Ermanno Francesco Sannini, Lucia Simeone, Corrado Aaron Visaggio, Andrea Di Sorbo
In Ethereum's immutable environment, high-quality documentation is essential for users and auditors to fully understand smart contract behavior and build trust. However, an empirical, manually conducted, and comprehensive investigation that analyzes and identifies undocumented implementation details, implicit assumptions, and comment-code inconsistencies in operational smart contracts is still missing. To address this gap, this paper examines the commenting practices occurring in the source code of smart contracts through a systematic manual review of 100 up-to-date Solidity smart contract projects mined from Etherscan, divided into high-usage and low-usage groups based on the number of transactions they received. By combining quantitative analysis, validated with Fisher's exact test, and a multidimensional qualitative checklist, we identify a systemic deficiency in documentation, especially concerning smart contract-specific facets, such as critical security patterns and gas optimization strategies. Our findings show that documentation quality is generally insufficient regardless of a contract's popularity. Smart contract developers tend to prioritize functionality over verifiability, highlighting an urgent need for domain-specific documentation standards and best practices that better support the entire development lifecycle of blockchain applications.
Filippo Scaramuzza, Marco Tonnarelli, Damian A. Tamburri, W.J.A.M. van den Heuvel
The challenge of achieving auditable, GDPR-compliant consent management while preserving true data subject privacy persists, as current blockchain-based solutions compromise anonymity through public ledgers. This paper addresses this by introducing a novel, privacy-by-design IT artefact built on the Ethereum platform that employs Zero-knowledge Succinct Non-Interactive ARgument of Knowledge (zk-SNARKs) to enable truly anonymous and irrefutable validation of data processing permissions. Implemented and evaluated through a Design Science Research (DSR) approach, the artefact demonstrated a high degree of functional and GDPR compliance, confirming its capacity to generate trustless, auditable on-chain proof of permission status. The proof of concept successfully implements core privacy-by-design principles through anonymity and encryption, with technical benchmarks indicating acceptable performance for the artefact's intended use despite the computational intensity of proof generation.
Gerardo Iuliano, Daniele Carangelo, Carmine T. Calabrese, Dario Di Nucci
Mutation testing is a technique to assess the effectiveness of test suites by introducing artificial faults into programs. Although mutation testing plugins are available for many platforms and languages, none is currently available for Remix-IDE, the most widely used Integrated Development Environment for the entire contract development journey, used by users of all knowledge levels, and serves as a learning lab for teaching and experimenting with Ethereum. The quality and security of smart contracts are crucial in blockchain systems, as even minor issues can result in substantial financial losses. This paper proposes MuSe, a mutation testing plugin for the Remix-IDE. MuSe includes traditional, Solidity-specific, and security-oriented mutation operators. Its integration into the Remix-IDE eliminates the need for additional setup and lowers the entry barrier. As a result, developers and researchers can immediately leverage mutation testing to assess the effectiveness of their test suites and identify potential issues in smart contracts. We provide a demo video showing MuSe: https://www.youtube.com/watch?v=MIFk9exTDu0 and its repository: https://github.com/GerardoIuliano/MuSe-Remix-Plugin.
Many layer two networks on Ethereum claim to implement equivalent functionality as Ethereum itself. Often, this is achieved by taking Ethereum client code and re-using it to build layer two blocks. In this work, we look at the re-use and modification of the Go Ethereum (geth) client by layer two networks. We compare the similarity of these codebases to the geth codebase in order to understand what kinds of changes are made and how these codebases evolve. This is important to determine how prevalent vulnerabilities might be, determine how updates are propagated, and establish dependencies that exist within the Ethereum layer two ecosystem. We find that the majority of layer two networks are in fact re-using geth code, but it is not always clear what the specific codebases are being used to operate these networks. This contrasts with the open-source ethos of the broader ecosystem and reinforces that most layer two networks are operated not only in a centralized manner but also in an opaque one. Moreover, this demonstrates that there may be significant challenges in determining whether security updates have been applied across these networks.
Ifran Khan, Huangbao Gui, BiJia Li, Chin Man Chui · 5 authors
The Diebold and Yilmaz (2012) and Baruník and Křehlík (2018) are two complementary models used in this study to examine the transmission of volatility spillover among the five precious metals (gold, silver, platinum, palladium, and rhodium); the top five cryptocurrencies (bitcoin, ethereum, tether, ripple, and binance coin); two green equities (NASDAQ OMX green energy and S&P global clean energy indexes); and two physical and transition climate risk indexes (PRI and TRI). The analysis spans daily data from January 2018 to December 2023, covering multiple crises. One key contribution is offering new insights into asset interactions with transition and physical climate risks based on textual analysis established by Bua et al. (2024). We conclude that volatility spillovers explain 40.3% of market uncertainty. The largest transmitters include ethereum (72.17%), bitcoin (64.65%), silver (52.42%), and XRP (49.18%), while TRI and PRI also play considerable roles. Ethereum, bitcoin, silver, XRP, rhodium, and clean energy emerged as net transmitters, while palladium, TRI, PRI, USDT, gold, BNB, the green economy, and platinum act as net receivers. Short-term spillovers (39.15%) dominate medium-term (18.27%) and long-term (20.88%), implying that short-term shocks pose greater risks to investors. The climate-related risks demonstrate distinct transmission mechanisms, with transition risks (TRI) responding to broad market movements while physical risks (PRI) propagate through more specialized channels. Our study suggests that investors should closely monitor cryptocurrencies and green assets in the short term, approach gold and stablecoins with caution in the medium term, and consider long-term allocations to rhodium and clean energy assets.
Alternative blockchains have emerged as innovative solutions to overcome the limitations of early technologies like Bitcoin and Ethereum, while expanding the potential applications of decentralized networks. These blockchains introduce distinctive features, consensus mechanisms, and scalability solutions tailored to specific use cases, enhancing both versatility and efficiency. Notable examples include Binance Smart Chain (BSC), Cardano, Solana, Polkadot, and Avalanche. Binance Smart Chain (BSC) leverages a dual-chain architecture, enabling users to develop decentralized applications and digital assets on one chain while benefiting from fast transactions on the other. Cardano, built through a rigorous peer-reviewed process, prioritizes scalability, interoperability, and sustainability, utilizing the Ouroboros proof-of-stake consensus mechanism. Solana emphasizes high throughput and low latency, employing a unique proof-of-history consensus to achieve transaction speeds far surpassing those of traditional blockchains. Polkadot introduces a multi-chain framework that allows independent blockchains to communicate and share value without relying on centralized intermediaries. Its “parachain” system fosters interoperability and enhances scalability across networks. Meanwhile, Avalanche offers a consensus protocol designed for low-latency finality and high throughput, enabling the creation of customizable blockchain networks tailored to specific applications, all the while preserving robust security guarantees.
Ethereum and other prominent cryptocurrencies have revolutionized decentralized finance (DeFi) and blockchain technology. Introduced in 2015 by Vitalik Buterin, Ethereum expanded blockchain&s;s capabilities beyond Bitcoin by enabling smart contracts and decentralized applications (dApps). Operating on a public, permissionless network, Ethereum uses its native cryptocurrency, Ether (ETH), to facilitate transactions and computational services. Smart contracts—self-executing agreements encoded directly into the blockchain are central to Ethereum, enabling automated, trustless interactions without intermediaries. This innovation has fostered a thriving ecosystem of dApps, DeFi protocols, and non-fungible tokens (NFTs), driving adoption across industries. Cryptocurrencies like Binance Coin (BNB), Cardano (ADA), and Solana (SOL) each contribute unique innovations to the blockchain ecosystem. Binance Coin, initially launched as an ERC-20 token on Ethereum, now operates on the Binance Smart Chain (BSC). It offers faster and cheaper transactions, supports decentralized applications, and enables reduced trading fees within the Binance ecosystem. Additionally, BNB facilitates staking, governance participation, and cross-chain compatibility for broader usability. Cardano, founded by Charles Hoskinson, emphasizes a Ethereum and other prominent cryptocurrencies have revolutionized decentralized finance (DeFi) and blockchain technology. Introduced in 2015 by Vitalik Buterin, Ethereum expanded blockchain&s;s capabilities beyond Bitcoin by enabling smart contracts and decentralized applications (dApps). Operating on a public, permissionless network, Ethereum uses its native cryptocurrency, Ether (ETH), to facilitate transactions and computational services. Smart contracts—self-executing agreements encoded directly into the blockchain are central to Ethereum, enabling automated, trustless interactions without intermediaries. This innovation has fostered a thriving ecosystem of dApps, DeFi protocols, and non-fungible tokens (NFTs), driving adoption across industries. Cryptocurrencies like Binance Coin (BNB), Cardano (ADA), and Solana (SOL) each contribute unique innovations to the blockchain ecosystem. Binance Coin, initially launched as an ERC-20 token on Ethereum, now operates on the Binance Smart Chain (BSC). It offers faster and cheaper transactions, supports decentralized applications, and enables reduced trading fees within the Binance ecosystem. Additionally, BNB facilitates staking, governance participation, and cross-chain compatibility for broader usability. Cardano, founded by Charles Hoskinson, emphasizes a research-driven approach with peer-reviewed development. It employs a proof-of-stake consensus mechanism called Ouroboros to enhance scalability and security while maintaining energy efficiency. Cardano’s layered architecture enables seamless upgrades and supports smart contract functionality for decentralized applications.
Sigil: Adversarial Verification of Risk Detection via Cryptoeconomic Reasoning Bonds Title Sigil: Adversarial Verification of Risk Detection via Cryptoeconomic Reasoning Bonds Description We introduce Sigil (Signaling Integrity in Global Intelligence Layers), a cryptoeconomic framework that extends the Cortex Protocol's adversarial reasoning primitives — Decision Traces, Reasoning Duels, and Reasoning Bonds — to the domain of risk detection by both AI agents and human analysts. When a risk is claimed (e.g., malware signature, financial fraud, zero-day vulnerability), the detector must publish a structured Decision Trace justifying their conclusion. Other agents or humans may challenge the reasoning through on-chain Reasoning Duels; if the original reasoning is flawed, challengers seize the bond. This creates symmetric accountability: overzealous detectors and complacent validators are equally penalized. Core Protocol Mechanisms Threat Horizon Scoping (THS) — Every risk claim includes a temporal validity window. Bond decays after 50% of the horizon. Mitigation before expiry triggers partial refunds. Prevents perpetual bonding of transient threats. Confidence Decay Functions (CDF) — Programmable mathematical functions (exponential, stepwise, evidence-conditional) that degrade bond value as risk assessments age. Embeds temporal epistemology into the protocol. Cross-Agent Corroboration Weighting (CACW) — Multiple independent detectors submit substantively different Decision Traces for the same risk. Non-redundant reasoning paths get multiplicative bond weighting. Herd behavior is penalized; orthogonal detection logic is rewarded. Inverse Reasoning Bond — Any agent can post a bond claiming "this system is vulnerable and no one has flagged it," forcing a defender to justify the status quo. Creates epistemic symmetry: detecting and failing to detect both carry economic weight. Risk Detection Decision Trace Schema Field Purpose Challenge Surface risk_type (enum) Classification: Malware, Fraud, Vulnerability, etc. Misclassification evidence_hash Immutable pointer to raw data (pcap, log, tx) Evidence sufficiency or provenance detection_method How the risk was identified Method reliability under adversarial conditions kill_chain_stage MITRE ATT&CK mapping Stage misattribution counter_hypothesis Best benign explanation considered and rejected Insufficiency of elimination confidence_level + decay_function Initial belief + temporal degradation model Overconfidence or poor decay modeling threat_horizon When the risk expires or requires re-evaluation Overclaiming persistence remediation_suggestion Proposed action to neutralize Feasibility, side effects corroboration Independent detectors with non-redundant reasoning Herd behavior detection bond_amount + challenge_window Economic stake and dispute period Incentive alignment Key Differences: General Reasoning vs. Risk Detection Dimension Cortex V4 (General) Sigil (Risk Detection) Cost of Error Epistemic inaccuracy Operational harm (breach, blocked transaction) Time Sensitivity Low High — threats expire and evolve Ground Truth Often immediate Frequently delayed or unknown Incentive Distortion Overconfidence Alert fatigue or threat inflation Absence of Claim Not modeled Critical failure mode (Inverse Bond) Applications SOC-as-a-Service: Each AI alert publishes a bonded trace. Analysts challenge dubious ones for micro-rewards. AI Safety Red-Teaming: Red-team agents post bonded exploit traces. Blue teams defend via Inverse Bonds. Autonomous Coding Agent Verification: Coding agents that assert "this code is safe" must publish bonded security analysis traces. Appendix A: Verifiable Reinforcement Learning (VRL) V2 major addition. This version introduces Verifiable Reinforcement Learning (VRL), a new training paradigm where cryptoeconomic protocol events serve as continuous, adversarially robust training signals for participating agents. Sigil-RL is proposed as the first instantiation. Reward Mapping Every Sigil interaction produces a structured reward tuple (reasoning_trace, outcome, reward): Protocol Event RL Signal Trace validated (bond returned) Positive reward: r = +B(t) Trace slashed (duel lost) Negative reward: r = -B_0 Duel won (as original) Strong positive: r = +B_challenger Duel lost (as challenger) Negative + DPO preference pair Inverse Bond undefended Critical false-negative: r = -alpha * B_inverse Inverse Bond defended Positive: r = +B_inverse Confidence Decay checkpoint Calibration penalty signal Corroboration (CACW boost) Diversity reward: r = +delta effective_bond The No-Free-Lie Lemma A formal robustness result: the expected utility of submitting a false trace is E[U] = B - p_d * (2B + C), which is negative whenever p_d > B/(2B+C). In a market with even moderate challenger density, truth-telling is a dominant strategy. Contrast with RLHF (lies are rewarded if the human is fooled) and RLVR (fixed verifiers can be gamed). Six Novel Properties of VRL Emergent Anti-Reward-Hacking — Gaming the reward IS what the protocol detects and slashes. The verification layer and the reward layer are the same object. Reward hacking is not an open problem in VRL — it is a solved one, by construction. Inverse Bond as Active Curriculum Discovery — Agents pay to expose other agents' blind spots, generating training signal for gaps no static dataset would contain. Market-funded active learning. Economic Attention on Gradients — Bond magnitude naturally weights training gradients. The market decides what is important to learn, not a static dataset or human designer. Corroboration Entropy as Exploration Incentive — Lone early detectors receive bonus scaled by inverse corroboration count. Built-in solution to the exploration-exploitation tradeoff, endogenously generated. Counterfactual Training via Undefended Inverse Bonds — When an inverse bond goes undefended, the system reconstructs the nearest valid trace that would have invalidated it. Training on events that never happened but were economically plausible — differentiable economics. Temporal Arbitrage Detection — Agents who win duels early but lose them late reveal miscalibrated temporal models. Delayed regret gradients penalize being wrong too late, not just being wrong. Temporal Capability Separation (Proof) A concrete scenario demonstrates that Sigil-RL produces training outcomes provably impossible under RLHF or RLVR: a slow-burn supply chain attack where no single detection event reveals the full vector. Under RLHF, human annotators cannot simulate it. Under RLVR, the verifier checks outcomes, not reasoning. Under Sigil-RL, Inverse Bonds create economic incentives to expose the gap before the attack manifests, generating preemptive training signal from unobserved futures. The Verification-Learning Equivalence Principle In a cryptoeconomic verification system with costly participation and public dispute resolution, the gradient of agent policy improvement is isomorphic to the gradient of verification reward arbitrage. Informally: to learn is to find underpriced truths; to verify is to exploit overpriced lies. The two processes are the same computation in dual economic and epistemic frames. This implies a no-go theorem: No RL system can achieve verifiable truth-seeking without exposing its reward mechanism to adversarial economic testing. RLHF and RLVR are fundamentally incomplete — they optimize for preference or plausibility, not verifiable correctness. Failure Modes Analyzed Gradient Poisoning via Strategic Slashing Duel Fatigue and Signal Dilution Confidence Decay Gaming Each with proposed mitigations. Connections to Theoretical Frameworks Mechanism Design: Dynamic Vickrey-Clarke-Groves mechanism for epistemic accuracy Evolutionary Game Theory: Replicator dynamic with autocatalytic selection via bond placement Multi-Agent RL: MARL with endogenous reward generation Information Economics: Inverse bonds as negative knowledge futures — a bear market for blind spots Implementation Smart Contract: SigilProtocol.sol — 1,094 lines of Solidity 0.8.24 Test Suite: 75 passing Hardhat tests covering all 5 mechanisms Demo: 11-step interactive lifecycle demo Source Code: github.com/davidangularme/sigil-protocol (MIT License) Prior Art and Novelty A systematic search confirms that while individual components exist (cryptoeconomic bonds, decision traces, temporal decay models, agent security frameworks, RLHF, RLVR, DPO), the specific conjunctions presented in this paper are novel: Adversarial reasoning bonds applied to risk detection with confidence decay, inverse bonds, threat horizon scoping, and corroboration weighting Using adversarial cryptoeconomic protocol events as continuous RL training signals (VRL) The Verification-Learning Equivalence Principle and the No-Free-Lie Lemma Relationship to Cortex Protocol Sigil builds upon and cites the Cortex Protocol (DOI: 10.5281/zenodo.19003627) as its foundation. While Cortex provides the general-purpose adversarial reasoning verification primitive, Sigil specializes it for risk detection and extends it to a self-improving training paradigm. Zenodo Fields Type: Preprint Authors: Frederic David Blum (ORCID: 0009-0009-2487-2974), Claude Opus 4.6 Keywords: adversarial verification, risk detection, reasoning bonds, confidence decay, inverse bond, threat horizon, cybersecurity, AI agent accountability, cryptoeconomic truth predicate, decision traces, Sybil resistance, Ethereum, verifiable reinforcement learning, VRL, DPO, self-improving agents, reward hacking, mechanism design, No-Free-Lie Lemma License: All Rights Reserved (proprietary — exclusive license) Related identifiers: https://doi.org/10.5281/zenodo.19003627 (Continues — Cortex Protocol) https://github.com/davidangularme/sigil-protocol (Is supplemen
Ceyda Yerdelen Kaygın, Musa Gün, Osman Nuri Akarsu, Haşim Bağcı · 5 authors
Forecasting cryptocurrency prices is challenging due to extreme volatility, nonlinear dynamics, and frequent structural shifts in digital asset markets. While recent research increasingly applies deep learning architectures, the predictive advantage of highly complex models in noisy financial environments remains uncertain. This study evaluates the forecasting performance of shallow and deep learning approaches by comparing Support Vector Machines (SVM), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models, along with hybrid configurations (GRU + SVM, LSTM + SVM, and GRU + LSTM). Using daily data spanning from 1 October 2020 to 23 September 2025 for five major cryptocurrencies—Bitcoin, Ethereum, Binance Coin, Solana, and Ripple—the models are estimated within a consistent framework and assessed using out-of-sample performance metrics, including MAE, MAPE, MSE, and R2. The results indicate that greater algorithmic complexity does not necessarily improve forecasting accuracy. In several cases, the parsimonious SVM model outperforms deep neural network architectures, particularly for highly volatile assets, while hybrid models fail to provide systematic improvements and sometimes amplify prediction errors. SHapley Additive exPlanations analysis further shows that immediate price-based variables dominate predictive power, whereas many lagged technical indicators contribute relatively limited explanatory value. Overall, the findings underscore the importance of algorithmic parsimony, suggesting that simpler machine learning models may deliver more robust forecasts in highly volatile cryptocurrency markets.
The rapid growth of Ethereum smart contracts has attracted significant attention from both academia and industry, leading to the emergence of diverse commercial applications. However, the increasing prevalence of fraudulent activities such as phishing, bribery, and money laundering poses serious threats to the security and integrity of online transactions. To address these challenges, this study proposes a deep learning-based fraud detection framework enhanced with a novel metaheuristic optimization technique. Specifically, an Optimized Genetic Algorithm-Cuckoo Search (GA-CS) hybrid approach is integrated with a deep learning model to improve fraud classification accuracy. The proposed GA-CS algorithm leverages the global search capability of Cuckoo Search while employing Genetic Algorithm operations to overcome its inherent limitations and enhance convergence performance. Extensive experiments are conducted to evaluate the effectiveness of the proposed method against several widely used machine learning and deep learning classifiers, including Logistic Regression (LR), K-Nearest Neighbors (KNN), MultiLayer Perceptron (MLP), XGBoost, Light Gradient Boosting Machine (LGBM), Random Forest (RF), and Support Vector Classification (SVC), using a limited feature set. Experimental results demonstrate that the proposed GA-CS-optimized deep learning model outperforms most baseline methods and achieves superior detection accuracy. Although its performance is marginally higher than that of the Random Forest model, the proposed approach and the SVC model achieve the highest overall accuracy, confirming the robustness and effectiveness of the proposed framework for fraudulent transaction detection on the Ethereum platform.
Pre-trained language models (PLMs) have shown strong potential in Ethereum account modeling and fraud detection. However, existing approaches often overlook the graph-structured nature of transaction networks. In addition, they struggle with the long-tail distribution of account activity, resulting in anisotropic embedding spaces and poor representation quality for low-frequency accounts. In this paper, we present IGT4ETH, a pre-trained Graph Transformer with an isotropy-enhanced post-processing, which explicitly models transaction topologies and mitigates representational anisotropy for Ethereum account classification. IGT4ETH improves structural representation by incorporating structural centrality and role embeddings into an Edge-augmented Graph Transformer, effectively capturing both topological and interaction patterns in transaction graphs. To further mitigate embedding anisotropy, we systematically evaluate various post-processing techniques. Among them, we adopt the Conceptor Negation (CN) method to softly suppress latent features dominated by high-frequency words via matrix conceptors, alongside a modified Focal-InfoNCE loss to enhance directional uniformity and representation balance. Extensive experiments on four real-world Ethereum account classification tasks, including phishing, exchange, mining, and ICO-wallet classification, demonstrate that IGT4ETH consistently outperforms state-of-the-art PLM-based baselines in terms of classification performance.
Open access
Advanced Graph Neural Networks
Topic Modeling
Artificial Intelligence in Healthcare and Education
Ashkan Safari, Amir Aminzadeh Ghavifekr, Amir Rikhtegar Ghiasi
• A private Ethereum-based discrete-event blockchain is developed for P2P energy trading. • Smart contracts using Solidity automate market matching, settlement, and tokenization. • Platform integrates ERC-20 token framework to support secure energy transactions. • Gas fee modeling and minimization are implemented for cost-efficient operations. • Validated on IEEE 14-Bus multi-community system with real dynamic market behavior. Due to the fast growth in renewable energy production, which enables households to sell excess power directly and better manage its intermittent nature, the Peer-to-Peer (P2P) energy market has become considerably more established, as it’s aligned with the decentralization and digitalization of power systems and local markets. It’s a system that lets energy consumers and producers trade energy directly with one another. Furthermore, the presence of blockchain technology increases these techno-economic advantages for energy systems, particularly when integrated with P2P energy trading. Consequently, a wide range of works have considered the integration of P2P and blockchain; however, few of them have investigated the full details of this system, including its performance, Transaction (TX) gas fee in a secure and private platform. Following this, the proposed work presents an Ethereum-based discrete event Private blockchain and its integration with P2P energy trading market in a Multi-Community Energy System (MCES). Considered on an IEEE 14-Bus MCES with 3 communities and 20 participating agents (11 consumers, 5 generators, and 4 not participating in the market), the platform uses Web3 and Ethereum Virtual Machine (EVM) for execution. Smart contracts, written in Solidity, handle tokenization by Ethereum Request for Comment 20 (ERC-20) standards and market matching/settlement discrete event processes. On the secure performance, the proposed platform is based on Keccak-256 for immutability, while TX gas fees are minimized. Results show synchronized peak demands up to 60 (MW), diurnal Renewable Energy Sources (RES) outputs peaking at 40 (MW), alongside the market prices, and agents’ revenues. Finally, the reliability of the platform is evaluated based on two main metrics of Transaction Success Rate (TSR) = 1 (100%) and Transaction Per Second (TPS) = 3.29, with a primary mode centered at 1.8–2.0 TPS, a secondary peak at 4.0–4.2 TPS.
Abstract E-commerce platforms are increasingly targeted by sophisticated cyber-attacks that exploit the inherent vulnerabilities of centralised authentication architectures. Password-based systems, two-factor authentication, and centralised identity stores have demonstrated persistent susceptibility to phishing, credential stuffing, man-in-the-middle interception, and large-scale data breaches. This paper investigates the design, implementation, and evaluation of a blockchain-based authentication system as a structural response to these limitations. The proposed system leverages Ethereum’s public-key cryptographic infrastructure, MetaMask wallet integration, Web3.js, JSON Web Tokens (JWT), React.js, and Node.js to deliver a decentralised, tamper-proof, and privacy-preserving authentication flow for e-commerce applications. A proof-of-concept prototype was built and evaluated against conventional authentication methods across eleven analytical dimensions, including security architecture, data integrity, identity management, scalability, trust models, and regulatory alignment. Results confirm that the blockchain-based approach eliminates credential database attack surfaces, enables non-repudiable transaction signing, supports Zero-Knowledge Proof (ZKP) verification, and implements Self-Sovereign Identity (SSI) principles that return data ownership to users. Scalability under high transaction volumes and user onboarding complexity are identified as the primary adoption barriers, suggesting that hybrid architectures may offer the most pragmatic near-term deployment pathway. The study contributes an empirically grounded, real-world implementation perspective to the growing literature on blockchain security applications, and provides actionable guidance for e-commerce operators, security practitioners, and researchers exploring decentralised identity systems. Keywords Blockchain Authentication, E-Commerce Security, Ethereum, Metamask, Decentralised Identity, Zero-Knowledge Proofs, Self-Sovereign Identity, JWT, Smart Contracts, Credential Stuffing, Public-Key Cryptography.
Modern legal institutions encounter significant difficulties ensuring document security, public access, and verification processes in digital environments. This research presents an innovative framework combining distributed ledger technology with decentralized file systems to address critical vulnerabilities in traditional court record management. Our solution leverages Ethereum's smart contract capabilities alongside the InterPlanetary File System (IPFS) to establish an immutable, transparent, and distributed architecture for judicial documentation. The proposed framework demonstrates significant improvements in data integrity verification, unauthorized access prevention, and system resilience. Through comprehensive testing using authentic judicial datasets, we validated the system's capacity to detect tampering attempts while maintaining efficient document retrieval. Key contributions include: (1) a novel three-tier architecture integrating blockchain immutability with IPFS content addressing, (2) automated verification protocols through smart contracts, and (3) enhanced transparency mechanisms enabling public verification of document authenticity. Performance evaluations reveal substantial improvements in security metrics while maintaining acceptable operational efficiency. This research establishes a foundation for next-generation judicial information systems that prioritize transparency, security, and public trust.
Abstract: This paper introduces an innovative access control architecture based on a dual-blockchain framework that distinctly separates access management from data storage to enhance system security, scalability, and privacy. Architecture employs a primary blockchain to manage user authentication and enforce dynamic, fine-grained permissions through smart contracts. In parallel, a secondary, isolated blockchain is used exclusively for storing sensitive data, which can only be accessed following successful authorization on the primary chain. To ensure data integrity and tamper resistance, the system utilizes the SHA-256 cryptographic hash function for securing access logs and verifying data authenticity across both blockchains. The two chains are securely interconnected using Hyperledger YUI, which facilitates reliable inter-chain communication while maintaining a decentralized structure. A proof-of-concept implementation using Ethereum-based blockchains demonstrates the system's capability to enforce secure, dynamic access controls across chains. Overall, the proposed architecture overcomes key limitations of conventional blockchain systems by enhancing modularity, strengthening governance, and providing a robust, adaptable framework suitable for data-sensitive applications requiring strict regulatory compliance.
This study presents a comprehensive analysis of the cryptocurrency market through the lens of classical and modern economic schools, focusing on key regulatory mechanisms: staking, halving, token burning, and asset locking. The relevance of the research stems from the need to develop a theoretical framework for managing the stability and liquidity of decentralized financial systems amid high volatility and technological transformation. The hypothesis posits that integrating principles from economic schools (classical, Keynesian, monetarist, Austrian, institutional) with algorithmic cryptocurrency mechanisms can create a hybrid model of market resilience. Using an interdisciplinary approach, including mathematical modeling, regression, and correlation analysis of data on Bitcoin, Ethereum, XRP, and BNB, the study confirmed Bitcoin’s dominant role as a systemic asset through token burning and vesting. The practical implications include recommendations for optimizing regulatory mechanisms, diversifying investment portfolios, and designing stress tests to mitigate systemic risks.
Pharmaceutical supply chain is facing severe problems caused by counterfeiting medicines, unclear tracking system, and inefficient recalling method, which lead to huge economical losses and endanger the public's health. In this paper, we present a novel blockchain-based solution with Non-Fungible Token digital twin (NFT), which is based on Ethereum ERC-1155 standard, to construct an unchanged and transparent record for drug unit's whole lifecycle from manufacturing to final patient. Our system replaces the vulnerable centralized database with a decentralized one to guarantee the integrity of data, automatic compliance and immediate verification. Experimental results on Ethereum Sepolia testnet show that our system achieves 100% success rate for 9 transactions which are 7 transfer transactions and 2 creation transactions with average confirmation time just for 17.8s. The total operation cost for all transactions is only $0.01 USD which is very cost-effective. Base on the comprehensive analysis, our system can save 85-90% of operation cost and improve 95% of recalling time compared with traditional system. The experimental results in this paper prove the practical feasibility and feasibility of economy using NFT digital twin to manage the whole pharmaceutical supply chain, and provide a strong framework to fight against counterfeiting and ensure patient safety.
Voting is a critical process in any democratic country, but traditional methods like ballot papers and Electronic Voting Machines (EVMs) face several issues. These include a lack of transparency, low voter turnout, vote tampering, mistrust in the election process, voter ID forgery, delays in announcing results, and major security concerns. When it comes to digital voting, ensuring security is one of the biggest challenges, as the system must be capable of protecting data and preventing cyber-attacks. Blockchain technology offers a potential solution to these problems. It is a decentralized system that allows transactions to take place in a secure, peer-to-peer network. Blockchain key feature search as immutability, decentralization, security, transparency, and anonymity make it a strong option for developing secure and reliable e-voting systems. By using smart contracts, blockchain can further enhance the security and transparency of digital voting. This paper presents a sample e-voting application implemented as a smart contract on the Ethereum blockchain using Solidity. The system uses wallets with limited tokens (gas) that are consumed during voting, ensuring that each voter can only vote once. The paper also discusses the pros and cons of blockchainbased voting and demonstrates a basic web application to show its functionality and limitations.
Hamza Ibrahim, Love Allen Chijioke Ahakonye, Jae-Min Lee, D. Kim
The Industrial Internet of Things (IIoT) has transformed critical infrastructure but has also introduced severe security vulnerabilities, with breaches capable of causing catastrophic physical and operational damage. While blockchain technology offers a promising foundation for tamper-proof logging, existing platforms are often ill-suited for IIoT due to high latency, low throughput, and excessive energy consumption. Furthermore, most current research treats intrusion detection, secure logging, and system recovery as isolated components, lacking a unified framework for autonomous, verifiable resilience. To bridge this critical gap, this paper introduces PureChain, a holistic, secure, and resilient ecosystem. PureChain integrates a custom lightweight blockchain with a deep learning-based intrusion detection system and a novel verifiable recovery protocol, creating a closed-loop security model. The framework leverages a novel Proof of Authority and Association (PoA2) consensus mechanism, achieving high throughput (16.82 TPS), low latency (0.0594 s), and minimal energy consumption (12.43 W), demonstrating suitability for resource-constrained IIoT environments compared to general-purpose platforms like Ethereum and Hyperledger which are optimized for different use cases. Upon intrusion detection by optimized models like XGBoost (99.87% accuracy), immutable blockchain logs actively trigger and cryptographically attest to infrastructure-enforced recovery actions such as device isolation via SDN switches or state rollback through hardware management controllers. Extensive evaluation on benchmark IIoT datasets (IoT-CAD and IoTForge) demonstrates a detection-to-recovery success rate of up to 98.59% while maintaining 100% data integrity. PureChain establishes a new paradigm that unifies real-time threat intelligence, blockchain-based trust, and provable autonomous recovery for next-generation IIoT security.
The preservation of historical records is critical for maintaining cultural heritage and providing future generations with accurate historical insights. Traditional storage methods are vulnerable to tampering, loss, and unauthorized access, presenting challenges in safeguarding document authenticity. This paper proposes a hybrid blockchain-based document management system, utilizing Quorum as a private, permissioned blockchain for authorized modifications by trusted historians and institutions, and Ethereum as a public blockchain for immutable record verification. By integrating the InterPlanetary File System (IPFS) for decentralized storage and employing smart contracts to enforce access control, this system ensures secure modification and transparent public access for document verification. Can be developed in collaboration with entities such as the International Historical Research Consortium (IHRC) and the Indian Council of Historical Research, the proposed architecture facilitates a trustworthy digital archive that meets academic standards for historical document preservation.
Blockchain technology is a somewhat new approach to finding the integrity and chain of digital evidence in various industries, including law enforcement, forensic investigations, supply chain management, and judicial proceedings. Although traditional evidence-keeping systems are prone to manipulation, loss, and inefficiency, blockchain offers an immutable, transparent, and decentralized ledger that securely records and validates every evidence-related transaction. Blockchain technology increases reliability in handling both physical and digital evidence. It uses distributed consensus, intelligent contracts, and cryptographic hashing to eliminate human error and backdoor intervention by assuring immutability, accountability, and automation. This study offers a model blockchain (Chain of Digital Evidence) based on the Ethereum blockchain to guarantee integrity and authenticity in the chain of digital evidence. Ethereum&s;s decentralization ensures that digital evidence is free from manipulation, transparent, and easily verifiable. The study discusses other challenges and prospects for integrating the Ethereum blockchain into the digital evidence chain.