Ethereum Blockchain Security, Performance, Markets, and Applications: Scoping Review with ☸️SAIMSARA

Name: SAIMSARA Evidence Object digital::ETHEREUM_SS
Creator: SAIMSARA
License: https://saimsara.com/license/

SAIMSARA

doi:10.62487/saimsarad7a61035

SAIMSARA Journal

Machine-Readable Science • ISSN 3054-3991

Ethereum Blockchain Security, Performance, Markets, and Applications: Scoping Review with ☸️SAIMSARA

Digital Health & Biotech

Issue 3, Volume 1, 2026

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DOI: 10.62487/saimsarad7a61035

Editorial note

• Last update: 2026-06-24 10:28:48

What is this paper about

Ethereum is not simply a blockchain platform—it is a complex programmable ecosystem where security, gas costs, market structure, privacy, and governance determine whether real-world applications succeed or fail. The full review maps the strongest evidence across 250 references and 488 original studies, revealing where Ethereum delivers measurable value, where hidden risks accumulate, and which technical strategies offer the most practical path forward.

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Video summary generated from this ☸️SAIMSARA evidence map. Full reference-linked paper and evidence JSON are available after purchase.

Abstract: 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.

Keywords: Ethereum blockchain; Smart contracts; Fraud detection; Phishing detection; NFT markets; Gas price prediction; Transaction processing; Decentralized applications; Machine learning; Blockchain security

Review Stats

Final search date and database lock: 2026-06-23 23:21:15 CEST
Plan: Pro (expanded craft tokens; source: Semantic Scholar)
Source: Semantic Scholar
Total Abstracts/Papers: 131146
Downloaded Abstracts/Papers: 1000
Included original and non-original Abstracts/Papers (all): 588
Included original Abstracts/Papers (Vote counting by direction of effect): 488
Reference Index (links used in paper): 250
Total participants/sample observations (topic deduplicated ΣN): 242345462

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Reference Index (250)

[1] Enhancing fraud detection in the Ethereum blockchain using ensemble learning — https://doi.org/10.7717/peerj-cs.2716
[2] Adaptive Attention-Based Graph Representation Learning to Detect Phishing Accounts on the Ethereum Blockchain — https://doi.org/10.1109/tnse.2024.3355089
[3] Gas fees on the Ethereum blockchain: from foundations to derivative valuations — https://doi.org/10.3389/fbloc.2024.1462666
[4] Systematic Review of Security Vulnerabilities in Ethereum Blockchain Smart Contract — https://doi.org/10.1109/access.2021.3140091
[5] Non-fungible token (NFT) markets on the Ethereum blockchain: temporal development, cointegration and interrelations — https://doi.org/10.1080/10438599.2022.2119564
[6] Generative Adversarial Networks for Cyber Threat Hunting in Ethereum Blockchain — https://doi.org/10.1145/3584666
[7] Is My Transaction Done Yet? An Empirical Study of Transaction Processing Times in the Ethereum Blockchain Platform — https://doi.org/10.1145/3549542
[8] Gas Cost Analysis of Fractional NFT on the Ethereum Blockchain — https://doi.org/10.1109/icbc56567.2023.10174920

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