Identifying Likely-Reputable Blockchain Projects on Ethereum
- URL: http://arxiv.org/abs/2503.15542v1
- Date: Fri, 14 Mar 2025 21:43:25 GMT
- Title: Identifying Likely-Reputable Blockchain Projects on Ethereum
- Authors: Cyrus Malik, Josef Bajada, Joshua Ellul,
- Abstract summary: This work presents a systematic approach that integrates multiple data sources with advanced analytics to evaluate credibility, transparency, and overall trustworthiness.<n>The study classifies accounts based on a dataset comprising 2,179 entities linked to illicit activities and 3,977 associated with reputable projects.<n>Using the LightGBM algorithm, the approach achieves an average accuracy of 0.984 and an average AUC of 0.999, validated through 10-fold cross-validation.
- Score: 0.9831489366502298
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Identifying reputable Ethereum projects remains a critical challenge within the expanding blockchain ecosystem. The ability to distinguish between legitimate initiatives and potentially fraudulent schemes is non-trivial. This work presents a systematic approach that integrates multiple data sources with advanced analytics to evaluate credibility, transparency, and overall trustworthiness. The methodology applies machine learning techniques to analyse transaction histories on the Ethereum blockchain. The study classifies accounts based on a dataset comprising 2,179 entities linked to illicit activities and 3,977 associated with reputable projects. Using the LightGBM algorithm, the approach achieves an average accuracy of 0.984 and an average AUC of 0.999, validated through 10-fold cross-validation. Key influential factors include time differences between transactions and received_tnx. The proposed methodology provides a robust mechanism for identifying reputable Ethereum projects, fostering a more secure and transparent investment environment. By equipping stakeholders with data-driven insights, this research enables more informed decision-making, risk mitigation, and the promotion of legitimate blockchain initiatives. Furthermore, it lays the foundation for future advancements in trust assessment methodologies, contributing to the continued development and maturity of the Ethereum ecosystem.
Related papers
- Trusted Compute Units: A Framework for Chained Verifiable Computations [41.94295877935867]
This paper introduces the Trusted Compute Unit (TCU), a unifying framework that enables composable and interoperable computations across heterogeneous technologies.
By enabling secure off-chain interactions without incurring on-chain confirmation delays or gas fees, TCUs significantly improve system performance and scalability.
arXiv Detail & Related papers (2025-04-22T09:01:55Z) - Quantifying the Blockchain Trilemma: A Comparative Analysis of Algorand, Ethereum 2.0, and Beyond [4.605490094506685]
This study evaluates and compares two leading proof-of-stake (PoS) systems, Algorand and 2.0.
We analyze each platform's strategies in a structured manner to understand their effectiveness in addressing trilemma challenges.
arXiv Detail & Related papers (2024-07-19T14:15:29Z) - MEV Ecosystem Evolution From Ethereum 1.0 [6.151915040556504]
In traditional finance, there are possibilities to create values, e.g., arbitrage offers to create value from market inefficiencies or front-running offers to extract value for the participants having privileged roles.
Such opportunities are readily available in DeFi ecosystems, where diverse participants engage in financial activities.
In this survey, first, we show how lucrative such opportunities can be. Then, we discuss how protocolfollowing participants trying to capture such opportunities threaten to sabotage blockchain's performance.
Finally, we review the current state of research trying to restore trustlessness and decentralization to provide all DeFi participants with a fair marketplace
arXiv Detail & Related papers (2024-06-19T14:22:26Z) - Remeasuring the Arbitrage and Sandwich Attacks of Maximal Extractable Value in Ethereum [7.381773144616746]
Maximal Extractable Value (MEV) drives the prosperity of the blockchain ecosystem.
We propose a profitability identification algorithm to identify MEV activities on our collected largest-ever dataset.
We have characterized the overall landscape of the MEV ecosystem, the impact the private transaction architectures bring in, and the adoption of back-running mechanisms.
arXiv Detail & Related papers (2024-05-28T08:17:15Z) - Blockchains for Internet of Things: Fundamentals, Applications, and Challenges [38.29453164670072]
Not every blockchain system is suitable for specific IoT applications.
Public blockchains are not suitable for storing sensitive data.
We explore the blockchain's application in three pivotal IoT areas: edge AI, communications, and healthcare.
arXiv Detail & Related papers (2024-05-08T04:25:57Z) - Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning [51.13534069758711]
Decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities.
Federated Learning (FL) enables participants to collaboratively train models while safeguarding data privacy.
This paper investigates the synergy between blockchain's security features and FL's privacy-preserving model training capabilities.
arXiv Detail & Related papers (2024-03-28T07:08:26Z) - Graph Attention Network-based Block Propagation with Optimal AoI and Reputation in Web 3.0 [59.94605620983965]
We design a Graph Attention Network (GAT)-based reliable block propagation optimization framework for blockchain-enabled Web 3.0.
To achieve the reliability of block propagation, we introduce a reputation mechanism based on the subjective logic model.
Considering that the GAT possesses the excellent ability to process graph-structured data, we utilize the GAT with reinforcement learning to obtain the optimal block propagation trajectory.
arXiv Detail & Related papers (2024-03-20T01:58:38Z) - Analyzing Reward Dynamics and Decentralization in Ethereum 2.0: An
Advanced Data Engineering Workflow and Comprehensive Datasets for
Proof-of-Stake Incentives [5.18461573800406]
Smart contract blockchain platform, Proof-of-Stake 2.0, guarantees precise execution of applications without third-party intervention.
Our study collects consensus reward data from the Beacon chain and conducts a comprehensive analysis of reward distribution and evolution.
To evaluate the degree of decentralization in PoS, we apply several inequality indices, including the Shannon entropy, the Gini Index, the Nakamoto Coefficient, and the Herfindahl-Hirschman Index (HHI)
arXiv Detail & Related papers (2024-02-17T02:40:00Z) - Enhanced Security and Efficiency in Blockchain with Aggregated Zero-Knowledge Proof Mechanisms [15.034624246970154]
Current approaches to data verification in blockchain systems face challenges in terms of efficiency and computational overhead.
This study proposes an innovative aggregation scheme for Zero-Knowledge Proofs within the structure of Merkle Trees.
We develop a system that significantly reduces the size of the proof and the computational resources needed for its generation and verification.
arXiv Detail & Related papers (2024-02-06T09:26:46Z) - Generative AI-enabled Blockchain Networks: Fundamentals, Applications,
and Case Study [73.87110604150315]
Generative Artificial Intelligence (GAI) has emerged as a promising solution to address challenges of blockchain technology.
In this paper, we first introduce GAI techniques, outline their applications, and discuss existing solutions for integrating GAI into blockchains.
arXiv Detail & Related papers (2024-01-28T10:46:17Z) - ACon$^2$: Adaptive Conformal Consensus for Provable Blockchain Oracles [31.439376852065713]
Power of smart contracts is enabled by interacting with off-chain data, which in turn opens the possibility to undermine the block state consistency.
We propose an adaptive conformal consensus (ACon$2$) algorithm, which derives consensus from multiple oracle contracts.
In particular, the proposed algorithm returns a consensus set, which quantifies the uncertainty of data and achieves a desired correctness guarantee.
arXiv Detail & Related papers (2022-11-17T04:37:24Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.