. The system is built around four modular smart contracts: IdentityRegistry, AcademicManager, EduToken, and ScholarshipFactory. The key innovation is the programmatic linkage between on-chain academic performance and automated scholarship disbursement, enabling a trustless, unbiased, and efficient financial aid model.
This article is devoted to the issue of cryptocurrency seizure, using Bitcoin as an example. First, the article analyzes the legal nature of virtual currencies, cryptocurrencies, and Bitcoin, taking into account their technical aspects and their disposability. Particular attention is paid to the methods of storing cryptocurrency, which have a direct impact on the legal regulations that can be applied in the area of enforcement. Next, the possibilities of enforcing bitcoin on the basis of the applicable regulations, including the provisions on the enforcement of claims (Articles 895 to 908(1) of the Code of Civil Procedure) and other property rights (Articles 909 to 912 of the Code of Civil Procedure). Keywords: virtual currency, cryptoasset, cryptocurrency, blockchain, bitcoin, seizure, judicial enforcement, judicial enforcement proceedings, property law, virtual assets, digital assets
The aim of this dissertation is to test the applicability of two strategies – Dollar Cost Average (DCA) and Lump-Sum (LS) – in the context of the crypto market. We tested these strategies on three assets, namely Bitcoin, Ethereum and Ripple. We developed a simulation using daily historical data recorded over a period of nine years. We then calculated performance ratios and created an AR-GARCH model to analyse their properties and predictive capacity more effectively. Our empirical results show that all assets are highly volatile and exhibit heavy tails and asymmetry. Additionally, they are moderately to highly correlated with each other. We also presented proof of higher Sharpe and Sortino ratios for DCA strategies, with Bitcoin performing better than the other two assets. The results also show that Bitcoin has low-to-moderate shock sensitivity and high persistence; Ethereum has low shock sensitivity and high persistence; and Ripple has both high shock sensitivity and persistence. Furthermore, we observed the impact of strategy choice on volatility. When compared to DCA, LS lowered shock sensitivity in Bitcoin and Ripple, enhancing persistence, while having an insignificant effect on Ethereum. Finally, we demonstrate that our model exhibits superior predictive capacity with regard to Ripple compared to Bitcoin and Ethereum, and that all three assets are inefficient. These findings contribute to previous literature by providing novel empirical data and attesting to the attributes of cryptocurrencies. Furthermore, this thesis improves financial awareness and provides investors with valuable information.
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(×tamp.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
The global insurance sector continues to be hampered by legacy systems characterized by operational inefficiencies, asymmetric information, and excessive administrative overhead. This paper presents ChainCare, a decentralized insurance platform designed to overcome these limitations by leveraging blockchain technology. Built on the Ethereum public blockchain, ChainCare employs smart contracts written in Solidity to automate insurance policy creation, claims processing, and settlements while ensuring transparency and auditability. The system integrates a React.js-based user interface with blockchain-backed transactions to deliver a secure and user-centric experience. By recording all operations on a distributed ledger, the platform minimizes fraudulent activities, reduces manual intervention, and accelerates claim resolution. Experimental validation of the prototype demonstrates that ChainCare significantly reduces administrative complexity and enhances trust through immutable, verifiable transactions. The proposed architecture establishes a practical framework for decentralized insurance, offering a scalable and transparent alternative to traditional systems.
The article proposes an algorithm for alerting the population in case of fires using feedback via SMS and mapping services. The mechanism integrates distributed ledger technology and artificial intelligence to improve the accuracy, coverage and adaptability of the system. The article also develops a model that takes into account the distribution of population density during alerting in a given zone, as well as the optimal radius of the alert zone. The model is implemented in the form of software “Model of the effective radius of alerting in case of fire”. Results. The results obtained can be used to adjust the existing model of public notification in case of large fires, both natural and man-made. This work is intended for those who make managerial decisions and manage the forces and means of extinguishing fires.
Vikash Singh, Little, Barrett, Phil Hayes, Fang, Max · 7 authors
Verifying the private liquidity state of Lightning Network (LN) channels is desirable for auditors, service providers, and network participants who need assurance of financial capacity. Current methods often lack robustness against a malicious or compromised node operator. This paper introduces a methodology for the verification of LN channel balances. The core contribution is a framework that combines Trusted Execution Environments (TEEs) with Zero-Knowledge Transport Layer Security (zkTLS) to provide strong, hardware-backed guarantees. In our proposed method, the node's balance-reporting software runs within a TEE, which generates a remote attestation quote proving the software's integrity. This attestation is then served via an Application Programming Interface (API), and zkTLS is used to prove the authenticity of its delivery. We also analyze an alternative variant where the TEE signs the report directly without zkTLS, discussing the trade-offs between transport-layer verification and direct enclave signing. We further refine this by distinguishing between "Hot Proofs" (verifiable claims via TEEs) and "Cold Proofs" (on-chain settlement), and discuss critical security considerations including hardware vulnerabilities, privacy leakage to third-party APIs, and the performance overhead of enclaved operations.
This study examines the relationship between market efficiency and digital financial innovation in the context of global financial transformation over the past decade, when fintech, cryptocurrency, and Decentralized Finance (DeFi) have significantly altered price formation and information dissemination mechanisms. The main issue raised is whether the Efficient Market Hypothesis (EMH) theory remains relevant in the face of digital market dynamics characterized by high volatility, speculative behavior, and regulatory uncertainty. The objective of this study is to assess the impact of digital innovation on information efficiency, price transparency, and the stability of modern financial markets. The study used the Systematic Literature Review (SLR) method, examining 15 scientific articles published between 2015 and 2025 from various academic databases. The findings indicate that digital technology increases access and speed of information distribution, but does not always result in consistently efficient markets. Crypto and DeFi markets have been shown to exhibit fluctuating efficiency due to price anomalies, information asymmetry, and weak regulation. Overall, the literature synthesis confirms that market efficiency in the digital era is dynamic and influenced by the interaction between technology, investor behavior, and governance quality. This study concludes that the EMH remains relevant as a basic framework, but needs reinterpretation to suit the complex and rapidly changing characteristics of digital markets.
Love Allen Chijioke Ahakonye, Hamza Ibrahim, Jae-Min Lee, Dong‐Seong Kim
Smart contract environments are increasingly targeted by stealthy, adaptive attacks that evade conventional rule-based or static anomaly detection systems. Inspired by the anglerfish’s bioluminescent filament, which perceives and lures activity in dark, dynamic environments, this research introduces a Bioluminescent Filament-Inspired Artificial Intelligence Perception framework for smart contract intrusion detection. The proposed model emulates biological sensory adaptation through multi-modal attention layers that dynamically illuminate anomalous behaviors in contract execution flows. By integrating self-supervised temporal perception with context-driven feedback, the framework continuously refines its detection sensitivity while maintaining low computational overhead. We evaluate the framework using fuzz-tested smart contract vulnerability datasets that simulate diverse malicious execution behaviors observed in Ethereum environments, demonstrating over 98% detection accuracy with a 40% reduction in latency compared to traditional deep learning-based IDS models. This biologically inspired perception paradigm offers a scalable, energy-efficient solution for securing blockchain-based decentralized systems against evolving threat vectors.
In recent years, deep learning (DL) has shown remarkable performance in smart contract vulnerability detection, with graph neural networks (GNNs) serving as a key technique for learning structured code representations. However, existing graph-based approaches suffer from semantic fragmentation, noisy node interference, and weak semantic alignment, which limit detection robustness and deployment efficiency. To address these challenges, we propose UCGVulDetector, a unified and efficient framework designed for smart contract security in blockchain-based communication systems. It consists of three modules: (1) Structural Simplification (UDP): a hierarchical pruning strategy that refines abstract syntax trees by removing redundant nodes while preserving key semantics; (2) Graph Information Enhancement (UGSF): constructing heterogeneous graphs from Solidity ASTs and integrating control-flow, data-flow, and state-slot-chain (SSC) relations to capture multi-dimensional semantics; and (3) Graph Encoding and Alignment (PGE+TSCC): employing a pairwise graph encoder combined with temperature-scaled contrastive learning to align vulnerability semantics in a shared latent space. These modules collaboratively unify structural and semantic information to enhance feature representation. Experiments on the SolidiFI and MESSI datasets demonstrate that UCGVulDetector achieves F1-score improvements of 10.53%$\mathbf{1 1. 5 0 \%}$%ver state-of-the-art methods, delivering more accurate and robust vulnerability detection performance.
A growth in the popularity of interconnected banking systems that make use of the Internet of Things (IoT) has occurred as a result of the rise in the acceptance of digital financial services, which has become increasingly ubiquitous. The Internet of Things devices, despite the fact that they make banking operations more efficient and improve the experience for customers, also make them more vulnerable to hackers on the other hand. It is the case that this is the situation, despite the fact that these devices are advantageous to customers. The goal of this study is to investigate the Internet of Things (IoT) technology in order to determine whether or not it has the capability of enhancing the cybersecurity of financial institutions by means of the implementation of real-time threat detection and protection strategies that are based on blockchain technology: this is the purpose of this research. By deploying sensors that are connected to the Internet of Things in conjunction with analytics, it is feasible to perform continuous monitoring of the activity that occurs on the network, the patterns of transactions, and the interactions that occur between devices. It is projected that as a result of this research, a decentralized security model that makes use of technologies such as the Internet of Things (IoT) and blockchain will be built. This is something that is anticipated to happen. The objective of this study is to ensure that all of these things are achievable in order to guarantee that the flow of data is as transparent as possible, that it is not subject to tampering, and that transaction records cannot be altered. This enables the solution to be implemented. This is accomplished through the use of the IoT. The distributed ledger is the component of blockchain technology that is responsible for guaranteeing that audit trails and transaction data are protected from unauthorized changes or fraudulent activity. This responsibility falls under the purview of the distributed ledger. A superior predictive analysis is produced as a result of the incorporation of algorithms based on artificial intelligence into the ecosystem of the Internet of Things.
<p>The rapid evolution of decentralized finance (DeFi) has brought revolutionary innovations to global financial systems; however, it has also revealed some major security vulnerabilities, especially of smart contracts. Traditional auditing methods and static analysis tools are prone to fail in identifying sophisticated threats, including reentrancy attacks, front-running, oracle manipulation, and honeypots. This review discusses the growing role of machine learning (ML) in enhancing the security of DeFi systems. It provides a comprehensive overview of modern ML-based methods related to the detection of smart contract vulnerabilities, transaction-level fraud detection, and oracle trust assessment. The paper also provides publicly available datasets, necessary toolkits, and architectural designs used for developing and testing these models. Additionally, it provides future directions like federated learning, explainable AI, real-time mempool inspection, and cross-chain intelligence sharing. While it is full of promise, the application of ML in DeFi security is plagued by issues like data scarcity, interoperability, and explainability. This paper concludes by highlighting the need for standardised benchmarks, shared data initiatives, and the integration of ML into development pipelines to deliver secure, scalable, and reliable DeFi ecosystems.</p>
This interdependent network of vehicles has been made possible by the widespread adoption of self-driving cars operating as interconnected swarm networks based on continuous data exchange. Leading a Data-Driven Paradigm Shift However, these networks are not without their challenges, as they are prone to security threats, data privacy vulnerabilities, and inefficient consensus mechanisms to facilitate decentralized decision-making. This study proposes a novel blockchain architecture using AI that could offer enhanced security by utilizing a hybridized consensus protocol for autonomous vehicles. Accordingly, machine learning algorithm-based optimizations in blockchain consensus algorithms, such as PoW, PoS, and DPoS consensus methods, facilitate real-time adaptive adjustments, lower mining costs, and accelerated transaction validation, all within the framework of decentralized trustworthiness. In self-driving car ecosystems, AI-augmented security processes like anomaly detection, deep learning-based intrusion prevention, and federated learning, enhance threat detection while lowering the cybersecurity risks. Moreover, privacy-enhancing cryptographic methods, such as homomorphic encryption, zero-knowledge proofs (ZKPs), and differential privacy, are incorporated to safeguard sensitive vehicle information against unauthorized access while allowing compliance with data privacy laws. Experimental evaluations confirm that the proposed AI-empowered frameworks lead to improved system resilience, optimized resource allocation and improved transaction throughput and latency in contrast to traditional blockchain implementations. Overall, this study demonstrates that using AI-enabled blockchain models can provide a fundamental method for protecting and improving autonomous vehicle networks.