With the shift from Centralized Finance (CeFi) to Decentralized Finance (DeFi), financial transactions have become trustless and self-executing through blockchain platforms, creating new opportunities while exposing the ecosystem to significant fraud risks. However, due to the lack of centralized oversight and the vulnerabilities in the blockchain platforms, DeFi transactions still face several security challenges, including fraud, identity theft, insider threats, and data breaches. Various methods, including regulatory frameworks, machine learning (ML), and deep learning (DL) techniques, are employed to detect these threats, particularly fraud, in DeFi transactions. Although these approaches help identify fraudulent activities, they face challenges related to accuracy and zero-day attacks due to insufficient data and the complexity of emergingfraud patterns. This study presents a novel approach for detecting and profiling fraud attacks, including zero-day ones in DeFi transactions, thereby eliminating the reliance on wallet transaction history, a limitation that previous research has heavily depended on. The proposed approach leverages two key components: a novel analyzer named DeFiTransLyzer (V1.0) and an Advanced Genetic Algorithm (AGA) for fraud transaction profiling. DeFiTransLyzer extracts 79 features from transaction and wallet data. At the same time, the AGA incorporates advanced techniques, including Penalized Fitness Evaluation, Elite Retention Strategy, Dynamic Mutation Rate, and dynamic generation, to create precise fraud profiles. By focusing solely on transaction features, the model ensures that all fraudulent activities, including zero-day ones, initiated within the first transaction of a new account can be effectively detected, without relying on prior wallet activity. To address the scarcity of comprehensive validation datasets, we introduce BCCCDeFiFraudTrans-2025, which comprises 1,026,867 annotated Ethereum transaction samples from the DeFi ecosystem. Additionally, the study establishes two taxonomies for systematic classification, covering the literature on fraud detection and profiling methods. Experimental results demonstrate that the proposed method achieves superior accuracy, precision, and efficiency while offering interpretability through its profiling mechanism. These promising outcomes highlight the potential of AGA profiling to enhance the detection and identification of fraudulent activities, including zero-day ones within DeFi transactions, contributing to the security and resilience of blockchainbased financial systems.
Este trabalho apresenta uma metodologia de detecção de contratos inteligentes do tipo mixers na rede Ethereum. Utilizou-se um modelo de aprendizado de måquina baseado em Random Forest, treinado com transaçþes do Tornado Cash e balanceado com amostras de 100 endereços aleatórios não relacionados a mixers. O modelo foi treinado com dados de março de 2025 e validado em 29/10/2020, dia de alto volume de transaçþes, identificando corretamente 3 endereços do Tornado Cash.
Gas fees play a crucial role in Ethereum blockchain transactions, directly affecting the cost and efficiency of decentralized applications. This study analyzes gas fee patterns across transaction types, temporal trends, and anomalous behaviors using a dataset of 1,000 Ethereum transactions. The results reveal that the average gas price was 120.5 Gwei, with a standard deviation of 45.2 Gwei, highlighting significant variability. Smart contract functions exhibited varying gas usage, with mint operations consuming the highest average gas (1,500,000 units) compared to approve (1,200,000 units) and transfer (800,000 units). A positive correlation (r = 0.65) was observed between gas price and value transferred, suggesting that higher-value transactions often incur elevated gas fees. Temporal analysis showed predictable patterns, with peak gas prices occurring between 13:00 - 17:00 UTC during high network activity and lower prices between 02:00 - 06:00 UTC. Additionally, anomaly detection identified 15 outlier transactions, including one with an unusually high gas price of 500 Gwei, reflecting network congestion or prioritization strategies. These findings provide actionable insights for optimizing transaction costs and improving smart contract efficiency. Future research could explore layer-2 scaling solutions, alternative fee mechanisms, and machine learning approaches for gas price prediction. This study contributes to a deeper understanding of Ethereumâs gas fee dynamics, offering valuable guidance for developers, users, and researchers in the blockchain ecosystem.
This study investigates the relationship between gas consumption and value transferred in Ethereum smart contracts, offering insights into resource utilization and efficiency within the blockchain ecosystem. Analyzing a dataset of 1,000 smart contracts, a moderate positive correlation r=0.45,p<0.05 was observed, indicating that higher gas consumption generally corresponds to larger financial transactions. The average gas consumption per contract was found to be 58,451,329.47 units, with a standard deviation of 20,123,456.89, highlighting significant variability in computational resource usage. Similarly, the average value transferred was 7,851.47 ETH, ranging from 0.001 ETH to over 100,000 ETH, showcasing the diverse financial applications of smart contracts. Efficiency analysis, measured as the ratio of value transferred to gas consumed, revealed an average efficiency of 0.00013 ETH per unit of gas, with some contracts achieving up to 0.01 ETH per unit of gas and others as low as 0.000007 ETH per unit of gas, reflecting varying levels of optimization. Outliers with disproportionately high gas consumption relative to value transferred were identified, suggesting inefficiencies or unique use cases. These findings underscore the importance of optimizing smart contract design to minimize gas costs and improve performance. Future research directions include functionality-specific analyses, anomaly detection, comparative studies across blockchain platforms, and exploring the economic implications of gas consumption. This work provides actionable insights for developers, researchers, and policymakers aiming to enhance the efficiency and sustainability of decentralized systems.
Ethereum's transition from a Proof-of-Work (PoW) to a Proof-of-Stake (PoS) consensus mechanism has significantly altered the networkâs block generation process and transaction efficiency. This study investigates the impact of stake-based block generation on Ethereumâs transaction fees, block density, and overall network performance by analyzing a dataset containing 303 records of Ethereum blockchain activity. The findings reveal a strong positive correlation between block generation rate and stake reward (r = 0.78, p < 0.01) and coin stake (r = 0.74, p < 0.01), indicating that validators with larger stakes generate blocks more frequently. Additionally, transaction fees positively correlate with block density (r = 0.65, p < 0.01), suggesting that network congestion remains a key determinant of transaction costs, despite the PoS transition. Further analysis shows that Ethereumâs PoS system optimizes block space utilization, with an observed mean block density of 1393.6% and a transaction fee standard deviation of 0.12 ETH, demonstrating a more stable fee structure than PoW. The average transaction fee recorded is 0.179 ETH, with a maximum observed fee of 0.98 ETH and a minimum of 0 ETH in some cases. While PoS provides greater fee stability, minor fluctuations in fees persist due to congestion-related effects. Additionally, the mean stake reward is 0.98, suggesting a relatively stable staking incentive structure across different blocks.
Chetan Chauhan, Pradeep Laxkar, Ram Kumar Solanki, S. R. Parihar ¡ 6 authors
Blockchain technology has emerged as a promising paradigm for addressing the inherent vulnerabilities of Internet of Things (IoT) networks. Conventional IoT systems rely on centralized architectures that are prone to single points of failure, data breaches, and unauthorized access. This paper presents a blockchain-enabled secure communication framework for smart IoT systems that integrates symmetric encryption, distributed ledger validation, and smart-contractâdriven access control. The proposed model is formalized through mathematical definitions of encryption, hashing, and contract execution, and validated using simulation tools such as NS-3 and Ethereum-based test environments. Comparative results demonstrate that the framework significantly improves communication security, data integrity, and resistance to cyberattacks while reducing latency and energy consumption relative to traditional models. The findings suggest that blockchain integration provides a scalable, resilient, and efficient foundation for trustworthy IoT communication in smart environments.
Asset exchange models (AEMs) provide a physics-inspired framework for studying wealth formation. These models capture wealth distribution dynamics via pairwise money exchanges, yielding steady-state distributions from exponential to heavy-tailed power laws. However, empirical validation remains limited due to scarce real-world transaction data. Here, we bridge this gap by analyzing spectral properties of Markov transition matrices from both AEMs and Ethereum blockchain data, enabling quantitative comparison of model and empirical exchange dynamics. We assess thermodynamic equilibrium in exchange processes - specifically, detailed balance - and derive steady-state wealth distributions from transition matrices. We find that equilibrium systems' spectra contain only real eigenvalues and link Ethereum price changes to spectral shifts. We also investigate external factors (e.g., taxes), showing that advantages for richer individuals make wealth evolution path-dependent on initial distributions. Our work establishes a quantitative framework for validating AEMs with real data, advancing economic modeling and understanding of wealth formation.
T Buvaneswari, Mageshkumar Naarayanasamy Varadarajan, M. Mythily, Hemantha Kumar B N ¡ 8 authors
The swift expansion of the Internet of Things (IoT) has expedited the implementation of smart sensors, generating substantial volumes of time-series data that require safe, efficient, and dependable management. Current centralized systems have constraints in maintaining integrity, protecting communications, and deriving economic value from this data. A blockchain-based smart house gateway network is suggested to address security concerns in smart home environments. The framework has three layers: device, gateway, and cloud. Blockchain technology is integrated at the gateway layer to provide decentralized storage and safe data interchange, eliminating single points of failure inherent in centralized systems. This integration ensures authentication, high availability, and secure communication across devices and stakeholders. The system utilizes Ethereum blockchain technology and is assessed based on important parameters such as response speed and detection accuracy. Experimental study demonstrates that the framework much surpasses traditional methods, improving resilience and reliability in smart home IoT ecosystems. The suggested system offers a scalable approach for safe data management and dependable value exchange in dispersed settings.
Ethereum, sebagai salah satu aset kripto utama, memiliki volatilitas harga yang tinggi, sehingga menciptakan kebutuhan akan model prediksi yang akurat untuk membantu pengambilan keputusan investasi. Penelitian ini bertujuan untuk mengimplementasikan kinerja dua model machine learning populer, yaitu Gated Recurrent Unit (GRU) yang merupakan model deep learning untuk data sekuensial, dan Extreme Gradient Boosting (XGBoost) yang merupakan model ensemble. Data yang digunakan adalah data historis harga harian Ethereum yang mencakup fitur Open, High, Low, Close, Volume (OHLCV). Metode penelitian meliputi tahap pra-pemrosesan data seperti normalisasi Min-Max Scaler dan pembagian data dengan rasio 80% data latih dan 20% data uji. Evaluasi kinerja kedua model diukur menggunakan metrik Root Mean Squared Error (RMSE) dan R-squared (R²). Hasil pengujian menunjukkan bahwa model GRU menghasilkan prediksi yang lebih baik, mencapai nilai RMSE 101.37 dan R² 0.9718, sedangkan model XGBoost memperoleh nilai RMSE 107.29 dan R² 0.9656. Hal ini mengindikasikan bahwa kemampuan GRU dalam menangkap pola dan dependensi temporal pada data deret waktu lebih unggul untuk kasus prediksi harga Ethereum. Kesimpulan dari penelitian ini adalah model GRU lebih efektif dan dapat diandalkan untuk memprediksi harga Ethereum dibandingkan XGBoost dalam penelitian ini.
Hafsteinn Hjartarson, FjĂślnir Thrastarson, Anna SigrĂĂ°ur Ăslind, GĂsli HjĂĄlmtĂ˝sson
Abstract Permissioned blockchains have gained prominence as a means of decentralizing trust while retaining controlled access, particularly in enterprise settings and regulated peer-to-peer environments. These systems offer advantages in scalability, performance, and security; however, challenges persist in effectively managing membership and its interaction with consensus protocols. Ethereumâs transition to Proof-of-Stake has also been a transition to managed membership, where validators are actively monitored and penalized for non-performance. This paper examines the dynamic tension between membership management and consensus protocols in permissioned blockchains, as well as the benefits of active management in improving overall system performance. In this paper, we propose a framework for dynamic membership management that includes actively admitting, monitoring, and ejecting members. Our approach decouples membership management from the underlying blockchain construction process. Our simulations confirm the potential benefits of managed membership, in part to facilitate lightweight mechanisms for improved performance and reliability. Our findings suggest that dynamic membership management is a critical area of study with significant implications for the future design of permissioned blockchains. Our contributions provide a conceptual foundation for designing dynamic membership protocols in permissioned blockchains, filling a gap in the literature and offering practical solutions to enhance blockchain performance in controlled environments.
Remote Procedure Call (RPC) services have become a primary gateway for users to access public blockchains. While they offer significant convenience, RPC services also introduce critical privacy challenges that remain insufficiently examined. Existing deanonymization attacks either do not apply to blockchain RPC users or incur costs like transaction fees assuming an active network eavesdropper. In this paper, we propose a novel deanonymization attack that can link an IP address of a RPC user to this user's blockchain pseudonym. Our analysis reveals a temporal correlation between the timestamps of transaction confirmations recorded on the public ledger and those of TCP packets sent by the victim when querying transaction status. We assume a strong passive adversary with access to network infrastructure, capable of monitoring traffic at network border routers or Internet exchange points. By monitoring network traffic and analyzing public ledgers, the attacker can link the IP address of the TCP packet to the pseudonym of the transaction initiator by exploiting the temporal correlation. This deanonymization attack incurs zero transaction fee. We mathematically model and analyze the attack method, perform large-scale measurements of blockchain ledgers, and conduct real-world attacks to validate the attack. Our attack achieves a high success rate of over 95% against normal RPC users on various blockchain networks, including Ethereum, Bitcoin and Solana.
Financial transactions in Islam must adhere to sharia principles, avoiding the elements of gharar (uncertainty), maysir (speculation), and riba (interest). One of the most prominent technological innovations in this domain is the implementation of Smart Contracts in cryptocurrency trading on the Ethereum platform. However, their application still faces significant sharia compliance challenges, particularly concerning contractual uncertainty and the potential for misuse. This study aims to analyze the implementation mechanism of Smart Contracts from a sharia compliance perspective while exploring the opportunities and challenges of their adoption within the Ethereum Ponorogo Community. Employing a qualitative approach with a case study method, data was collected through interviews, observation, and documentation. The findings reveal that while Smart Contracts offer greater transparency, efficiency, and automation in transactions, challenges such as regulatory ambiguity, limited sharia literacy, and the inherent volatility of crypto assets remain major obstacles. Although the Ethereum Ponorogo Community has made efforts to avoid non-compliant elements, their practices are more "aspirational" and not yet fully guaranteed by a comprehensive sharia regulatory framework. This research concludes that a clear roadmap is needed, which includes a focus on education, stronger regulations (including fatwas from sharia authorities), and audits of Smart Contract code. By following this path, the technology can become a more ethical and sharia-compliant solution for crypto transactions, bridging the gap between technological innovation and the principles of Islamic economics.
Andrada Cristina Artenie, Diana Laura Silaghi, Daniela Elena Popescu
Blockchain technologies, despite their profound transformative potential across multiple industries, continue to face significant scalability challenges. These limitations are primarily observed in restricted transaction throughput and elevated latency, which hinder the ability of blockchain networks to support widespread adoption and high-volume applications. To address these issues, research has predominantly focused on Layer 1 solutions that seek to improve blockchain performance through fundamental modifications to the core protocol and architectural design. Alternatively, Layer 2 solutions enable off-chain transaction processing, increasing throughput and reducing costs while maintaining the security of the base layer. Despite their advantages, Layer 2 approaches are less explored in the literature. To address this gap, this review conducts an in-depth analysis on Ethereum Layer 2 frameworks, emphasizing their integration with machine-learning techniques, with the goal of promoting the prevailing best practices and emerging applications; this review also identifies key technical and operational challenges hindering widespread adoption.
Rayhan Ferdous Srejon, M. Fahim, Sk. Md. Shadman Ifaz, Md. Kamrul Hasan ¡ 6 authors
Ride-sharing platforms have revolutionized urban mobility, offering millions of users convenient and costeffective transportation. However, mainstream centralized platforms such as Uber and Lyft continue to face pressing concerns including data privacy breaches, high service charges, security vulnerabilities, and a lack of transparency due to centralized control. To address these limitations, this research proposes a semipublic blockchain-based ride-sharing platform integrating Hyperledger Fabric for secure and permissioned data management with Ethereum smart contracts for transparent ride booking, fare calculation, and payments. The platform leverages the InterPlanetary File System (IPFS) for immutable, decentralized storage and uses the Cosmos SDK to enable seamless interoperability between public and private blockchains. A user-centric pay-as-you-drive model is introduced to ensure fair and distance-based billing. Preliminary evaluations show that our system outperforms traditional blockchain consensus methods (PoW, PoA) in throughput, latency, and resource usage. At the same time, it remains economically viable with an operational cost of under 33,000 BDT per node. Future improvements include benchmarking with Hyperledger Caliper, transitioning from Vagrant to Docker for better scalability, and implementing backend services using Node.js or Golang with MongoDB for efficient metadata handling. Together, these enhancements support a secure, decentralized, and scalable alternative to existing ride-sharing systems.
The traditional cheque clearance systems face several challenges, including processing delays, high operational costs, and a lack of transparencyâoften resulting in inefficiencies and increased risk of fraud. To address these issues, a Blockchain-Based Cheque Clearance System Using Ethereum is proposed, aiming to revolutionize the banking sector by providing a secure, transparent, and efficient mechanism for cheque transactions between banks and users. This system leverages Ethereumâs decentralized blockchain network and smart contracts to automate the cheque clearance process, ensuring faster and tamper-proof transactions. Historically, cheque clearance relied on manual or centralized procedures that were prone to errors and fraudulent activities. Traditional methods often required inter-bank coordination, causing delays and inconsistencies. Although some digitized systems were introduced over time, they remained centralized and lacked the trust and security required for critical financial operations. Inspired by the capabilities of blockchain technology, this project seeks to overcome these limitations by decentralizing the cheque clearance process. The motivation lies in the growing need for a solution that enhances security, reduces processing time, and ensures data integrity in financial transactions. By adopting blockchain, the system can provide immutable transaction records, minimize human intervention, and significantly reduce operational inefficiencies. The proposed solution uses Ethereum smart contracts to enable real-time verification, validation, and clearance of cheques. Each transaction is permanently recorded on the blockchain, ensuring transparency and auditability. The decentralized nature of Ethereum removes reliance on a central authority, thereby increasing system resilience and reliability. This innovative approach aims to redefine the cheque clearance process, offering banks and users a seamless, secure, and efficient transactional experience.
This paper proposes a cryptocurrency portfolio trading system (CPTS) that optimizes trading performance in the cryptocurrency futures market by leveraging reinforcement learning and timeframe analysis. By employing the advantage actorâcritic (A2C) algorithm and analysis of variance (ANOVA) portfolios are constructed over multiple timeframes. Data corresponding to the trade of 18 major cryptocurrencies on Binance Futuresââbetween January 2022 and December 2023ââare used to show that trading strategies can be effectively categorized into those with high-frequency (10, 30, and 60 min) and low-frequency (daily) timeframes. Empirical results demonstrate statistically significant differences in returns between these timeframe groups, with major cryptocurrencies (e.g., Bitcoin and Ethereum) exhibiting higher returns in high-frequency trading (16â17%) than in daily trading (6â7%) during training. Performance evaluation during the test period revealed that the low-frequency group achieved a 43.06% average return, significantly outperforming the high-frequency group (5.68%). The ANOVA results confirm that both the frequency type and portfolio selection significantly influence trading performance at the 5% significance level. This study offers a novel approach to cryptocurrency trading that considers the distinct characteristics of different timeframes. The effectiveness of combining reinforcement learning with statistical analysis for portfolio optimization in highly volatile cryptocurrency markets is demonstrated.
We examine how Bitcoin and Ethereum volatilities react to macroeconomic data releases from the US, Germany, and Japan before, during, and after their official announcements. Analyzing 5-minute observations from 2016 to 2023, we find that volatility responds significantly to select news categories, particularly in the pre-announcement period. US monetary policy news consistently drives volatility across all phases, with a heightened impact during the pandemic. Ethereum shows greater sensitivity to US announcements than Bitcoin but remains unresponsive to non-US news, especially before the pandemic. Our findings highlight the need to account for both pre- and post-announcement periods when evaluating the intraday price impact of macroeconomic news on cryptocurrencies. ⢠We examine the response of Bitcoin and Ethereum volatilities to macroeconomic figures. ⢠We show that volatility reacts only to a few news categories. ⢠US monetary policy news consistently affects volatility before, during, and after its release. ⢠Ethereum volatility is more sensitive to US announcements compared to Bitcoin. ⢠Ethereum exhibits less pre-announcement volatility and less sensitivity to non-US news.
The cold-chain supply of perishable fruits continues to face challenges such as fuel wastage, fragmented stakeholder coordination, and limited real-time adaptability. Traditional solutions, based on static routing and centralized control, fall short in addressing the dynamic, distributed, and secure demands of modern food supply chains. This study presents a novel end-to-end architecture that integrates multi-agent reinforcement learning (MARL), blockchain technology, and generative artificial intelligence. The system features large language model (LLM)-mediated negotiation for inter-enterprise coordination, Pareto-based reward optimization balancing spoilage, energy consumption, delivery time, and climate and emission impact. Smart contracts and Non-Fungible Token (NFT)-based traceability are deployed over a private Ethereum blockchain to ensure compliance, trust, and decentralized governance. Modular agents-trained using centralized training with decentralized execution (CTDE)-handle routing, temperature regulation, spoilage prediction, inventory, and delivery scheduling. Generative AI simulates demand variability and disruption scenarios to strengthen resilient infrastructure. Experiments demonstrate up to 50% reduction in spoilage, 35% energy savings, and 25% lower emissions. The system also cuts travel time by 30% and improves delivery reliability and fruit quality. This work offers a scalable, intelligent, and sustainable supply chain framework, especially suitable for resource-constrained or intermittently connected environments, laying the foundation for future-ready food logistics systems.
This research examines the application of Long Short-Term Memory (LSTM) neural networks for predicting cryptocurrency prices, with a focus on Bitcoin (BTC) and Ethereum (ETH), the two dominant digital assets with the highest market capitalization. The study addresses the critical challenge of accurately forecasting cryptocurrency price movements in highly volatile markets, which is essential for informed investment decision-making in the digital economy. The methodology employs LSTM models trained on historical closing price data from 2014 to 2024 for Bitcoin and from 2017 to 2024 for Ethereum, utilizing an 80:20 training-to-testing ratio. Results demonstrate exceptional predictive accuracy with R² values of 99.08% for Bitcoin and 97.44% for Ethereum, while MAPE values remained low at 1.8% and 1.9%, respectively. The study concludes that LSTM models effectively capture complex patterns in cryptocurrency price movements, providing reliable short-term forecasting capabilities and contributing valuable insights to the intersection of artificial intelligence and digital economy development.
This study investigates the impact of digital currencies (including central bank digital currencies [CBDCs], cryptocurrencies, and Ethereum) on monetary policy effectiveness, specifically focusing on inflation-targeting success and financial stability. Using Autoregressive Distributed Lag (ARDL) modelling on monthly global data spanning January 2010 to December 2024, the empirical findings demonstrate that digital currencies significantly improve monetary policy outcomes. The results indicate that CBDCs and Ethereum transactions notably enhance inflation-targeting success, enabling central banks to better achieve targeted inflation through improved transaction efficiency and transparency. Ethereum also consistently demonstrates a stabilising impact on financial stability by reducing inflation volatility. Conversely, cryptocurrencies exhibit mixed impacts, suggesting potential speculative disruptions. The error-correction mechanisms highlight robust short-run adjustments towards equilibrium, supporting the reliability of the ARDL approach. These findings emphasize the need for policymakers to strategically integrate digital currencies into monetary policy frameworks, and recommend enhanced regulatory oversight, strategic adoption of Ethereum technology, and careful management of monetary growth and velocity of money to sustain economic stability.
The Ethereum platform is booming with growing richness and variety in decentralized finance (DeFi) products. However, this progress comes with sophisticated threats, such as sandwich attacks, where attackers exploit the openness and certainty of blockchain technology to manipulate market prices and secure illegal financial rewards through a strategically planned series of transactions. The existing sandwich attack detection methods are ineffective at detecting multi-token transactions and fail to identify multi-token sandwich attacks. To tackle this challenge, this study improves the original detectorâs algorithm to identify both traditional single-token and multi-token sandwich attacks. The enhanced system is not only responsive and accurate but also capable of detecting and alerting potential multi-token sandwich attacks. It has been successfully integrated with the go-Ethereum client (Geth). The system is performance-optimized with an average processing time of 0.81 seconds per block and an accuracy rate of 96.17%. The response time for detecting new blocks in real-time is usually no more than 4 seconds, with most between 2 and 3 seconds, which meets practical application requirements. By carefully analyzing the transaction data flow, this system is not only able to identify the traditional front-running attack and sandwich attack, but also extends to multi-currency complex attack strategies. The core innovation lies in the systemâs ability to accurately detect and provide early warnings of multi-token sandwich attacks through real-time analysis of in-block transactions, all while maintaining the overall operational efficiency of the node.
<b>Abstract</b><br>Traditional electoral systems exhibit critical vulnerabilities including vote manipulation, centralized points of failure, and compromised transparency that undermine democratic integrity. This research presents BLOCKELECT, a decentralised blockchain-based secure voting system designed to address these fundamental challenges. The system employs Ethereum smart contracts written in Solidity to enforce immutable voting rules, Web3.js for blockchain integration, and MetaMask wallet authentication for secure voter verification. The proposed architecture implements dual interfaces for voters and electoral commissions, with distributed consensus mechanisms ensuring real-time transaction validation. Smart contracts automatically enforce electoral rules while maintaining cryptographic immutability of all voting transactions. The decentralised design eliminates single points of failure by distributing vote storage and validation across multiple network nodes. System validation employed comprehensive testing including unit, integration, system, and security testing methodologies. Results demonstrate successful prevention of vote tampering, elimination of double voting, and provision of transparent, auditable election results. Implementation utilised Truffle framework, Ganache blockchain simulation, and Node.js back-end services following an Agile Prototype-based Iterative Development methodology. This research demonstrates the feasibility of blockchain technology in creating trustworthy electoral systems, indicating that blockchain-based voting represents a viable solution for enhancing democratic processes while addressing persistent challenges of electoral fraud and lack of public confidence in traditional voting mechanisms.Traditional electoral systems exhibit critical vulnerabilities including vote manipulation, centralized points of failure, and compromised transparency that undermine democratic integrity. This research presents BLOCKELECT, a decentralised blockchain-based secure voting system designed to address these fundamental challenges. The system employs Ethereum smart contracts written in Solidity to enforce immutable voting rules, Web3.js for blockchain integration, and MetaMask wallet authentication for secure voter verification. The proposed architecture implements dual interfaces for voters and electoral commissions, with distributed consensus mechanisms ensuring real-time transaction validation. Smart contracts automatically enforce electoral rules while maintaining cryptographic immutability of all voting transactions. The decentralised design eliminates single points of failure by distributing vote storage and validation across multiple network nodes. System validation employed comprehensive testing including unit, integration, system, and security testing methodologies. Results demonstrate successful prevention of vote tampering, elimination of double voting, and provision of transparent, auditable election results. Implementation utilised Truffle framework, Ganache blockchain simulation, and Node.js back-end services following an Agile Prototype-based Iterative Development methodology. This research demonstrates the feasibility of blockchain technology in creating trustworthy electoral systems, indicating that blockchain-based voting represents a viable solution for enhancing democratic processes while addressing persistent challenges of electoral fraud and lack of public confidence in traditional voting mechanisms.
With the advent of quantum computing, cryptocurrencies that rely on blockchain technology face mounting cryptographic vulnerabilities. This paper presents a comprehensive literature review evaluating how quantum algorithms, specifically Shors and Grovers, could disrupt the foundational security mechanisms of cryptocurrencies. Shors algorithm poses a threat to public-key cryptographic schemes by enabling efficient factorization and discrete logarithm solving, thereby endangering digital signature systems. Grovers algorithm undermines hash-based functions, increasing the feasibility of fifty one percent attacks and hash collisions. By examining the internal mechanisms of major cryptocurrencies such as Bitcoin, Ethereum, Litecoin, Monero, and Zcash, this review identifies specific vulnerabilities in transaction and consensus processes. It further analyses the current hardware limitations of quantum systems and estimates when such attacks could become feasible. In anticipation, it investigates countermeasures including Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD), and protocol-level modifications such as memory-intensive proof-of-work algorithms and multi-signature schemes. The discussion integrates recent advancements in quantum error correction, hardware scalability, and NIST-standardized cryptographic algorithms. This review concludes that while quantum computers are not yet advanced enough to pose an immediate threat, proactive integration of quantum-resistant solutions is essential. The findings underscore the urgent need for cryptocurrencies to adopt post-quantum cryptographic standards to preserve the decentralized trust, integrity, and security that define blockchain-based digital cryptocurrencies.
Eyal Briman, Nimrod Talmon, Angela Kreitenweis, Muhammad Idrees
Abstract The Optimism Retroactive Project Funding (RetroPGF) is a key initiative within the blockchain ecosystem that retroactively rewards projects deemed valuable to the Ethereum and Optimism communities. Managed by the Optimism Collective, a decentralized autonomous organization (DAO), RetroPGF represents a large-scale experiment in decentralized governance. Funding rewards are distributed in OP tokens, the native digital currency of the ecosystem. As of this writing, four funding rounds have been completed, collectively allocating over $100M, with an additional $1.3B reserved for future rounds. However, we identify significant shortcomings in the current allocation system, underscoring the need for improved governance mechanisms given the scale of funds involved. Leveraging computational social choice techniques and insights from multiagent systems, we propose improvements to the voting process by recommending the adoption of a utilitarian moving phantoms mechanism [1]. This mechanism was originally introduced by Freeman et al. [1], is designed to enhance social welfare (using the $$\ell _1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>â</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> norm) while satisfying strategyproofnessâtwo key properties aligned with the applicationâs governance requirements. Our analysis provides a formal framework for designing improved funding mechanisms for DAOs, contributing to the broader discourse on decentralized governance and public goods allocation.