The complete codebase and supplementary materials for this study have been archived on Figshare to ensure full reproducibility and to facilitate adoption by other researchers and practitioners. The archive includes all Python scripts used for data preprocessing, model training, hyperparameter tuning, threshold optimisation, and SHAP explainability analysis. Also included are the processed CSV files used for the analysis, along with all figures and tables presented in this paper. The repository is organised to enable straightforward replication of the experiments and adaptation of the framework to other datasets or blockchain platforms.
Our study examines the impact of climate policy uncertainty on the volatility of Bitcoin, Ethereum and Litecoin. Using monthly Climate Policy Uncertainty Index data from 2010 to 2024, we forecast daily cryptocurrency volatility with a GARCH-MIDAS model. The results show that higher climate policy uncertainty significantly increases volatility across all three cryptocurrencies over the full sample period. Out-of-sample analysis, which captures structural changes in energy consumption, reveals stronger effects for Bitcoin. Ethereum shows insignificant responses following its transition to a proof-of-stake mechanism, while Litecoin exhibits a significant positive relationship with uncertainty. Overall, climate policy uncertainty proves to be a strong predictor of cryptocurrency volatility, particularly for energy-intensive assets. The findings highlight the importance of policy-related information in shaping investor behaviour in crypto markets and provide useful implications for cryptocurrency issuers, retail investors and portfolio managers seeking to manage risk under changing regulatory and environmental conditions.
ASEGUNLOLUWA E. BABALOLA, DAVID O. ILESANMI, PREYE ADEOLA
Electronic voting can improve the speed of ballot processing and result generation, but conventional systems often depend on centrally controlled infrastructure that may create concerns relating to record alteration, transparency and administrative control. This study presents the development of a blockchain based electronic voting prototype that integrates election creation, candidate management, voter address authorization, ballot submission and result retrieval within a web application. The system adopts an Ethereum based architecture comprising a Next.js user interface, Web3 communication, MetaMask wallet connection, Solidity smart contracts and a local blockchain environment provided by Ganache. A factory smart contract is used to create separate election contracts, enabling each election to maintain its own candidates, authorized voter addresses, election status and vote totals. Before a ballot is accepted, the relevant election contract verifies that the election is active, that the submitting address is authorized and that the address has not previously voted. The developed prototype provides interfaces for election creation, voting and result presentation, demonstrating the integration of the web application with the smart contract and blockchain components. The study provides a basis for the independent management of multiple elections through separate smart contract instances.
The Criminal Evidence Management System using Blockchain is designed to provide a secure, transparent, and tamper-resistant platform for managing digital criminal evidence throughout its lifecycle.Traditional evidence management systems rely on centralized databases, making them vulnerable to unauthorized access, data manipulation, and single points of failure.Such limitations can compromise the integrity of evidence and weaken the chain of custody during legal proceedings.To address these challenges, the proposed system leverages blockchain technology to ensure the authenticity, immutability, and traceability of digital evidence.The system employs Ethereum blockchain and Solidity smart contracts to securely record evidence-related transactions, while Python, Django, and Web3 facilitate seamless interaction between users and the blockchain network.Role-based access control enables administrators and investigating officers to perform authorized operations such as evidence submission, retrieval, and verification.Every transaction is permanently recorded on the blockchain, creating an auditable history that enhances accountability and prevents unauthorized modifications.The proposed solution improves the reliability and efficiency of evidence management by eliminating the risks associated with centralized storage and manual record-keeping.Through secure storage, transparent access, and automated verification, the system strengthens the chain of custody, increases trust among law enforcement agencies, and supports the admissibility of digital evidence in judicial processes, making it a robust solution for modern forensic investigations.
Hai Liang, Xiaoye Lu, Changsong Yang, Yujue Wang · 6 authors
Smart contracts are immutable programs that automatically execute predefined logic. Once deployed, their underlying vulnerabilities are notoriously difficult to patch and highly susceptible to malicious exploitation, often leading to severe financial losses. Although existing vulnerability detection methods have demonstrated certain advantages, they still fail to achieve adequate structural–semantic coverage of vulnerability-relevant behaviors, as they are unable to jointly model opcode semantics, control-flow transitions, and data-dependency relations. To overcome these limitations, this paper proposes a novel smart contract vulnerability detection model named Cross-aligned Penetrative Graph Network (CPGNet). Specifically, CPGNet first constructs control flow graphs and data flow graphs from the abstract syntax tree, and combines them with opcode semantic embeddings to form a multidimensional initial code representation. Based on this representation, a cross-alignment mechanism is introduced to effectively capture and integrate the complex interactions between control-flow transitions and data-flow dependencies. Furthermore, an explicit–implicit feature penetration architecture is designed to inject shallow local opcode patterns into the deep semantic modeling process, enabling multi-source features to dynamically complement each other. By jointly modeling opcode semantics, control-flow structures, and data-dependency relations, CPGNet significantly enhances the representation capability for hidden and complex vulnerability patterns. Experimental results on two datasets show that CPGNet achieves stable performance, with F1-scores of 88.69% and 90.58% on the benchmark Ethereum dataset, and 78.10% and 71.53% on DIVE for reentrancy and timestamp dependency detection, respectively. These results verify the effectiveness of jointly modeling opcode semantics and graph-level structural dependencies.
Ethereum sebagai platform smart contract banyak digunakan untuk dApps, namun Ethereum (Layer 1) masih menghadapi kendala skalabilitas ketika aktivitas transaksi meningkat, yang berdampak pada kemacetan jaringan, naiknya latensi, dan biaya transaksi yang tinggi. Makalah ini menyajikan kajian literatur yang berfokus pada rollups sebagai solusi utama Ethereum Layer 2 untuk mengurangi beban pemrosesan transaksi pada Layer 1. Metode yang digunakan adalah literature review terarah dengan sintesis tematik terhadap literatur yang membahas: sumber masalah skalabilitas di Ethereum Layer 1, konsep dan arsitektur rollups, perbedaan Optimistic Rollups dan Zero-Knowledge Rollups, serta tantangan implementasi rollups pada kondisi nyata. Hasil kajian menunjukkan bahwa rollups menjadi pendekatan dominan karena menggabungkan transaksi dalam batch untuk diproses di Layer 2, lalu melaporkan ringkasan/bukti ke Layer 1 sehingga efisiensi biaya dan kapasitas transaksi meningkat. Namun, efektivitas rollups bergantung pada aspek operasional dan keamanan seperti ketersediaan data, mekanisme penarikan dana, serta risiko ketergantungan pada operator/penyedia infrastruktur. Secara keseluruhan, rollups efektif sebagai solusi skalabilitas Ethereum, tetapi memerlukan penguatan desain keamanan dan keandalan operasional agar dapat diadopsi lebih luas.
Smart contracts underpin a wide range of decentralized applications—from financial services to supply-chain management—but their immutability and direct control of assets magnify the impact of any security bugs. Although many fuzz approaches have been proposed and have demonstrated their effectiveness in uncovering vulnerabilities, existing methods often rely on unguided random mutation scheduling, generate redundant inputs, and fail to adapt to smart contract-specific characteristics. To overcome these challenges, we present FuzzMaster, a feedback-driven fuzzing framework that combines deep reinforcement learning (DRL) with lightweight probabilistic scheduling to steer mutation selection at runtime intelligently. By continuously analyzing execution feedback—code coverage, function-call sequences, and vulnerability signals—FuzzMaster’s DRL agent and probabilistic tables prioritize high-impact mutations and avoid wasted effort on redundant seeds. On standard VeriSmart and SmartBugs benchmarks, FuzzMaster achieves a 66.2% detection rate with 100% precision (versus 46.9% for ItyFuzz and 43.1% for Confuzzius) and uncovers most bugs within the first second of execution. Meanwhile, in real-world Ethereum contracts, FuzzMaster identified 97 vulnerabilities in 6 categories. These results demonstrate that dynamic, vulnerability-aware mutation scheduling can dramatically improve both the efficiency and effectiveness of smart contract fuzz testing.
Smart contracts have achieved significant success, however, their security remains a long-standing challenge. The immutability and transparency of smart contracts require establishing a strong mechanism to prevent private leakage and trusted data tampering. Apart from traditional logic and code-level vulnerabilities arising from insufficient control over contract variables and function parameters, smart contracts may store private-dependent information in blockchain records, which is a critical type of vulnerability, but often overlooked in existing security analysis. In this paper, we present an automated approach for synthesizing security policies, named SmartIFSyn, to eliminate information flow vulnerabilities in smart contracts. We formalize the semantics of Solidity, the most widely used smart contract language, and analyze information flow security of Solidity smart contracts from two perspectives: local-variable security and global-interaction security. We present a type system to guide the elimination of local-variable vulnerabilities by inferring a policy and resort to constraint solving to synthesize a desired policy in case that the type system fails. The policy ensures both local-variable and global-interaction security while it is maximally aligned with user preference. Furthermore, the policy can be subsequently converted into enforceable specifications. We implement our approach in a tool and evaluate it on 17,160 real-world Ethereum smart contracts. The experimental results demonstrate the efficacy of our approach, e.g., detected 243 vulnerabilities in 223 real-world Ethereum smart contracts.
Democratic electoral processes rely fundamentally on the integrity, transparency, and confidentiality of vote recording and tallying. Conventional centralized e-voting infrastructures are susceptible to single-point-of-failure attacks, insider manipulation, and audit opacity, undermining public confidence in electoral outcomes. This paper proposes a novel blockchain-based e-voting architecture that integrates a hybrid consensus mechanism combining Practical Byzantine Fault Tolerance (PBFT) and Proof-of-Authority (PoA) to achieve simultaneously high transaction throughput, low confirmation latency, and strong Byzantine fault resilience. The system employs RSA-based digital signatures, zero-knowledge proofs (ZKP) for voter anonymity, and Ethereum-compatible smart contracts encoded in Solidity for automated ballot management and tamper-evident tallying. The proposed framework is evaluated through a simulated electoral environment involving up to 50,000 concurrent voters, demonstrating a peak throughput of 8,750 transactions per second (TPS), an average vote confirmation latency of 0.22 seconds, and a fault tolerance threshold of up to f = (n−1)/3 Byzantine nodes. Comparative analysis against Ethereum Proof-of-Work, standard PBFT, Hyperledger Fabric, and centralized database voting systems confirms that the proposed hybrid approach outperforms all baselines across throughput, latency, security, and scalability dimensions. The system achieves 97.8% integrity assurance and 95.3% voter anonymity preservation under adversarial network conditions, establishing a practically deployable, auditable, and voter-verifiable e-voting solution suitable for national-scale elections.
Inundating networks with traffic to cripple service availability defines a DDoS attack. Traditional defences, like firewalls and centralized scrubbing centers, can suffer from single points of failure during large-scale attacks. Enter new blockchain technology, with Ethereum probably leading the way in decentralized solutions. Since Ethereum smart contracts enable DDoS detection and enforcement of validation rules, events can be managed automatically, applying rewards or penalties without a central authority. In this way it leverages the positive aspects of crypto-economic mechanisms and reputation systems; giving people an incentive to honestly participate while making abusing the system unprofitable, creating a trustless, transparent, resilient decentralized defence against cyber threats. The architecture of a blockchain-validated system that gates access to services includes a back-end gateway responsible for verifying transactions on the blockchain before processing user queries. The off-chain detection algorithm identifies unexpected traffic spikes that exceed a predefined threshold. Attackers were deterred by cost, the system itself stayed up and running, and the smart contract worked autonomously. The suggested approach maintained 96% access success for authorized users, successfully blocked 92% of DDoS traffic, and guaranteed 98% uptime during simulated attacks. All validated access attempts were 100% immutably recorded on-chain, and attackers had to pay 300% more employs transparent on-chain rules and Ethereum smart contracts to manage access. Because every access attempt is permanently documented on the blockchain, it is difficult to alter logs or stop denial-of-service attacks without detection.
The growth of the usage of decentralized applications on Ethereum has seen the rise of an increasing number of bad actors that are using it to commit fraud, phishing, money laundering and financial scams. Traditional detection methods are less effective to detect accounts with more complex and changing behaviours. The paper suggests a novel multi-level framework for detecting malicious Ethereum accounts based on supervised classification and adaptive anomalous account verification using a routing based on probabilities. To get the transaction behavior features, opcode features and time-interval features from the publicly available EtherShield data set, we use the entire data set to extract the entire features. The first is a Level-1 where an XGBoost machine learning model classifies Ethereum accounts into Fair, Likely Malicious and Malicious categories, and outputs calibrated probability scores for any account. Uncertainty about malicious accounts are escalated to level-2 where anomaly verification and behavioural re-assessment are carried out by using models such as Random Forest and Isolation Forest. The final classification is obtained by decision fusion process, which combines the results obtained from both levels. The Random Forest-based verification module is evaluated in the experiments and is found to be 95% accurate, 94% macro precision, 95% macro recall and 95% macro F1-score, which is significantly better than the Isolation Forest (baseline). Besides, stratified 5-Fold Cross Validation further demonstrates that the proposed framework is robust and generalizable with a mean accuracy of 94.96% ± 0.43, mean precision of 94.40% ± 0.42, mean recall of 94.84% ± 0.47 and mean F1-Score of 94.66% ± 0.42. The proposed framework proves to be an effective solution to minimize misclassification, enhance the reliability of detection and offer a scalable answer to safeguard Ethereum blockchain ecosystems from newly emerged malicious activities.
Mingxi Ye, Yuhong Nan, Zhijie Zhong, Jianzhong Su · 7 authors
Given the critical nature of Ethereum, exploiting 1-day vulnerabilities that are patched but not yet widely deployed is essential. Meanwhile, Automatic Patch-based Exploit Generation (APEG) is a promising technique for this, as it helps developers understand root causes, verify fixes in downstream forks, and detect incomplete patches. However, existing exploit generation tools can not work well for vulnerabilities on Ethereum due to three key unique challenges: (1) navigating complex and cross-language exploit paths hidden within patches, (2) synthesizing complicated and stateful environment configurations, and (3) handling non-deterministic inconsistencies between blockchain nodes that lead to false alarms. To address these challenges, we introduce ChainDelta , a novel fuzzing agent framework driven by Large Language Models to automatically generate exploits based on Ethereum security patches. ChainDelta consists of three core modules: a directed fuzzer utilizes call graph analysis to guide testing towards vulnerable code based on the patch information; an agent-based environment fuzzer acts as an expert to automatically set up the necessary blockchain states to trigger vulnerabilities; and finally, a state-aware sanitizer performs differential analysis while monitoring the blockchain transient state to distinguish true inconsistencies from benign non-determinism. We evaluate ChainDelta on a diverse benchmark with real-world patches, covering a wide range of types such as data racing and denial-of-service. ChainDelta successfully generated exploits with a 64% success rate and only a 15.8% false positive rate. An ablation study confirms the contribution of each module to the overall performance. To demonstrate its practical impacts, we conducted a real-world auditing campaign on top of ChainDelta , leading to the discovery of four previously undisclosed vulnerabilities with bug bounties.
Decentralized Finance (DeFi) has revolutionized financial services by eliminating traditional intermediaries, but this openness creates new vulnerabilities that malicious actors exploit for fraud. The pseudonymous nature of blockchain transactions and lack of centralized oversight make traditional fraud detection methods inadequate for the DeFi ecosystem. This paper introduces ChainGuard, an end-to-end fraud detection system that leverages verifiable machine learning with zero-knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARKs). ChainGuard utilizes a comprehensive approach that combines advanced feature extraction from Ethereum blockchain transaction data, optimized machine learning models, and on-chain verification through zk-SNARKs. Our solution enables privacy-preserving fraud detection while maintaining the ability to verify results without exposing sensitive transaction data and the internal architecture of the model. We demonstrate that ChainGuard achieves permissible accuracy in detecting fraudulent activities across Ethereum and various DeFi platforms while ensuring computational efficiency through multiple optimization techniques, including quantization. Experimental results show that our approach achieves performance comparable to traditional fraud detection methods while maintaining the decentralized and trustless nature of blockchain systems.
Backdoor attacks on Ethereum smart contracts are a kind of attack in which an adversary exploits the privileges of his/her developed smart contract to manipulate the cryptoassets generated by that contract. Although there are several countermeasures against backdoor attacks, to the best of our knowledge, no empirical study based on the source code of the backdoor attacks has been conducted so far. In this paper, we conduct an empirical study to understand how backdoor attacks on Ethereum smart contracts are performed in the real world by analyzing all smart contracts with source code. To this end, we also propose a new investigation tool for the empirical study, named BACCA. We then identify several key insights. First, we totally found 288,440 contracts for backdoors attacked, called contract backdoors, including potential ones. The most significant backdoor attack is DisableTransfer, which disables transfers of assets to other addresses, and we identify 211,687 smart contracts affected by this attack. Second, several backdoor attacks are combined with other types of backdoor attacks, called combination contract backdoors, and 90.9% of DisableTransfer are combination contract backdoors. Third, GenerateToken, which enables an adversary to generate new cryptoassets, is the largest as a backdoor without any combinations, i.e., unique contract backdoors, and 23.8% of GenerateToken are the unique contract backdoors. Fourth, when we analyze the number of transactions sent to the above contract backdoors to identify their gas consumption in the real world, we identify that about 9 trillion dollars have been consumed by them. We also found a remarkable contract backdoor that receives more than a million transactions, resulting in gas consumption of about two billion dollars. We further discuss implications for the design of analysis tools for subsequent work and the impact of compiler updates on the above backdoor attacks.
Sabri Hisham, Mokhairi Makhtar, Azwa Abdul Aziz, Ahmad Firdaus · 6 authors
Abstract The global expansion of blockchain technology has unfortunately been accompanied by a rise in fraudulent activities within decentralized applications (DApps), leading to substantial financial losses. The immense volume of transaction data (big data) makes manual detection of abnormal account behavior impossible, necessitating the use of automated machine learning (ML) techniques. Existing anomaly machine learning detection approaches often rely on single-classifier models that suffer from limited generalization, high false-positive rates, or insufficient feature relevance, thereby compromising detection accuracy and system security. Moreover, the high dimensionality and complexity of blockchain data necessitate more sophisticated and robust methodologies that can effectively identify relevant features and leverage the strengths of multiple learning algorithms. This study addresses a key gap by proposing a novel anomaly detection framework for the Ethereum blockchain that distinctively integrates the Boruta feature selection algorithm with a combination of ensemble methods and a fuzzy logic classifier. Specifically, we investigate the performance of various ensemble techniques (bagging, boosting, voting, and stacking) combined with foundational models (Decision Tree, Random Forest, K-Nearest Neighbors, and XGBoost), including a specialized Fuzzy ENORA model. The objective is to significantly enhance the accuracy of anomaly detection. Our results demonstrate that the ensemble models consistently and significantly outperformed single-classifier models, achieving a mean performance metric of 0.99 across accuracy, precision, recall, and F1 score, affirming the robustness of the proposed Boruta-driven ensemble approach for securing blockchain transactions.
Abstract: In the era of the digital economy, establishing an efficient and compliant data asset rights confirmation system within scalable distributed infrastructures is of critical importance. However, under heterogeneous distributed ledger environments, data circulation is often trapped in a binary tension between privacy preservation and regulatory accessibility, while facing severe scalability bottlenecks. Existing studies lack a unified solution that simultaneously addresses cross-chain interoperability, post-quantum security, and low-cost verification. To this end, this paper proposes a data asset rights confirmation framework based on hybrid post-quantum zero-knowledge proofs. The framework designs a scalable recursive composition architecture combining Scalable Transparent Argument of Knowledge (STARKs) and Succinct Non-interactive Argument of Knowledge (SNARKs), leveraging off-chain compressed permutation to significantly reduce on-chain storage overhead. In parallel, a light-client-based distributed cross-chain state synchronization protocol and a regulation-friendly privacy auditing module (based on threshold encryption) are constructed to ensure transactional atomicity and conditional auditability during data circulation. Experimental evaluations conducted on two datasets, Ethereum NFT transactions and credit card fraud detection, demonstrate that, compared with cross-chain privacy-preserving solutions such as zkCross, the proposed framework reduces on-chain verification Gas costs by approximately 18.2%, compresses proof size to 0.28 kB, and achieves a peak throughput of 1,618 Transactions Per Second (TPS). Moreover, under controlled experimental conditions, the framework attains an audit success rate of 99.6% with only 14.0% performance overhead. Overall, this study alleviates the long-standing trade-offs among privacy protection, regulatory compliance, and computational scalability, and provides a verifiable technical solution for the interoperability and infrastructure development of next-generation distributed systems.
The paper proposes an extended quality assessment model for Distributed Ledger Technology platforms, referred to as DLT-QM, developed on the basis of the ISO/IEC 25010 standard while considering the architectural and operational specifics of decentralized and blockchain-based systems. The relevance of the study is determined by the rapid development of digital technologies and the growing adoption of DLT platforms in finance, e-government, logistics, IoT ecosystems, and enterprise information systems, alongside the absence of a unified formalized approach for comprehensive quality assessment of such platforms. The study analyzes the applicability of ISO/IEC 25010 charac-teristics to DLT-oriented software systems and identifies a set of DLT-specific quality attributes reflecting the unique properties of distributed ledger environments, including decentralization level, consensus reliability, transaction finality, auditability, trust model, interoperability, and on-chain/off-chain balance. For each characteristic, mathematical metrics are formalized to support multicriteria quality assessment and optimization of architectural decisions in software engineering tasks. The integral quality indicator QDLT is defined as a weighted combination of the traditional ISO/IEC 25010 component and a DLT-specific component, enabling the adaptation of the model to various application scenarios. The proposed model is validated using four representative DLT platforms: Hyperledger Fabric, Ethereum, Corda, and Polygon. The obtained results confirm the existence of structural trade-offs between decentralization, performance, security, and interoperability in modern distributed systems. Furthermore, a scenario-oriented application methodology is developed, including a procedure for determining weighting coefficients depending on the application domain, such as financial consortium systems, e-government infrastructures, and IoT supply chain environments. The practical significance of the research lies in the development of a formalized decision-support instrument for selecting DLT platforms in the design and implementation of modern software systems and digital services. Keywords: blockchain, distributed ledger technology, DLT platforms, decentralized systems, distributed systems, information technologies, digital technologies, software engineering.
The structural supranational criterion has been applied to public attractors with a legal form, to a monetary attractor with none, and to private platform attractors. This paper carries it to a third attractor type, the computational-settlement platform on a pure-protocol substrate, the larger smart-contract platforms and the rollups that settle to them. The supranational criterion holds that an entity is a cascade attractor when its binding residue density spans participants so that no single participant’s reversal dissolves the network, with density a multiplicative product of depth, extent, and interconnection in which a necessary component at near-zero nullifies the whole. The decisive choice is the location of the residue. The residue is the composed stack, the deployed contracts, the value locked in them, the standards, and the settlement finality that applications and rollups depend on. It is not the token, which is the internal unit and the instrument that secures and meters the chain. Depth is switching-cost lock-in of composed state. Extent is the deployed value and the user base. Interconnection is composability, the dependence of applications on applications and of settlement layers on a base layer, the settlement interconnection of the Euro re-realised in contract code. The reversal test is the consensus halt, which suspends but does not relocate the residue, because the composed stack persists across it and resumes on restart. The de-concentration that governs whether a platform halts is operational, the diversity of the implementation, the clients and the sequencers, and it is not the distribution of stake, a measure on which the halting platform scores as well as the resilient one. The Ethereum finality incident of May 2023, set against the halt records of Solana and Avalanche, is the existence proof. The same class of client defect stalled Ethereum without halting it, because the independent clients that did not share the defect carried the chain, and halted the two platforms that ran a single client. The argument is set alongside the blockchain trilemma, which states the scalability-decentralisation tradeoff the spectrum populates, and it contributes what the trilemma does not, the location of the residue and the survival of the attractor when it halts.
Giulio Segalini, Yigit Çolakoğlu, Marko Putnik, Jérémie Decouchant
Decentralized Finance (DeFi) applications rely heavily on the order in which transactions are executed, making them susceptible to reordering attacks that enable adversaries to extract Blockchain Extractable Value (BEV). While linear blockchain systems such as Ethereum have inspired extensive research into fair ordering mechanisms, DAG-based consensus protocols have remained largely unprotected despite their growing adoption for scalability and performance. In this paper, we introduce Tilikum, a DAG-based ledger protocol that ensures fair transaction ordering without relying on weak edges. Tilikum achieves ordering linearizability by leveraging median-based timestamp aggregation, or batch order fairness, while maintaining low data redundancy and robust garbage collection. We implemented Tilikum in Rust and evaluated it against representative baselines, namely Narwhal/Tusk, Pompē, Themis and FairDAG. Our results show that Tilikum achieves up to $39\times$ higher throughput than other fair-ordering baselines, while fully blocking state-of-the-art DAG-specific reordering attacks.
We investigate the evolving structure of interactions in cryptocurrency markets using a network-based framework constructed from high-frequency price data spanning 2020-2025. Directed and weighted networks are constructed from statistically significant Granger causal relationships between cryptocurrency log-returns, enabling us to quantify the flow of influence across assets. We find that normalized returns exhibit heavy-tailed distributions, consistent with the presence of large intermittent fluctuations and in line with stylized facts of financial markets. The resulting networks display pronounced heterogeneity in link weights and nodal strengths, indicating that a small subset of cryptocurrencies contributes disproportionately to market dynamics. By ranking cryptocurrencies based on their nodal out-strength, we uncover a dynamically evolving hierarchy of influence. Ethereum consistently emerges as the most influential asset, while Bitcoin shows a gradual decline in its relative importance. The ranking structure exhibits substantial temporal variability, with multiple cryptocurrencies entering and exiting the top positions over time. Our findings reveal a highly competitive and non-stable organization of the cryptocurrency ecosystem.
To map and synthesize original research on the Ethereum blockchain, emphasizing the dominant study themes, practical implications, recurring technical findings, and future research needs across security, performance, decentralized applications, markets, privacy, governance, and domain-specific implementations. The review uses 250 references and builds its evidence map from 488 original studies with 242345462 total participants/sample observations (topic-deduplicated ΣN). This review suggests that Ethereum is best understood not as a single application but as a programmable settlement layer whose real-world value is consistently conditioned by security, transaction-cost, and governance constraints rather than by immutability alone. The most recurrent result-level signal indicates that openness creates measurable adversarial surfaces, with blockchain extractable value reaching $540.54M over 32 months and censoring actors producing 46% of blocks while delaying affected transactions by an average of 85%. In parallel, machine learning and graph-based methods were repeatedly associated with strong fraud and phishing detection performance, with reported accuracies exceeding 96% in several settings. These patterns support a practical emphasis on gas-aware design, contract assurance, and continuous monitoring, often realized through hybrid on-chain/off-chain and permissioned architectures. Because most evidence comes from experimental and feasibility studies, future work should prioritize longitudinal, real-world deployment studies that measure security incidents, cost, and resilience beyond controlled testnets.