Anomaly Detection in Blockchain: A Systematic Review of Trends, Challenges, and Future Directions

Ruslan Shevchuk; Vasyl Martsenyuk; Bogdan Adamyk; Vladlena Benson; Andriy Melnyk

doi:10.3390/app15158330

Anomaly Detection in Blockchain: A Systematic Review of Trends, Challenges, and Future Directions

Ruslan Shevchuk^*
, Vasyl Martsenyuk
, Bogdan Adamyk
, Vladlena Benson
, Andriy Melnyk

^*Corresponding author for this work

University of Bielsko-Biala
Ternopil National Economic University
West Ukrainian National University

Research output: Contribution to journal › Article › peer-review

4 Citations (SciVal)

13 Downloads (Pure)

Abstract

Blockchain technology’s increasing adoption across diverse sectors necessitates robust security measures to mitigate rising fraudulent activities. This paper presents a comprehensive bibliometric analysis of anomaly detection research in blockchain networks from 2017 to 2024, conducted under the PRISMA paradigm. Using CiteSpace 6.4.R1, we systematically map the knowledge domain based on 363 WoSCC-indexed articles. The analysis encompasses collaboration networks, co-citation patterns, citation bursts, and keyword trends to identify emerging research directions, influential contributors, and persistent challenges. The study reveals geographical concentrations of research activity, key institutional players, the evolution of theoretical frameworks, and shifts from basic security mechanisms to sophisticated machine learning and graph neural network approaches. This research summarizes the state of the field and highlights future directions essential for blockchain security.

Original language	English
Article number	8330
Number of pages	28
Journal	Applied Sciences
Volume	15
Issue number	15
DOIs	https://doi.org/10.3390/app15158330
Publication status	Published - 26 Jul 2025

Bibliographical note

Copyright © 2025 by the authors. Licensee MDPI, Basel, Switzerland.
This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license
(https://creativecommons.org/licenses/by/4.0/).

Keywords

Blockchain
Anomaly Detection
domaine map analysis
scientometric database
Trends
Knowledge graph
CiteSpace

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10.3390/app15158330Licence: CC BY 4.0

Anomaly Detection in Blockchain
Copyright © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Final published version, 7.22 MBLicence: CC BY 4.0

Cite this

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title = "Anomaly Detection in Blockchain: A Systematic Review of Trends, Challenges, and Future Directions",

abstract = "Blockchain technology{\textquoteright}s increasing adoption across diverse sectors necessitates robust security measures to mitigate rising fraudulent activities. This paper presents a comprehensive bibliometric analysis of anomaly detection research in blockchain networks from 2017 to 2024, conducted under the PRISMA paradigm. Using CiteSpace 6.4.R1, we systematically map the knowledge domain based on 363 WoSCC-indexed articles. The analysis encompasses collaboration networks, co-citation patterns, citation bursts, and keyword trends to identify emerging research directions, influential contributors, and persistent challenges. The study reveals geographical concentrations of research activity, key institutional players, the evolution of theoretical frameworks, and shifts from basic security mechanisms to sophisticated machine learning and graph neural network approaches. This research summarizes the state of the field and highlights future directions essential for blockchain security.",

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author = "Ruslan Shevchuk and Vasyl Martsenyuk and Bogdan Adamyk and Vladlena Benson and Andriy Melnyk",

note = "Copyright {\textcopyright} 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).",

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AU - Shevchuk, Ruslan

AU - Martsenyuk, Vasyl

AU - Adamyk, Bogdan

AU - Benson, Vladlena

AU - Melnyk, Andriy

N1 - Copyright © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

PY - 2025/7/26

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AB - Blockchain technology’s increasing adoption across diverse sectors necessitates robust security measures to mitigate rising fraudulent activities. This paper presents a comprehensive bibliometric analysis of anomaly detection research in blockchain networks from 2017 to 2024, conducted under the PRISMA paradigm. Using CiteSpace 6.4.R1, we systematically map the knowledge domain based on 363 WoSCC-indexed articles. The analysis encompasses collaboration networks, co-citation patterns, citation bursts, and keyword trends to identify emerging research directions, influential contributors, and persistent challenges. The study reveals geographical concentrations of research activity, key institutional players, the evolution of theoretical frameworks, and shifts from basic security mechanisms to sophisticated machine learning and graph neural network approaches. This research summarizes the state of the field and highlights future directions essential for blockchain security.

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KW - Anomaly Detection

KW - domaine map analysis

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KW - Trends

KW - Knowledge graph

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Anomaly Detection in Blockchain: A Systematic Review of Trends, Challenges, and Future Directions

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