Background Smart contracts are revolutionizing the way two or more parties subscribe to and apply an agreement. The main reason is that they promise to increase efficiency, transparency, and security. However, their inherent vulnerabilities can lead to automated exploits, resulting in significant resource losses. Among these, reentrancy is one of the most impactful security flaws. Over the past few years, reentrancy vulnerabilities have caused substantial financial damage and threatened the viability of entire blockchain ecosystems. Therefore, investigating whether reentrancy can be effectively mitigated and exploring the methodologies designed to address it is crucial. Methodology This paper aims to provide a comprehensive understanding of the impact of reentrancy vulnerabilities in Ethereum smart contracts and to assess the theoretical and practical approaches proposed to counter them. By following the PRISMA framework, we conduct a literature review of academic publications to identify, screen, and analyze relevant studies on detection and mitigation techniques. Results Our findings indicate that the estimated financial loss due to reentrancy attacks amounted to around 350 million USD by 2023, with increasing frequency and profitability of incidents. Despite advancements in mitigation tools, particularly machine learning, they only partially address vulnerabilities and remain ineffective against zero-day attacks. Contribution This paper identifies critical challenges in reentrancy detection, including the lack of standardized benchmarks and data on zero-day vulnerabilities. It emphasizes the need for unified datasets and evaluation frameworks to facilitate fair comparisons, improving detection effectiveness. Additionally, it highlights the need for tools addressing all four reentrancy vulnerability types and reporting performance results.
This research article presents a novel architecture to empower multi-agent economies by addressing two critical limitations of the emerging Agent2Agent (A2A) communication protocol: decentralized agent discoverability and agent-to-agent micropayments. By integrating distributed ledger technology (DLT), this architecture enables tamper-proof, on-chain publishing of AgentCards as smart contracts, providing secure and verifiable agent identities. The architecture further extends A2A with the x402 open standard, facilitating blockchain-agnostic, HTTP-based micropayments via the HTTP 402 status code. This enables autonomous agents to seamlessly discover, authenticate, and compensate each other across organizational boundaries. This work further presents a comprehensive technical implementation and evaluation, demonstrating the feasibility of DLT-based agent discovery and micropayments. The proposed approach lays the groundwork for secure, scalable, and economically viable multi-agent ecosystems, advancing the field of agentic AI toward trusted, autonomous economic interactions.
Coffee is consumed worldwide, with its supply chain starting with coffee growers, who benefit least from it. Across its production, distribution, and commercialization processes, there are risks and issues that could damage the safety and authenticity of this product. Therefore, the coffee industry is looking for innovative technologies that allow traceability in the coffee supply chain. In this context, blockchain technology offers a promising solution as it supports traceability via a decentralized system that allows immutable records and transparent access; it also promotes collaborative work and removes intermediaries by generating trust between participants. This systematic literature review describes the state-of-the-art in research and development about the use of blockchain technology to improve traceability in the coffee supply chain. We also outline the open challenges that remain to be addressed in this field. We use the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology to achieve this goal. Our findings suggest that the developments are mainly conceptual designs and prototypes, focusing on tracing products and verifying their authenticity using the Ethereum or Hyperledger blockchains. Also, our results show various challenges on the technology side, like efficiency improvements, integration with other technologies, infrastructure, and a lack of standards. There are also challenges at the management level, like the necessity of agreements for traceability processes, data governance, willingness to invest and pay, education, and support to deploy the technology on farms. After overcoming these open challenges, blockchain technology can improve traceability and increase value for stakeholders in the coffee supply chain.
Cryptocurrencies like Bitcoin can be considered commodities under the Commodity Exchange Act (CEA) and the Commodity Futures Trading Commission (CFTC) has jurisdiction over cryptocurrencies considered to be commodities, particularly in the context of futures trading. This paper presents a method for long and short term trend prediction of certain cryptocurrencies which is predicated on an application of the Fractal Market Hypothesis. This is an area of market theory where the self-affine properties of a fractal stochastic field are used to model a financial time series. After an introduction to the underlying theory and mathematical modelling, a fundamental analysis of Bitcoin and Ethereum to U.S. Dollar exchange markets is conducted. This analysis is based on a consideration that a changes in polarity of the 'Beta-to-Volatility' and the 'Lyapunov-to-Volatility' ratios to indicate an impending change to the Bitcoin/Ethereum price trend signal. This is used to recommend a long, a short or a hold trading position for which algorithms are provided (coded in Matlab) and 'back-tested'. An optimisation of these algorithms is conducted, leading to a strategy for implementing an ideal range of the key parameters for 'driving' the algorithms developed. This is based on maximising the accuracy and profitability to assure a high level of confidence. The application of the trading strategy developed through this approach is demonstrated to provide useful information to aid cryptocurrency investments and quantify the likelihood that the market will become bull or bear dominant. Under stable conditions, Machine Learning (using the 'TuringBot') is shown to provide useful estimates of future price values and/or fluctuations over small event horizons in time. This minimises any \lq trading delay' caused by filtering the data and increases returns by providing optimal trade positions within a \lq micro-trend' that is too fast for detection otherwise. In certain cases, this increase can reach ~10%. The results presented confirm that Bitcoin and Ethereum exchanges are self-affine (fractal) stochastic fields with L\'evy distributions, displaying a Hurst Exponent of ~ 0.32, a Fractal Dimension of ~ 1.68 and Levy Index of ~1.22. They also confirm that the Fractal Market Hypothesis and its indices provide a suitable market model, that generates returns on investments that outperform all Buy and Hold strategies based on more standard market indices.
P. Preethy Jemima, P. Privietha, Sylvia Grace J, Srideivanai Nagarajan · 5 authors
Smart contracts, which are implemented on decentralized blockchain systems to provide transparency, security, and immutability, are self-executing contracts with terms directly encoded into software. By removing middlemen and increasing efficiency and trust, these contracts automatically carry out predetermined activities once criteria are satisfied. Finance, supply chain management, real estate, and gaming are among the key uses. Notwithstanding their potential, smart contracts have some serious drawbacks, the most significant of which being security flaws. Incidents like the DAO hack on Ethereum provide as examples of how bugs, re-entrancy attacks, and unrestricted external calls have resulted in large financial losses. Widespread use requires addressing these issues. Developers should do thorough testing, including unit, integration, and edge case tests, and adhere to published recommendations such as Ethereum Smart Contract Security Best Practices in order to address security vulnerabilities in smart contracts. Thorough audits of both internal and external code are crucial, as are post-audit corrections. While proper management of external interactions, such as avoiding external calls and validating return values, lowers risks, security tools like Certora, MythX, and Slither can assist in identifying vulnerabilities. Security is further improved by employing multi-signature wallets for high-stakes transactions, using Checks-Effects-Interactions patterns and mutex locks to mitigate reentrancy attacks, and using Flashbots to stop transaction front-running. Additional protections include fallback procedures for recovering from unforeseen failures, monitoring solutions, and reusable contracts for emergency halts. When combined, these steps provide a strong foundation for risk reduction, enhanced security, and guaranteeing the dependability of smart contracts in practical implementations.
This study examined the dynamic interconnectedness and portfolio implications within the cryptocurrency ecosystem, focusing on five representative digital assets across the core functional categories: Layer 1 cryptocurrencies (Bitcoin (BTC) and Ethereum (ETH)), decentralized finance (Uniswap (UNI)), stablecoins (Dai), and crypto infrastructure tokens (Maker (MKR)). Using the Extended Joint Connectedness Approach within a Time-Varying Parameter VAR framework, the analysis captured time-varying spillovers of return shocks and revealed a heterogeneous structure of systemic roles. Stablecoins consistently acted as net absorbers of shocks, reinforcing their defensive profile, while governance tokens, such as MKR, emerged as persistent net transmitters of systemic risk. Foundational assets like BTC and ETH predominantly absorbed shocks, contrary to their perceived dominance. These systemic roles were further translated into portfolio design, where connectedness-aware strategies, particularly the Minimum Connectedness Portfolio, demonstrated superior performance relative to traditional variance-based allocations, delivering enhanced risk-adjusted returns and resilience during stress periods. By linking return-based systemic interdependencies with practical asset allocation, the study offers a unified framework for understanding and managing crypto network risk. The findings carry practical relevance for portfolio managers, algorithmic strategy developers, and policymakers concerned with financial stability in digital asset markets.
The rapid evolution of telemedicine has enhanced healthcare accessibility, yet significant challenges persist, particularly in data security, patient engagement, latency, and scalability. Existing telemedicine solutions rely on centralized architectures, making Electronic Health Records (EHRs) susceptible to data breaches and unauthorized access. This research proposes a novel system which integrates the metaverse and blockchain into telemedicine which can be a transformative approach to solve problems in remote healthcare. By combining immersive virtual environments with decentralized data management, the proposed solution described in this paper aims to give users more ways to interact with each other, enhanced data security, increased efficiency, and higher scalability. The Metaverse serves as the foundation for the implementation of 3D consultation rooms, virtual training spaces, and individual care. Blockchain offers safe, transparent, and immutable data exchange that will create patient-empowered medical records for them. Real-time devices and analysis of real-time physiological data from wearables, sensors, Internet of Things (IoT) devices, and Artificial Intelligence (AI) analytics complete the system. The proposed solution extensively uses Virtual Reality (VR)/Augmented Reality (AR) devices, IoT sensors, Ethereum, and the Unity 3D platform, among others. Assessments indicate that system receives a significantly high level of satisfaction from its users, better secured data, increased automation of processes, and compliance with global standards such as General Data Protection Regulation (GDPR). Compliance with such global standards is achieved through smart contract-based access management, smart contract-based consent management, and immutable audit trails in the blockchain. Moreover, this research demonstrates that incorporating high-tech tools like AI and VR into telemedicine is currently feasible. This paves the way for the creation of even more secure and user-friendly telemedicine platforms that employ neural networks. This research sets a foundation for next-generation telemedicine ecosystems.
Lambard Maxence, Cyrille Bertelle, D apos Amours Claude
In an increasingly complex contractual landscape, the demand for transparency, security, and efficiency has intensified. Blockchain technology, with its decentralized and immutable nature, addresses these challenges by reducing intermediary costs, minimizing fraud risks, and enhancing system compatibility. Smart contracts, initially conceptualized by Nick Szabo and later implemented on the Ethereum blockchain, automate and secure contractual clauses, offering a robust solution for various industries. However, their complexity and the requirement for advanced programming skills present significant barriers to widespread adoption. This study introduces a multi-level finite state machine model designed to represent and track the execution of smart contracts. Our model aims to simplify smart contract development by providing a formalized framework that abstracts underlying technical complexities, making it accessible to professionals without deep technical expertise. The hierarchical structure of the multi-level finite state machine enhances contract modularity and traceability, facilitating detailed representation and evaluation of functional properties. The paper explores the potential of this multi-level approach, reviewing existing methodologies and tools, and detailing the smart contract generation process with an emphasis on reusable components and modularity. We also conduct a security analysis to evaluate potential vulnerabilities in our model, ensuring the robustness and reliability of the generated smart contracts.
S M Mostaq Hossain, Amani Altarawneh, Maanak Gupta
As blockchain technologies are increasingly adopted in enterprise and research domains, the need for secure, scalable, and performance-transparent node infrastructure has become critical. While self-hosted Ethereum nodes offer operational control, they often lack elasticity and require complex maintenance. This paper presents a hybrid, service-oriented architecture for deploying and monitoring Ethereum full nodes using Amazon Managed Blockchain (AMB), integrated with EC2-based observability, IAM-enforced security policies, and reproducible automation via the AWS Cloud Development Kit. Our architecture supports end-to-end observability through custom EC2 scripts leveraging Web3.py and JSON-RPC, collecting over 1,000 real-time data points-including gas utilization, transaction inclusion latency, and mempool dynamics. These metrics are visualized and monitored through AWS CloudWatch, enabling service-level performance tracking and anomaly detection. This cloud-native framework restores low-level observability lost in managed environments while maintaining the operational simplicity of managed services. By bridging the simplicity of AMB with the transparency required for protocol research and enterprise monitoring, this work delivers one of the first reproducible, performance-instrumented Ethereum deployments on AMB. The proposed hybrid architecture enables secure, observable, and reproducible Ethereum node operations in cloud environments, suitable for both research and production use.
Junliang Luo, Katrin Tinn, Şengül Duran, Di Wu · 5 authors
Tokenized U.S. Treasuries have emerged as a prominent subclass of real-world assets (RWAs), offering cryptographically secured, yield-bearing instruments issued across multi-chain Web3 infrastructures, with growing significance for transparency, accessibility, and financial inclusion. While the market has expanded rapidly, empirical analyses of transaction-level behaviours remain limited. This paper conducts a quantitative, function-level dissection of U.S. Treasury-backed RWA tokens, including BUIDL, BENJI, and USDY across multi-chain: mostly Ethereum and Layer-2s. Decoded contract calls expose core financial primitives such as issuance, redemption, transfer, and bridging, revealing patterns that distinguish institutional participants from smaller or retail users for the extent and limits of inclusivity in current RWA adoption. To infer address-level economic roles, we introduce a curvature-aware representation learning model. Our method outperforms baseline models in role inference on our collected U.S. Treasury transaction dataset and generalizes to address classification across broader public blockchain transaction datasets. The decoded transaction-level patterns in tokenized U.S. Treasuries across chains surface the degree of retail participation, and the role inference model enables the distinction between institutional treasuries, arbitrage bots, and retail traders based on behavioral patterns, facilitating future more transparent, inclusive, and accountable Web3 finance.
This study aims to analyze and compare the performance of three major cryptocurrencies—Bitcoin, Ethereum, and Solana—during the 2021–2024 period, based on return, risk, and risk-adjusted performance indicators (Sharpe Ratio). The research applies a comparative quantitative method using secondary data from CoinMarketCap. The analysis includes descriptive statistics, annual return calculations, standard deviation, Value at Risk (VaR), Expected Shortfall (ES), and Sharpe Ratio. ANOVA was used to test differences in return, while Kruskal-Wallis and Mann-Whitney U tests were employed for risk and performance due to non-normal data distributions. The results show no significant differences in average return among the three assets. However, there are significant differences in risk levels, with Solana being the most volatile, followed by Ethereum and Bitcoin. In terms of Sharpe Ratio, no significant difference in performance was found. These findings indicate that while there are absolute differences in return and risk, the three assets provide a balanced level of return when adjusted for risk. Hence, diversification among these assets may serve as a relevant strategy for investors depending on their risk profiles.
Ovaj rad istražuje teorijske temelje i praktičnu primjenu dokaza nultog znanja u blockchain sustavima, s fokusom na Polygon zkEVM blockchain. Analiziraju se zk-SNARK i zk-STARK sustavi dokazivanja te njihova implementacija u ZK-rollup rješenjima za poboljšanje skalabilnosti blockchain mreža. Teorijska analiza pokazuje kako dokazi nultog znanja omogućavaju verifikaciju transakcija bez otkrivanja osjetljivih podataka, čime se adresiraju izazovi privatnosti i skalabilnosti. Praktični dio uključuje implementaciju decentralizirane aplikacije za glasovanje na Polygon zkEVM Cardona Testnet mreži, demonstrirajući primjenu tehnologije u realnoj situaciji. Analiza transakcijskih podataka potvrđuje značajne uštede goriva kroz batch procesiranje transakcija u odnosu na direktno izvršavanje na Ethereum glavnom lancu. Rad identificira ključne prednosti i ograničenja trenutnih implementacija te predlaže smjerove za buduća istraživanja u području post-kvantne kriptografije i hardverske akceleracije.
The traditional voting process, whether paper-based or electronic, is often criticized for its lack of transparency, susceptibility to fraud, and dependence on centralized authorities. Blockchain technology, particularly in the Web3 ecosystem, provides a decentralized, secure, and tamper-proof solution for digital voting. This paper explores how blockchain can enhance election integrity by leveraging decentralized applications (DApps), smart contracts, and cryptographic security. The proposed system employs Ethereum-based smart contracts to automate vote casting and tallying while ensuring voter privacy through zero-knowledge proofs. Decentralized Identity (DID) is integrated for secure authentication, preventing double voting and identity fraud. The paper discusses system architecture, security considerations, scalability challenges, and real-world applications of blockchain voting, highlighting how Web3 can transform democratic elections.
This paper provides a comprehensive empirical analysis of the economics and dynamics behind arbitrages between centralized and decentralized exchanges (CEX-DEX) on Ethereum. We refine heuristics to identify arbitrage transactions from on-chain data and introduce a robust empirical framework to estimate arbitrage revenue without knowing traders' actual behaviors on CEX. Leveraging an extensive dataset spanning 19 months from August 2023 to March 2025, we estimate a total of 233.8M USD extracted by 19 major CEX-DEX searchers from 7,203,560 identified CEX-DEX arbitrages. Our analysis reveals increasing centralization trends as three searchers captured three-quarters of both volume and extracted value. We also demonstrate that searchers' profitability is tied to their integration level with block builders and uncover exclusive searcher-builder relationships and their market impact. Finally, we correct the previously underestimated profitability of block builders who vertically integrate with a searcher. These insights illuminate the darkest corner of the MEV landscape and highlight the critical implications of CEX-DEX arbitrages for Ethereum's decentralization.
An address is indicated as an identifier of the user on the blockchain, and is defined by a hash value of the ECDSA verification key. A vanity address is an address that embeds custom characters such as a name. To generate a vanity address, a classical try-and-error method is employed, and thus the number of characters to be embedded is limited. In this paper, we focus on the functionality of identity-based signatures (IBS) where any strings can be employed as a verification key, and explore whether IBS can be used for generating a vanity address. We attach importance to the fact that it is not realistic to replace ECDSA with key recovery, which is currently employed for issuing transactions in Ethereum, to an IBS scheme. Even if this replacement is possible, it is not a reasonable price for the ease of the vanity address generation. Thus, we pay attention to a generic construction of IBS from signatures, and construct an IBS scheme from ECDSA with key recovery. Though we cannot directly generate a vanity address due to the key recovery functionality of the underlying ECDSA, we can connect any string with an address due to the functionality of IBS that can give additional meaning to the address. We implement our system by Solidity, and demonstrate that the gas cost is almost same as that of the ECDSA signature verification.
We consider the online algorithmic challenge that is faced by blockchains that have multidimensional block constraints and serve quasi-patient bidders. We first provide online approximation algorithms for the important special cases of small transactions or a small number of dimensions; this solves open problems left by [Babaioff and Nisan, EC 2025]. Second, we study multidimensional variants of Ethereum's EIP-1559 protocol. We show that if the block builders manage to approximately optimize each block's welfare myopically, then an approximation to the global offline optimal welfare is obtained. On the other hand, we show that, unlike in the single-dimensional case, EIP-1559 by itself does not guarantee any good approximation.
Blockchain technologies had a significant impact on many sectors of contemporary society, with virtual currencies being the most prominent example. The introduction of the Ethereum blockchain and its native support for smart contracts, self-enforcing programs that enable trustworthy digital interactions, has broadened the possible adoption contexts. These programs possess unique characteristics, such as code immutability and autonomous execution, which necessitate innovative testing methodologies. Despite their growing adoption, current testing practices and tools for smart contracts lag behind those available for traditional software systems, raising concerns about the reliability of decentralized applications. This dissertation addresses these challenges through three core research objectives. First, it advances mutation testing for Ethereum smart contracts by introducing a practical framework and tool that support Solidity-specific test adequacy assessment and incremental mutation analysis during development. This enables developers to systematically evaluate and improve the fault-detection capabilities of their test suites based on metrics that go beyond simple code coverage. Second, it explores how smart contract auditing practices can benefit from mutation testing by integrating live mutant inspection into code reviews and automating the generation of missing test cases. This contribution enhances the auditors’ ability to identify weaknesses in the test suite and provide actionable feedback to clients. Third, it supports smart contract maintenance activities by proposing a novel capture-replay testing framework and tool for upgradeable contracts. The approach harnesses historical blockchain transactions as tests, allowing developers to detect behavioral inconsistencies introduced by upgrades without the need to manually reconstruct testing scenarios. Through these contributions, the thesis aims to advance the state of smart contract quality assurance, offering both practical frameworks and theoretical insights that enhance the reliability of blockchain-based systems.
This study aims to analyze the impact of the U.S. Federal Reserve System’s monetary policy on major cryptocurrencies. Specifically, it explores whether the effects differ between volatile cryptocurrencies, such as Bitcoin and Ethereum, and the stablecoin Tether. To this end, we utilize an autoregressive distributed lag (ARDL) bounds testing approach, analyzing monthly data from January 2019 to April 2025. The empirical results indicate that the responses of volatile and stable cryptocurrencies to the Fed’s monetary policy differ. In the long term, the prices of Bitcoin and Ethereum tend to react positively to the Fed’s monetary policy changes, whereas Tether’s prices experience a negative impact. We recommend novel policy implications in this study based on these empirical findings.
With the advent of blockchain, Decentralised Finance (DeFi) has become an accessible and decentralised way to create financial services. Under the umbrella of DeFi services, we find Decentralised Exchanges (DEXs) i.e., smart contracts that allow one to exchange tokens. Over time, new malicious activities have begun to spread, particularly those related to DEXs. Maximal Extractable Value (MEV), a practice in which block creators can add, remove, or change the order of transactions to increase their gains at the expense of other users, is becoming pervasive.In this study, we collected a 2-year dataset and analysed the MEV activity on the Ethereum blockchain. With our analyses, we show that despite the countermeasures, the problem is still actual and that the value extracted could hinder the adoption of Ethereum in future projects. Furthermore, we provide an in-depth analysis of DEX platforms and tokens that are more susceptible to MEV attacks, showing that major markets are still far from solving the problem. Finally, we show that MEV attackers are becoming more sophisticated as they tend to chain different types of attacks (i.e., sandwich and arbitrage). Linked attacks are more profitable, as attackers extract more than 5 billion USD, while traditional attacks net 382 million USD.
Sanctioning blockchain addresses has become a common regulatory response to malicious activities. However, enforcement on permissionless blockchains remains challenging due to complex transaction flows and sophisticated fund-obfuscation techniques. Using cryptocurrency mixing tool Tornado Cash as a case study, we quantitatively assess the effectiveness of U.S. Office of Foreign Assets Control (OFAC) sanctions over a 957-day period, covering 6.79 million Ethereum blocks and 1.07 billion transactions. Our analysis reveals that while OFAC sanctions reduced overall Tornado Cash deposit volume by 71.03% to approximately 2 billion USD, attackers still relied on Tornado Cash in 78.33% of Ethereum-related security incidents, underscoring persistent evasion strategies. In this paper, we identify three significant, structural limitations in current sanction enforcement practices: (i) fragmented censorship in blockchain consensus and application layer; (ii) the complexity of obfuscation virtual asset services exploited by users; and (iii) the susceptibility of naive binary sanction classifications to dusting attacks. Our analysis and findings contribute to ongoing discussions around regulatory effectiveness in Decentralized Finance by providing empirical evidence, clarifying enforcement challenges, and informing future compliance strategies in response to sanctions and blockchain-based security risks.
Alisa Kalacheva, Pavel Kuznetsov, Igor Vodolazov, Yury Yanovich
The rise in cryptoasset valuations and the ease of creating new tokens have spurred an increase in illicit activities within the market. Decentralized exchanges (DEX) facilitate the trading of a vast array of tokens, including those with minimal liquidity, amplifying the risk of fraudulent schemes. Fraudulent practices take various forms, including counterfeit tokens, rug pulls, and pump-and-dump schemes, all lacking functional innovation and relying heavily on aggressive social media marketing. This study contributes to the identification and profiling of deceitful tokens on DEX platforms. Our approach involved compiling on new tokens with an active trading start and attracted competition to buy them in first blocks spanning multiple years from the Ethereum blockchain, tracking all associated purchase and sale transactions. Our analysis revealed that Uniswap V2 predominantly hosts the trading of new tokens, with an alarming discovery that over 98% of tokens minted daily exhibit fraudulent characteristics. Subsequently, a machine learning model was developed to predict the likelihood of a rug pull occurring shortly after trading commencement. Although the dataset labeling methodology and detection problem statement are exploratory, we demonstrate the economic significance of the proposed approach within trading pipeline. The findings highlight the importance of identifying fraudulent activities and emphasize the need for collaboration between decentralized exchanges and regulatory bodies to mitigate financial losses for investors.
This paper presents the design, development, and thorough evaluation of a novel network security prototype that integrates Artificial Intelligence (AI) and blockchain technology to significantly enhance cyber security. As AI becomes increasingly embedded in cybersecurity solutions, ensuring the provenance, accountability, and integrity of AI-generated decisions has emerged as a critical challenge. Without reliable logging mechanisms, AI models remain vulnerable to adversarial manipulation and pose significant risks to critical security infrastructure. To address this, our research combines a state-of-the-art Convolutional Neural Network (CNN)-based threat detection module with a permissioned Ethereum-compatible blockchain. A custom-designed Solidity smart contract ensures secure, structured storage of comprehensive AI model metadata, while interactions with the blockchain are seamlessly managed through a lightweight Flask-based REST API. Each recorded transaction generates a unique cryptographic fingerprint, providing robust evidence for audits and forensic analyses. We evaluated the system's effectiveness through rigorous experimentation on a controlled test network, confirming immutability, traceability, and verifiable integrity of all logged metadata entries. Results demonstrated significant improvements in anomaly detection accuracy, reduced false-positive rates, and ensured real-time responsiveness essential for effective intrusion prevention. Despite controlled-environment limitations, such as transaction latency and blockchain-related operational costs, our prototype successfully establishes proof-of-concept for leveraging blockchain as an immutable audit trail for AI-driven cybersecurity systems. Future research directions include integrating advanced scaling techniques, such as layer 2 solutions, and extending the blockchain logging capabilities to cover the entire AI model lifecycle, including detailed training logs and comprehensive version histories. This work provides foundational contributions towards building trusted, auditable, and transparent AI solutions in regulated cyber security domains.
The environmental impact of cryptocurrencies has attracted increasing scrutiny, largely due to the high energy consumption of blockchain networks. However, empirical research on the causal relationship between cryptocurrency trading activity and carbon emissions remains scarce. This study addresses this gap by analysing the dynamic interplay between cryptocurrency trading and CO₂ emissions for Bitcoin, Ethereum, and Binance Coin, using monthly data from January 2015 to September 2024. Employing the Toda-Yamamoto augmented Granger causality approach, we apply logarithmic transformations to ensure data stationarity and address integration and endogeneity concerns. Our results reveal a bidirectional Granger causality between Bitcoin trading and CO₂ emissions, suggesting a feedback loop between market activity and environmental impact. For Ethereum, we find a similar albeit weaker bidirectional causality from trading to emissions, while no significant causal link is detected for Binance Coin, likely reflecting its more energy-efficient consensus mechanism. These findings highlight the disproportionate environmental burden of proof-of-work cryptocurrencies and underscore the need for targeted regulatory responses. We recommend the adoption of carbon-sensitive crypto policies, such as mandatory energy usage disclosures and incentives for transitioning to sustainable consensus mechanisms. This study advances the environmental finance literature by providing robust empirical evidence on the links between digital asset markets and carbon emissions.
With the growing prevalence of electric vehicles (EVs), electrical grids face increasing strain due to heightened demand and potential overload during charging. This paper proposes a tokenized Ethereum-based framework that enables charging point operators (CPOs) and stations (CSs) to manage EV charging requests while ensuring grid stability through time flexibility (adjustable durations) and power flexibility (dynamic load modulation). Smart contracts automate peer-to-peer trading of charging parameters like energy needs and time limits, shifting loads to off-peak hours and adjusting prices based on real-time grid capacity. Simulations reveal that EV users who adopted time- and power-flexible charging experienced a 42% increase in participation compared to those using rigid, fixed-rate systems. Two scenarios were tested: 1) requests every 15 minutes on a 33 kW grid, where smart charging achieved a 71% efficiency improvement over uncontrolled charging and increased acceptance rates from 38% to 70%; and 2) consecutive requests to the same CSs, where acceptance rates rose from 23% to 43%, with smart charging reducing peak-to-valley load differences by 43–50%, flattening demand profiles. The system, developed on Ethereum using Remix IDE and MetaMask and tested on the Sepolia testnet, demonstrates higher electricity sales, improved grid stability, and enhanced flexibility in time, power, and cost. Tokenization incentivizes participation through rewards, allows users to bid for priority slots via proof-of-stake (PoS), and ties reputation metrics to token costs. Practical Byzantine Fault Tolerance (PBFT) ensures fault tolerance, while dynamic pricing and monetized flexibility create scalable EV-grid synergy, balancing supply-demand mismatches and attracting investors.