Blockchain Papers

Bin Hu, Helena Rong

As the "agentic web" takes shape-billions of AI agents (often LLM-powered) autonomously transacting and collaborating-trust shifts from human oversight to protocol design. In 2025, several inter-agent protocols crystallized this shift, including Google's Agent-to-Agent (A2A), Agent Payments Protocol (AP2), and Ethereum's ERC-8004 "Trustless Agents," yet their underlying trust assumptions remain under-examined. This paper presents a comparative study of trust models in inter-agent protocol design: Brief (self- or third-party verifiable claims), Claim (self-proclaimed capabilities and identity, e.g. AgentCard), Proof (cryptographic verification, including zero-knowledge proofs and trusted execution environment attestations), Stake (bonded collateral with slashing and insurance), Reputation (crowd feedback and graph-based trust signals), and Constraint (sandboxing and capability bounding). For each, we analyze assumptions, attack surfaces, and design trade-offs, with particular emphasis on LLM-specific fragilities-prompt injection, sycophancy/nudge-susceptibility, hallucination, deception, and misalignment-that render purely reputational or claim-only approaches brittle. Our findings indicate no single mechanism suffices. We argue for trustless-by-default architectures anchored in Proof and Stake to gate high-impact actions, augmented by Brief for identity and discovery and Reputation overlays for flexibility and social signals. We comparatively evaluate A2A, AP2, ERC-8004 and related historical variations in academic research under metrics spanning security, privacy, latency/cost, and social robustness (Sybil/collusion/whitewashing resistance). We conclude with hybrid trust model recommendations that mitigate reputation gaming and misinformed LLM behavior, and we distill actionable design guidelines for safer, interoperable, and scalable agent economies.

A. Venkata Nagarjun, R. Sujatha

Because of the rapid acceleration of cloud computing, data transfer security and intrusion detection in cloud networks have become emerging areas of concern. All traditional security mechanisms have central vulnerabilities, cannot detect real-time threats, and are ineffective against zero-day attacks. Signature-based approaches of existing intrusion detection systems (IDS) do not cover the dynamically changing nature of cyber threats. Conventional blockchain security methods suffer from poor scalability and dynamic threat analysis. Therefore, this research proposes integrating Ethereum Blockchain and Deep Learning to construct a well-founded security framework for cloud networks with data migration security and real-time intrusion detection. The architecture has five distinct methods, each of which deals with particular security issues. Blockchain-Aware Federated Learning for Secure Model Training (BAFL SMT) guarantees tamper-proof and decentralized deep learning model training, which reduces model poisoning attacks by 98.4%. Graph Neural Networks for Adaptive Intrusion Detection (GNN-AID) captures graph structures for real-time anomaly detection in networks while reducing false positives to 1.2%. Quantum-inspired Variational Autoencoders (QI VAE ZDAD) provide enhanced zero-day attack detection, with an improved detection rate of 92%. Self-Supervised Contrastive Learning for Blockchain Security Auditing (SSCL-BSA) detects smart contract vulnerabilities automatically, resulting in an 87% reduction in fraud risk. Finally, Hierarchical Transformers for Secure Data Migration (HT SDM) enhance the transfer security of large-scale cloud data, achieving an attack classification accuracy of 99.1%. Overall, this multi-layer security framework will greatly enhance cloud security by preserving data integrity, cutting down the intrusion detection time by up to 65%, and enhancing response mechanisms. By marrying the immutable transparency of blockchain with superior anomaly detection at deep learning, this research provides a scalable, real-time, and intelligent approach to strengthening security against the backed-up transfer of data within cloud networks.

Patan Mushiya Katoon, Anil V. Turukmane

Electronic Health Records (EHR) is the main core of modern healthcare, but interoperability across different blockchain platforms is a key challenge. This work proposes a cross-chain middleware architecture, which facilitates secure and real-time synchronization of EHR data between Hyperledger Fabric (private blockchain) and Ethereum Sepolia Testnet (public blockchain). The framework integrates AES-256 encryption and Inter Planetary File System (IPFS) as decentralized storage to enhance patient privacy. To facilitate interoperability across the blockchains the research introduces a smart middleware layer. This layer autonomously monitors the blockchain events, processes encrypted CIDs, enforces real time cross chain consistency and smart contract-based access control. The experimental evaluation shows that proposed framework achieves low synchronization times (< 195 ms), low gas and latency costs, small encryption overhead (< 4–5 KB), robust file storage and retrieval through IPFS. Such positive evaluations with scalable and real-time deployment, sets the foundation of patient centric interoperable healthcare ecosystems.

Lumengo Bonga‐Bonga, Muhammad Khalique

This paper investigates the relationship between cryptocurrencies and other financial assets, with a particular focus on the dynamics of information flow between developed and emerging markets. To achieve this objective, the study applies a combined methodology of spillover index analysis and network topology based on graph theory. The analysis covers key cryptocurrencies (Bitcoin and Ethereum), stocks, and conventional currencies over the period November 2017 to September 2022, and distinguishes between short-term and long-run dynamics. The empirical findings show that in the short run, Bitcoin and Ethereum predominantly act as net shock transmitters, whereas in the long run, stocks and conventional currencies, together with Bitcoin and Ethereum, become the principal conveyors of spillover shocks. The network topology analysis corroborates these results by revealing the centrality of these assets in the spillover structure. By integrating spillover and network approaches across different markets and time horizons, this study contributes to the literature by providing a more nuanced understanding of how cryptocurrencies interact with traditional financial assets under varying market conditions.

Shashank Motepalli, N. Garg, Gengrui Zhang, Hans‐Arno Jacobsen

Geospatial decentralization is essential for blockchains, ensuring regulatory resilience, robustness, and fairness. We empirically analyze five major Proof of Stake (PoS) blockchains: Aptos, Avalanche, Ethereum, Solana, and Sui, revealing that a few geographic regions dominate consensus voting power, resulting in limited geospatial decentralization. To address this, we propose Geospatially aware Proof of Stake (GPoS), which integrates geospatial diversity with stake-based voting power. Experimental evaluation demonstrates an average 45% improvement in geospatial decentralization, as measured by the Gini coefficient of Eigenvector centrality, while incurring minimal performance overhead in BFT protocols, including HotStuff and CometBFT. These results demonstrate that GPoS can improve geospatial decentralization {while, in our experiments, incurring minimal overhead} to consensus performance.

Abhirup Khanna, Divya Srivastava, Anushree Sah, Sarishma Dangi · 8 authors

The increasing complexity of urban energy systems requires decentralized, sustainable, and scalable solutions. The paper presents a new multi-layered framework for smart energy management in microgrids by bringing together advanced forecasting, decentralized decision-making, evolutionary optimization and blockchain-based coordination. Unlike previous research addressing these components separately, the proposed architecture combines five interdependent layers that include forecasting, decision-making, optimization, sustainability modeling, and blockchain implementation. A key innovation is the use of Temporal Fusion Transformer (TFT) for interpretable multi-horizon forecasting of energy demand, renewable generation, and electric vehicle (EV) availability which outperforms conventional LSTM, GRU and RNN models. Another novelty is the hybridization of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), to simultaneously support discrete and continuous decision variables, allowing for dynamic pricing, efficient energy dispatching and adaptive EV scheduling. Multi-Agent Reinforcement Learning (MARL) which is improved by sustainability shaping by including carbon intensity, renewable utilization ratio, peak to average load ratio and net present value in agent rewards. Finally, Ethereum-based smart contracts add another unique contribution by providing the implementation of transparent and tamper-proof peer-to-peer energy trading and automated sustainability incentives. The proposed framework strengthens resilient infrastructure through decentralized coordination and intelligent optimization while contributing to climate mitigation by reducing carbon intensity and enhancing renewable integration. Experimental results demonstrate that the proposed framework achieves a 14.6% reduction in carbon intensity, a 12.3% increase in renewable utilization ratio, and a 9.7% improvement in peak-to-average load ratio compared with baseline models. The TFT-based forecasting model achieves RMSE = 0.041 kWh and MAE = 0.032 kWh, outperforming LSTM and GRU by 11% and 8%, respectively.

List, Michael

Ethereum ist seit Jahren die größte Smart-Contract-Blockchain und nach Bitcoin die zweitgrößte Blockchain-Plattform. Smart-Contracts, die als dezentrale Anwendungen beschrieben werden können, laufen auf einer gemeinsamen Rechenplattform, auf der alle Teilnehmer auf einer geteilten Codebasis arbeiten. Zur Absicherung ist es nötig, dass ein Konsens über die Ein- und Ausgaben aller Smart-Contracts geschaffen wird. Die Ausführung von Smart-Contract-Code verbraucht sogenannte Gas-Einheiten, die als eine Art Treibstoff betrachtet werden können. Gas-Einheiten zeigen den erforderlichen Rechenaufwand an und haben direkte Auswirkungen auf den realen Energieverbrauch. Daher sollten idealerweise alle Smart-Contracts so implementiert sein, dass sie möglichst wenig Gas-Einheiten verbrauchen. Derartige Codeoptimierungsansätze sind nicht trivial. Zum Zeitpunkt des Verfassens dieser Diplomarbeit gibt es bereits solche Mechanismen, welche teilweise direkt in den gängigen Compilern integriert sind. Solche Mechanismen basieren in der Regel auf festen Mustern, welche manuell beschrieben werden müssen und dann auf Smart-Contracts angewendet werden können. In dieser Arbeit haben wir untersucht, ob klassische Verfahren zur Erkennung von Codeähnlichkeiten verwendet werden können, um Optimierungsmuster automatisch aus Quellcode-Repositories ableiten zu können. Zunächst haben wir einen Symbolic-Execution-Ansatz untersucht, welcher sich aufgrund von technischen Einschränkungen und der Abhängigkeit von veralteten Compiler-Versionen als ungeeignet erwies. Daraufhin haben wir einen Fingerprinting-Ansatz basierend auf Kontrollflussgraph-Blöcken gewählt. Mithilfe von Slither konnten wir Metriken wie Cyclomatic-Complexity, Fan-Out und Informationsfluss-Metriken extrahieren und anschließend Distanzen zwischen Codestücken berechnen, um mit den Ergebnissen potenzielle semantische Code-Klone zu erkennen. Wir haben die Evaluierung unseres Ansatzes auf 1.200 manuell markierten Smart-Contracts aus einem Datensatz mit 160.000 Einträgen durchgeführt, was zu 574 Vergleichen führte und konnten eine korrigierte Genauigkeit von 88% für die Erkennung von semantischen Code-Äquivalenzen auf Blockebene erzielen. Für 1.300 Code-Paare haben wir zusätzlich eine Gasverbrauchsmessung durchgeführt, indem wir die Blöcke in generierte Smart-Contracts verpackt und auf einer lokalen Blockchain ausgeführt haben. Dabei konnten wir tatsächliche gasreduzierende Codeänderungen identifizieren. Trotz einiger wesentlichen Einschränkungen zeigt das, dass das Mining gasoptimiertem Codes aus versionierten Source-Code-Repositories mittels Code-Metriken möglich ist.

Mrs. Vaishali Aniket Wanarkar

The rapid evolution of blockchain technology has revolutionized digital asset ownership through NonFungible Tokens (NFTs). NFTs enable creators to tokenize unique digital assets such as art, music, and collectibles, ensuring authenticity, transparency, and verifiable ownership. This research paper presents the design and development of a decentralized NFT Marketplace using Solidity Smart Contracts and Pinata IPFS (InterPlanetary File System) integration. The proposed system eliminates the need for intermediaries by leveraging blockchain-based automation, allowing creators to mint, list, and sell NFTs securely while maintaining full ownership control. The marketplace integrates MetaMask wallet authentication for secure transactions and employs Solidity smart contracts to handle NFT minting, transfer, and royalty distribution on the Ethereum blockchain. Additionally, Pinata IPFS provides decentralized storage for digital media and metadata, ensuring data permanence and tamper-proof accessibility. The system architecture combines transparency, security, and user-friendliness, empowering creators with fair compensation and buyers with verifiable proof of ownership. Experimental implementation results demonstrate that the proposed NFT Marketplace provides a reliable, transparent, and scalable environment for digital asset exchange. This study highlights the potential of decentralized systems in reshaping the digital economy and sets the foundation for future enhancements such as multi-chain support, AI-based recommendations, and mobile integration.

Ade Indriawan, Nur Aini Rakhmawati

The rise of non-fungible tokens (NFTs) has increased the risk of fraud and market manipulation. This study introduces a method for detecting wash trading in the NFT marketplace using Graph Neural Networks (GNNs) applied to Ethereum blockchain transaction data. We constructed a heterogeneous graph, used Depth-First Search for labelling, and extracted graph features, including PageRank and degree centrality. We evaluate various classification models: Multilayer Perceptron (MLP), Graph Convolutional Neural Network (GCN), and Heterogeneous Graph Convolutional Neural Network (HeteroGCN). The results show that GNN models, particularly the feature-enhanced HeteroGCN, exhibit superior performance compared to featureless models and traditional tabular baselines. The key contribution of this study is that PageRank and Degree Centrality features significantly improve the accuracy of identifying transactions involved in market manipulation.

Fiqri Khalid Aziz, Rizal Rachman

Tingginya risiko manipulasi data dan lemahnya sistem autentikasi tradisional menimbulkan tantangan serius dalam menjaga keamanan identitas digital. Dampak dari permasalahan ini adalah meningkatnya potensi pencurian data, penyalahgunaan identitas, serta rendahnya tingkat kepercayaan terhadap sistem keamanan digital yang ada. Untuk menjawab tantangan tersebut, penelitian ini mengembangkan sistem autentikasi wajah berbasis blockchain yang aman, transparan, dan terdesentralisasi. Sistem dirancang dengan memanfaatkan algoritma SHA-256 untuk mengubah data wajah menjadi hash yang tidak dapat dibalik, sehingga menjaga privasi dan integritas data pengguna. Informasi identitas dicatat ke dalam blockchain melalui smart contract berbasis Ethereum yang dijalankan menggunakan Ganache, dengan bahasa pemrograman Python dan pustaka face_recognition sebagai deteksi wajah serta web3.py untuk integrasi blockchain. Hasil penelitian menunjukkan sistem mampu mengenali wajah secara akurat, memverifikasi identitas secara real-time, serta memastikan data tidak dapat dimanipulasi karena tercatat dalam blockchain. Sistem ini dapat dimanfaatkan oleh institusi pendidikan, perusahaan, maupun instansi pemerintah untuk meningkatkan keamanan akses, sistem absensi, serta perlindungan data sensitif. Penelitian selanjutnya dapat diarahkan pada implementasi di jaringan testnet Ethereum publik agar mendekati skenario dunia nyata, termasuk pengujian biaya gas, performa transaksi, serta integrasi dengan aplikasi berbasis mobile.

Prof. Madhavi Bhosale, Abhishek Kangude, Vedant Khandare, Sunil Kajave

Abstract In recent years, advancements in blockchain technology have paved the way for creating transparent, secure, and decentralized digital ecosystems. This paper presents a blockchain-based electronic voting (e-voting) system designed to overcome the limitations of traditional and centralized electronic voting methods. The proposed system integrates Solidity-based smart contracts, a Python middleware API using Web3.py, and a Flutter frontend to create a secure, verifiable, and user-friendly voting platform. The architecture ensures voter anonymity, immutability of votes, and real-time result verification through blockchain’s decentralized ledger. The system employs MetaMask for voter authentication, enabling a one-person-one-vote mechanism and eliminating centralized control or tampering risks. Experimental simulations using Ganache demonstrate efficient transaction processing, transparent result computation, and tamper-proof data storage. The proposed solution enhances security, transparency, and trust in digital elections and serves as a foundation for scalable, real-world implementations in organizational, academic, and governmental voting scenarios. This research contributes toward developing next-generation decentralized voting infrastructures that reinforce democratic integrity and public confidence in electoral processes. · Keywords : Blockchain Technology; E-Voting System; Smart Contracts; Decentralized Applications (DApps); Solidity; Ethereum; Python Web3.py; Flutter Frontend; MetaMask Authentication; Digital Elections; Voter Privacy; Transparency; Immutability; Secure Voting; Electronic Governance

Moravvej Hamedani, Motahhareh

This thesis examines three distinct topics on settlement microstructure and market efficiency in both DeFi and TradFi. Chapter one introduces the thesis’ unifying lens, arguing that settlement microstructure drives market efficiency across these markets. It links the three papers by showing how access in Bitcoin private channels, timing in Ethereum intertemporal gas hedging, and composition in equity market retail participation jointly determine fees, latency, liquidity, and price discovery, while previewing the policy framework that renders these mechanisms legible, bounded, and measurable. Chapter two, based on the working paper “Private Settlement in Blockchain Systems” with Dr. Alfred Lehar, provides evidence that the settlement market in blockchain systems is not purely transactional and diverges from the predictions of a simple competitive auction model. Using data from the Bitcoin blockchain, we find that 5.88% of transactions, labeled as private, bypass the competitive auction and are routed directly to miners. Despite being more active than the average user, these transactions are consistently confirmed by a single miner, a statistically unlikely outcome in a competitive environment. Our findings suggest that high-demand users form long-term agreements with miners, paying, on average, 20% lower fees. This chapter also documents how such settlement contracts are structured and operate within an unregulated market. Chapter three, based on the working paper “Gas Tokens: Market for Future Settlement in the Ethereum Blockchain” with Dr. Alfred Lehar, examines the implications of gas tokens as a potential market for future settlement within the Ethereum network. We show that sophisticated and frequent users are more engaged in gas token markets, pre-purchasing tokens to hedge against fluctuations in gas prices and paying, on average, 15.25% lower settlement fees. Moreover, bots actively pursue arbitrage opportunities in gas token markets and hold substantial volumes. Our findings indicate that traded gas token prices have strong predictive power for future gas prices. This research contributes to the development of modern financial instruments for price discovery and hedging within the Ethereum network as a two-sided market. We also empirically analyze the implementation of the Ethereum Improvement Proposal EIP-1559 as a natural experiment. Chapter four, based on my working paper “Silencing the Noise: Amplified Effects, A Causal Study on Price Efficiency”, investigates the causal effects of noise trader removal on market liquidity. In September 2022, an unexpected internet disruption in Iran restricted noise traders while informed traders retained access through brokers. This disruption led to a 6.65-fold increase in the bid-ask spread and a 46.8% decrease in informed trade speed due to market access asymmetry. Social media censorship in affected regions further amplified information asymmetry, resulting in a 7.2% price impact. Using a five-year analysis of political unrest, this study disentangles the effects of unrest and internet disruption on noise trading activity. The findings reveal that political unrest increases regional noise trading activity, whereas internet disruption decreases it. When both unrest and internet disruption occur simultaneously, regional noise trading activity decreases by 23.5%. This paper provides novel insights into market microstructure and the dynamics of liquidity provision through noise trading in emerging markets.

Melanie Cao, Andy Hou

This paper presents the first rigorous empirical investigation into a fundamental question of cryptocurrency valuation: Are cryptocurrency prices in line with the prices of fundamental assets? To answer this, we analyze the nine largest cryptocurrencies by market capitalization—Bitcoin (BTC), Ethereum (ETH), Solana (SOL), Binance Coin (BNB), Ripple (XRP), Cardano (ADA), Litecoin (LTC), Tron (TRX), and the stablecoin DAI—against a suite of traditional benchmarks, including major fiat currencies (EUR, CAD, JPY), gold, and the S&amp;P500 index. Our dataset spans from 1 January 2014 to 30 June 2025, with start dates varying for newer cryptocurrencies to ensure robust time series analysis. Guided by the asset pricing theory, we formulate a martingale test: if a cryptocurrency is priced in line with a fundamental numeraire asset, its price ratio relative to that numeraire must follow a martingale process. Our extensive empirical analysis reveals that the prices of major cryptocurrencies (BTC, ETH, SOL, BNB) consistently reject the martingale hypothesis when traditional assets (currencies, gold, equities) serve as the numeraire, indicating a decoupling from fundamental valuation anchors. Conversely, when Bitcoin or Ethereum itself is used as the numeraire, most smaller cryptocurrencies are priced in line with these crypto benchmarks, suggesting an internal valuation ecosystem that operates independently of traditional finance.

Andrei Arusoaie, Claudiu-Nicu Bărbieru, Oana-Otilia Captarencu, Paul-Flavian Diac · 6 authors

Ethereum is currently the main blockchain ecosystem providing decentralised trust guarantees for applications ranging from finance to e-government. A common criticism of blockchain networks has been their energy consumption and operational costs. The switch from Proof-of-Work (PoW) protocol to Proof-of-Stake (PoS) protocol has significantly reduced this issue, though concerns remain, especially with network expansions via additional layers. The ERC-4337 standard is a recent proposal that facilitates end-user access to Ethereum-backed applications. It introduces a middleware called a bundler, operated as a third-party service, where part of its operational cost is represented by its power consumption. While bundlers have served over 500 million requests in the past two years, fewer than 15 official bundler providers exist, compared to over 100 regular Ethereum access providers. In this paper, we provide a first look at the active power consumption overhead that a bundler would add to an Ethereum access service. Using SmartWatts, a monitoring system leveraging Running Average Power Limit (RAPL) hardware interfaces, we empirically determine correlations between the bundler workload and its active power consumption.

Asheshemi Nelson Oghenekevwe, Okoro Akpohrobaro Daniel, Ayeh Blessing Elohor, Ayo Michael Ifioko · 6 authors

Developments of Web 3.0 technologies present vital problems regarding data confidentiality, authentication of users and their privacy in decentralised systems. The traditional multifactor authentication (MFA) systems have been effective when deployed in Web2 environments but have failed in protecting sensitive information in the decentralised environment because they use centralised servers and are also dependent on static security factors. The paper explores the concept of multifactor authentication that is based on blockchain technology as the effective method of improving the use of data confidentiality in Web3. A blockchain-augmented MFA infrastructure was created on the basis of an Ethereum smart contract, decentralised storage, and biometric data that were cryptographically encrypted. Simulation demonstrated significant increases in security relative to conventional MFA systems, a significant drop in the probability of breaching (0.0270 to 0.0040), an improvement in the entropies, a decrease in the likelihood of session hijacking, and limited mutual information leakage. Also, the blockchain-based system becomes more resistant to Man-in-the-Middle (MITM) and phishing attacks, mitigating them by about 60 per cent and 50 per cent success rates, respectively. Whereas the blockchain MFA made some minor sacrifices in latency and computation cost in the course of authentication, such a trade of costs is productive in the Web3 environment where security and data integrity remain of utmost importance. The study could be useful to developers, security practitioners and policymakers who intend to develop more secure, scalable, and user-centric authentication mechanisms in decentralised apps. As a potential improvement, it is suggested that future research should implement the aspect of consensus optimisation and Layer-2 to increase the efficiency and scalability further.

Daniel Ferreira

ABSTRACT Research Question/Issue Blockchain technology promises to revolutionize governance through strong commitments, trustlessness, and transparency. This paper examines how these promises have failed to materialize in practice. Research Findings/Insights Drawing on case evidence from major blockchains, including Bitcoin and Ethereum, I argue that blockchains have evolved into technocracies where developers, foundations, and companies exercise disproportionate control. Rather than being exceptional, blockchain governance suffers from the same coordination problems, collective action failures, and centralization tendencies that plague traditional governance systems. Theoretical/Academic Implications The paper concludes that while blockchains offer valuable experiments in governance design, their alleged advantages over traditional institutions remain largely mythical. Practitioner/Policy Implications Blockchain organizations should acknowledge their reliance on off‐chain coordination and informal authority. Investors must understand that blockchain governance depends on trusting technical elites, while regulators should recognize that decentralization claims often mask concentrated power structures requiring traditional oversight.

Akmam Majed Mosa

Decentralized finance (DeFi) protocols are becoming increasingly targeted by cyber threats, such as liquidity drain attacks, smart contracts flaws that leverage instant loans, and increasingly sophisticated threats that include DarkGate ransomware. We develop a hybrid framework that integrates CTI and predictive analytics to facilitate improving consensus mechanisms in a blockchain network. The proposed framework is centered on three layers , a data collection and processing layer, a security oracle layer that engages to mitigate intervention, and a dynamic adaptive mechanism to reach consensus. A 250-node testbed was built and deployed with the Hyperledger Besu and Geth deployments of Ethereum incorporating hybrid GRU-BiLSTM which utilize GNN's for predicting attacks. The results reveal improvements of transaction processing TPS of up to +236%, settlement latency improved -75%, fork rate improved to less than 3%, and downtime improved from 15% to 1.5%. Statistical tests T-Test and ANOVA also reveal these were of high statistically significance at p &lt; 0.01. This study emphasizes that bridging functional aspects of AI with adaptive consensus mechanisms will be an effective approach at combating advanced cyber-attacks while maintaining reliability and resilience in DeFi systems.

Bowen He, Yufeng Hu, Zhuo Chen, Yuan Chen · 8 authors

The prosperity of Ethereum gives rise to a new type of transaction-based phishing scam. Specifically, users are tempted to visit phishing websites and sign phishing transactions that allow scammers to withdraw their tokens. Meanwhile, to accelerate the deployment of phishing websites, scammers have introduced a business model, Drainer-as-a-Service (DaaS). In this model, drainer operators focus on crafting specialized phishing toolkits, named ''wallet drainers'', while drainer affiliates handle the deployment and promotion of phishing websites. After stealing victims' tokens, they will distribute profits. In this paper, we present the first systematic study of DaaS on Ethereum. To begin with, we propose a snowball sampling approach to build the first large-scale DaaS dataset, including 1,910 profit sharing contracts, 56 operator accounts, 6,087 affiliate accounts, and 87,077 profit-sharing transactions. Then, we analyze the scale of DaaS from the perspectives of victims, operators, and affiliates, and perform clustering analysis to uncover dominant DaaS families. Finally, we reported DaaS accounts in the dataset and 32,819 phishing websites deployed with DaaS toolkits to the community. Our work aims to serve as a guide for Ethereum service providers to enhance user protection against DaaS.

Khalid Al‐Khatib, Fares Ayoub, Qais Marji

No abstract is available for this record.

Liviu Ionut Epure

This project presents QoreChain, a novel Layer 1 blockchain architecture that addresses two critical challenges facing distributed ledger technology: vulnerability to quantum computing attacks and inefficient network resource allocation. As cryptographically relevant quantum computers are projected to emerge within 5-10 years, current blockchain infrastructures relying on elliptic curve cryptography face existential security threats. Simultaneously, existing networks struggle with scalability, cross-chain interoperability, and intelligent resource optimization. QoreChain introduces a quantum-native security architecture implementing ML-KEM (Kyber-1024) for post-quantum key exchange with migration pathways to Dilithium and Falcon signatures. Our hybrid classical-PQC bridge protocol enables seamless cryptographic migration without network disruption while maintaining backward compatibility—a capability absent in current blockchain platforms. Beyond quantum resistance, QoreChain integrates artificial intelligence at the protocol level through an Adaptive Intelligence Layer that performs dynamic transaction routing, predictive resource allocation, and cognitive consensus optimization, achieving demonstrable performance improvements: 5,914+ transactions per second with sub-second finality, 40% reduction in finality times during peak loads, and 60% reduction in cross-chain swap slippage through AI-driven liquidity positioning. The architecture comprises three synergistic innovations: (1) a multi-layer scalability framework with AI-driven chain selection routing transactions across main chain, sidechains, and paychains based on value and computational requirements; (2) the QoreChain Consensus Algorithm (QCA) extending Combined Proof of Stake with reputation-weighted validator selection and temporal consensus layering enabling parallel consensus sessions across different time horizons; and (3) comprehensive developer tooling including natural language smart contract generation with cross-chain compilation, automated vulnerability detection using predictive AI, and voice-first accessibility features. We demonstrate QoreChain's practical applicability through integration specifications for enterprise environments (financial services, defense, healthcare), IoT deployments with hardware-optimized lightweight cryptography for resource-constrained devices, and universal cross-chain connectivity supporting Ethereum, Solana, TON, BSC, Avalanche, and Cosmos ecosystems via IBC, LayerZero, and proprietary protocols. Performance benchmarks, security proofs, and economic sustainability models validate QoreChain's viability as future-proof blockchain infrastructure for the post-quantum era.Abstract content goes here

Stefanos Leonardos, Daniël Reijsbergen, Barnabé Monnot, Georgios Piliouras

Transaction fee mechanisms are pivotal elements of blockchain economies, as they resolve the inherent scarcity in the number of transactions that can be added to each block. First-price auction mechanisms implemented by early blockchain protocols, however, contributed to pronounced intra-block disparities, unpredictable waiting times, high congestion, and other inefficiencies. To mitigate these effects, alternative fee market mechanism has been proposed, e.g., Ethereum’s EIP-1559. In this article, we investigate the ramifications of EIP-1559 on system performance and user experience. Although we prove that EIP-1559 exhibits chaotic behavior even under optimal conditions, we demonstrate that the influence of this chaotic behavior on the primary design objective of the fee mechanism—blocks whose long-term average size equals the target—is limited. Our theoretical bound shows that block sizes in the EIP-1559 mechanism are lower bounded by target utilization—half-full blocks—and the upper bound is capped at 6% beyond the target. These findings are confirmed by an empirical evaluation that shows that the average discrepancy has been 2.9% under Proof-of-Work and decreases to around 1% or less following Ethereum’s transition to Proof-of-Stake. However, the chaotic oscillations in block sizes and the slow adjustments during periods of demand bursts (e.g., NFT drops) result in undesirable inter-block variations in mining rewards and compromise the overall user experience. To address these issues, we propose an alternative base fee adjustment rule, characterized by a learning rate that adapts according to an additive increase, multiplicative decrease (AIMD) update scheme. Our data-driven simulations show that the latter robustly outperforms the EIP-1559 protocol across various demand scenarios.

Jotaro Yano

We formalize a cross-domain "ZK coprocessor bridge" that lets Solana programs request private execution on Aztec L2 (via Ethereum) using Wormhole Verifiable Action Approvals (VAAs) as authenticated transport. The system comprises: (i) a Solana program that posts messages to Wormhole Core with explicit finality; (ii) an EVM Portal that verifies VAAs, enforces a replay lock, parses a bound payload secretHash||m from the attested VAA, derives a domain-separated field commitment, and enqueues an L1->L2 message into the Aztec Inbox (our reference implementation v0.1.0 currently uses consumeWithSecret(vaa, secretHash); we provide migration guidance to the payload-bound interface); (iii) a minimal Aztec contract that consumes the message privately; and (iv) an off-chain relayer that ferries VAAs and can record receipts on Solana. We present state machines, message formats, and proof sketches for replay-safety, origin authenticity, finality alignment, parameter binding (no relayer front-running of Aztec parameters), privacy, idempotence, and liveness. Finally, we include a concise Reproducibility note with pinned versions and artifacts to replicate a public testnet run.

Rabia Arshad, Muhammad Milhan Afzal Khan, Saman Rasheed, Irtaza Ijaz · 5 authors

Blockchain technology has transformed decentralized data exchange and digital payments but the consistently high gas prices pose a significant challenge to its scalability and efficiency. This research explores the role of AI-driven gas price prediction and data compression methods on gas utilization in blockchain systems with special emphasis on Ethereum transactions. Using actual Ethereum transaction history, we compare the performance of compressed versus uncompressed payloads with three different compression algorithms: Zlib, Brotli, and Gzip. Beyond that, a linear regression model is also trained to forecast hourly gas Price fluctuations given past transaction history. The methodology includes thorough statistical analysis to provide accurate and reproducible results. Our results show that compressing text data over 141 bytes using the Zlib algorithm prior to making transactions on the Ethereum network decreases the amount of gas Used without altering system time. This validates the efficiency of combining data compression with gas price forecasting in minimizing transaction costs without affecting performance. Moreover, our study further encompasses investigation of actual gas Price trends and provides real-world insights for optimizing timing strategies for economic transaction execution. These results enhance the knowledge of Ethereum gas dynamics and provide valuable solutions for enhancing economic efficiency and resource utilization in applications based on blockchain. Future efforts will involve applying the framework to the Ethereum mainnet, using deep learning models for increased prediction accuracy, and adaptive compression dependent on network state and transaction size.

Idowu Olugbenga Adewumi

No abstract is available for this record.