Jose Almarcha-Sanchez, Maria-Jesus Alba-Baena, Volodymyr Dubetskyy, Maria‐Dolores Cano
Abstract Open-source simulators let engineers stress-test blockchain ideas long before field deployment, yet few studies compare tools side-by-side. This tutorial article benchmarks two research-grade simulators, namely, BlockSim and Simewu, and the production-grade IOTA Hornet node under an identical traffic harness that runs on laptop-class hardware. Results show that consensus style dominates capacity. A DAG ledger that finalizes one milestone per second (≈ 6 tx s⁻¹) surpasses the 10 Transactions Per Second (TPS) ceiling of a six-node Bitcoin simulation, while Ethereum-style 12 s blocks lift the same mesh to approximately ~ 20TPS.BlockSim reproduces proof-of-work fairness within ± 3% of theoretical expectations, and a ten-fold increase in propagation delay cuts a miner’s reward roughly in half despite equal hash power. Hornet delivers protocol-truth execution, but at noticeably higher CPU, memory and bandwidth cost than the simulators. All scripts, Docker files and raw logs are released under an open license, providing a one-click baseline for future benchmarking of new distributed-ledger technologies.
The global digital identity landscape is undergoing an unprecedented crisis. Approximately 1.1 billion individuals worldwide lack any verifiable form of digital identity, while existing identity systems face existential threats from the industrialization of deepfake technology with injection attacks targeting biometric verification surging 900% since 2022 and occurring at a rate of once every five minutes in 2024. Simultaneously, conventional blockchain-based identity proposals that store biometric templates on-chain introduce critical privacy vulnerabilities incompatible with emerging regulatory frameworks including the EU AI Act (2024) and GDPR. This paper presents ZKP-GDIS (Zero-Knowledge Proof Global Decentralized Identity System), a novel, privacy-by-design identity architecture that fundamentally departs from prior work in three key dimensions. First, ZKP-GDIS never stores raw biometric data on-chain; instead, it employs zk-SNARK (Zero-Knowledge Succinct Non-Interactive Argument of Knowledge) cryptographic commitments that allow identity verification without any disclosure of underlying biometric features. Second, we introduce a Hybrid Deepfake-Resistant Liveness Pipeline (HDRLP) — a multi-modal anti-spoofing layer that fuses passive CNN-based texture analysis, photoplethysmography (PPG) heart-rate detection, and hardware-attested device fingerprinting to defeat both presentation and injection attack vectors. Third, the system adopts W3C Decentralized Identifier (DID) standards and implements a federated governance model, enabling cross-jurisdictional interoperability while respecting national digital sovereignty. We provide formal security proofs under the computational Diffie-Hellman hardness assumption, evaluate the system against the ISO/IEC 30107-3 Presentation Attack Detection benchmark, and report experimental results demonstrating 99.87% genuine acceptance rate, 0.004% false acceptance rate under deepfake attack, and 94% reduction in on-chain gas costs versus Ethereum mainnet through zkEVM Polygon deployment. ZKP-GDIS establishes a reproducible, standards- compliant, and audit-ready framework for the next generation of global digital identity infrastructure.
This study aims to comparatively examine the relationships between Bitcoin and Ethereum's energy consumption and price dynamics. Using daily frequency data, Augmented Dickey-Fuller (ADF), Phillips-Perron (PP), ARDL cointegration tests, and Toda–Yamamoto causality analysis were applied to evaluate the effects of cryptocurrency markets on energy demand from both short-term and long-term perspectives. The analysis results indicate that there is a long-term cointegration relationship between energy consumption and prices for Bitcoin and a unidirectional causality from prices to energy consumption. In contrast, ARDL boundary test results for Ethereum revealed no long-term relationship, and causality analysis also failed to detect any directional causality between price and energy consumption. This indicates that with Ethereum's transition to a Proof-of-Stake mechanism, energy consumption has become independent of price movements. The findings reveal that the effects of cryptocurrency markets on the energy economy vary according to technology-specific structural characteristics.
Saber Zerhoudi, Kanishka Ghosh Dastidar, Felix Klement, Artur Romazanov · 12 authors
Moltbook is a social network where every participant is an AI agent. We analyze 1,312,238 posts, 6.7~million comments, and over 120,000 agent profiles across 5,400 communities, collected over 40 days (January 27 to March 9, 2026). We evaluate the platform through three layers. At the interaction layer, 91.4% of post authors never return to their own threads, 85.6% of conversations are flat (no reply ever receives a reply), the median time-to-first-comment is 55 seconds, and 97.3% of comments receive zero upvotes. Interaction reciprocity is 3.3%, compared to 22-60% on human platforms. An argumentation analysis finds that 64.6% of comment-to-post relations carry no argumentative connection. At the content layer, 97.9% of agents never post in a community matching their bio, 92.5% of communities contain every topic in roughly equal proportions, and over 80% of shared URLs point to the platform's own infrastructure. At the instruction layer, we use 41 Wayback Machine snapshots to identify six instruction changes during the observation window. Hard constraints (rate limit, content filters) produce immediate behavioral shifts. Soft guidance (``upvote good posts'', ``stay on topic'') is ignored until it becomes an explicit step in the executable checklist. The platform also poses technological risks. We document credential leaks (API keys, JWT tokens), 12,470 unique Ethereum addresses with 3,529 confirmed transaction histories, and attack discourse ranging from template-based SSH brute-forcing to multi-agent offensive security architectures. These persist unmoderated because the quality-filtering mechanisms are themselves non-functional. Moltbook is a socio-technical system where the technical layer responds to changes, but the social layer largely fails to emerge. The form of social media is reproduced in full. The function is absent.
Aleksei Adadurov, Sergey Barseghyan, Anton Chtepine, Antero Eloranta · 6 authors
We study optimal auction design for Maximum Extractable Value (MEV) auction markets on Ethereum. Using a dataset of 2.2 million transactions across three major orderflow providers, we establish three empirical regularities: extracted values follow a log-normal distribution with extreme right-tail concentration, competition intensity varies substantially across MEV types, and the standard Revenue Equivalence Theorem breaks down due to affiliation among searchers' valuations. We model this affiliation through a Gaussian common factor, deriving equilibrium bidding strategies and expected revenues for five auction formats, first-price sealed-bid, second-price sealed-bid, English, Dutch, and all-pay, across a fine grid of bidder counts $n$ and affiliation parameters $ρ$. Our simulations confirm the Milgrom-Weber linkage principle: English and second-price sealed-bid auctions strictly dominate Dutch and first-price sealed-bid formats for any $ρ> 0$, with a linkage gap of 14-28\% at moderate affiliation ($ρ=0.5$) and up to 30\% for small bidder counts. Applied to observed bribe totals, this gap corresponds to \$10-18 million in foregone revenue over the sample period. We also document a novel non-monotonicity: at large $n$ and high $ρ$, revenue peaks in the interior of the affiliation parameter space and declines thereafter, as near-perfect correlation collapses the order-statistic spread that drives competitive payments.
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.
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.
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.
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.
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.
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.
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.
Optimizing asset exchanges on blockchain-driven platforms poses a novel and challenging graph query optimization problem. In this model, assets represent vertices and exchanges form edges, recasting the graph query task as a routing problem over a large-scale, dynamic graph. However, the existing solutions fail to solve the problem efficiently due to the non-linear nature of the edge weights defined by a concave swap function. To address the challenge, we propose PRIME, a two-stage iterative graph algorithm designed for the Token Graph Routing Problem (TGRP). The first stage employs a pruned graph search to efficiently identify a set of high-potential routing paths. The second stage formulates the allocation task as a strongly convex optimization problem, which we solve using our novel Adaptive Sign Gradient Method (ASGM) with a linear convergence rate. Extensive experiments on real-world Ethereum data confirm PRIME's advantages over industry baselines. PRIME consistently outperforms the widely-used Uniswap routing algorithm, achieving up to 8.42 basis points (bps) better execution prices on large trades while reducing computation up to 96.7%. The practicality of PRIME is further validated by its deployment in hedge fund production environments, demonstrating its viability as a scalable graph query processing solution for high-frequency decentralized markets.
This paper tackles the discovery of tMEV, that is, the Maximal Extractable Value on blockchains that arises from Token smart contracts. This scope differs from the existing MEV-discovery research, which analyzes application-layer contracts or attacker contracts, but ignores the wide and diverse range of token contracts. This paper presents a pipeline of techniques for tMEV discovery, including tSCAN, a static analysis tool for identifying non-standard supply-control functions in token contracts, and tSEARCH, a searcher that uncovers profitable tMEV opportunities by generating, refining, and solving token-specific constraints. By replaying real-world transactions, this paper demonstrates both the profitability of tMEV strategies and existing searchers' unawareness of them: the proposed tSEARCH extracts $10\times$ more profit than observed MEV activity on Ethereum. The practicality of tMEV searching is demonstrated through a prototype built on Slither, showing high effectiveness with low performance overhead.
This study provides an econometric investigation of Bitcoin’s return dynamics using daily data over 5.5 years from January 2020 to September 2025. This research deeply analyses the market behaviour of Bitcoin over other assets like Gold, Silver, Ethereum, Tether, Nifth50, BankNifty. In this analysis we used advanced time series and statistical models such as ARIMA, GARCH(1,1), Rolling GARCH, Half-Life estimation, and EGARCH models to evaluate conditional mean behavior, volatility clustering, persistence, asymmetric shock effects, and regime-dependent risk transmission. With the use of this models, rolling Garch reveals structural instability with persistence decline in later periods. EGARCH results asymmetric shock effects, where negative shocks increases volatility more than positive shocks. Forecasting models suggests that volatility will eventually return to its long term average, but risk is still expected to remain high for some time before normalizing. The analysis reveals strong conditional heteroskedasticity and near-integrated volatility persistence during crisis periods specific around the COVID-19 market collapse (2020), the FTX bankruptcy shock (2022), the April 2024 Bitcoin halving, and the 2025 Bybit exchange hack. Using various data visualizations, the analysis reveals high risky nature of Bitcoin trade with high returns compared to other assets. Deep learning model LSTM reveals the nature that closing price of next day is unpredictable as obvious in case of such high volatile nature of Bitcoin. These findings underline the importance and nature of trading in Bitcoin for individuals who are thinking to invest.
This Systematization of Knowledge (SoK) provides a comprehensive historical analysis of Maximal Extractable Value (MEV) in blockchain systems, tracing its conceptual evolution through three distinct eras. We organize the fragmented literature on MEV into a unified chronological framework, beginning with Era~I (August 2014 - August 2020), which introduced Miner Extractable Value from pmcgoohan's seminal Reddit warning through the ``Dark Forest'' recognition, covering Proof-of-Work systems with public mempools and Priority Gas Auctions. Era~II (August 2020 - April 2024) marks the generalization to Maximal Extractable Value, encompassing formal taxonomies, Realized Extractable Value, Proposer-Builder Separation, the Ethereum Merge, MEV-Boost, and the integration of non-atomic and CEX-DEX arbitrage. Era~III (April 2024, present) addresses the frontier of Cross-Chain MEV, beginning with early studies on Layer-2 ecosystems, where value extraction spans multiple blockchains, rollups, bridges, and sequencers. We present a conceptual taxonomy distinguishing potential from realized extractable value, and single-domain from cross-domain phenomena. Our systematization identifies mitigations that emerged in response to each era, highlights measurement challenges, and proposes a research agenda for standardized metrics, detection benchmarks, and cross-chain infrastructure design.
The increasing incidents of forged academic certificates and the inefficiencies of traditional verification systems highlight the urgent need for a secure, transparent, and reliable credential management mechanism. Conventional systems are largely centralised, time-consuming, and prone to manipulation, resulting in high administrative overhead and verification delays. We prepared an AI-Based Decentralized Academic Credential Verification System that leverages blockchain technology, smart contracts, and artificial intelligence to provide a tamper-proof platform for issuing, storing, and validating academic records. Artificial Intelligence is integrated to perform anomaly detection during certificate issuance and AI-based facial authentication for students, enhancing security and preventing fraudulent entries before blockchain storage. Students gain permanent, secure access to their verified credentials, while verifiers, such as employers, can instantly authenticate certificates using blockchain records or QR code scanning, eliminating the need for intermediaries. By integrating Ethereum, Solidity, Web3.js, IPFS, React.js, and AI models, the proposed system delivers a decentralized, scalable, and cost-effective solution that enhances trust, reduces verification time, and effectively combats academic credential fraud.
Alinsha S, A Althaf, Chris P Reji, Fahad Mohammed A · 6 authors
Electronic voting techniques have gained popularity as a contemporary alternative to traditional paper-based elections because of their effectiveness and accessibility. The current electronic voting methods, however, have significant security flaws, such as multiple voting, identity theft, centralized control, and a lack of transparency. Despite the fact that blockchain technology is decentralized, immutable, and auditable, many blockchainbased voting systems merely employ cryptographic credentials and lack robust voter identification verification processes. The blockchain-based electronic voting system SecureVote, which incorporates multi-factor verification and facial biometric authentication, is proposed in this study. Ethereum smart contracts are used by the system to guarantee transparent result calculation and tamper-proof vote storage. SecureVote employs one-time password (OTP) validation as a secondary authentication method in conjunction with client-side facial recognition and deep learning-based feature extraction. The suggested design makes use of Web3.js and a decentralized application (DApp) concept for safe wallet-based transaction signing and blockchain interaction. High authentication reliability, avoidance of double voting, and effective transaction processing with low gas overhead are all demonstrated by the experimental results. SecureVote combines biometric multifactor authentication with blockchain immutability to enhance the reliability, transparency, and integrity of remote voting.
Open access
2 source records
Internet Traffic Analysis and Secure E-voting
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
The Ethereum ecosystem, which secures over $381 billion in assets, fundamentally relies on client APIs as the sole interface between users and the blockchain. However, these critical APIs suffer from widespread implementation inconsistencies, which can lead to financial discrepancies, degraded user experiences, and threats to network reliability. Despite this criticality, existing testing approaches remain manual and incomplete: they require extensive domain expertise, struggle to keep pace with Ethereum's rapid evolution, and fail to distinguish genuine bugs from acceptable implementation variations. We present APIDiffer, the first specification-guided differential testing framework designed to automatically detect API inconsistencies across Ethereum's diverse client ecosystem. APIDiffer transforms API specifications into comprehensive test suites through two key innovations: (1) specification-guided test input generation that creates both syntactically valid and invalid requests enriched with real-time blockchain data, and (2) specification-aware false positive filtering that leverages large language models to distinguish genuine bugs from acceptable variations. Our evaluation across all 11 major Ethereum clients reveals the pervasiveness of API bugs in production systems. APIDiffer uncovered 72 bugs, with 90.28% already confirmed or fixed by developers, including one critical error in the official specifications themselves. Beyond these raw numbers, APIDiffer achieves up to 89.67% higher code coverage than existing tools and reduces false positive rates by 37.38%. The Ethereum community's response validates our impact: developers have integrated our test cases, expressed interest in adopting our methodology, and escalated one bug to the official Ethereum Project Management meeting. By making APIDiffer open-source, we enable continuous validation of Ethereum client API implementations, thereby strengthening the foundational integrity of the entire Ethereum ecosystem.
Abstract Software development plays a central role in digital sustainability, yet developers’ role and engagement remains understudied. Here we analyse nearly a decade of developer discussions available on the code repository Github on Ethereum, a widely used open-source blockchain platform. Using topic modelling, with interpretation supported by large language models and a sustainability framework for software systems, we trace how economic, environmental, social, individual, and technical sustainability themes emerge and evolve over time. We find that sustainability awareness, particularly related to energy efficiency and cost, intensifies during key events such as the transition from proof-of-work to proof-of-stake consensus, which substantially reduced energy use. We identify influential contributors and thematic specialisation, providing a transferable framework for understanding sustainability in emerging developer communities. These findings highlight the role of developer discourse in shaping sustainable software ecosystems and integrating sustainability into open-source development.