Anomaly Detection in Blockchain: A Systematic Review of Trends, Challenges, and Future Directions
Source abstract
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.
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Research context
OpenAlex topics: Blockchain Technology Applications and Security · Anomaly Detection Techniques and Applications · Network Security and Intrusion Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science and Automatics, University of Bielsko-Biala, 43-309 Bielsko-Biala, Poland
Department of Computer Science, West Ukrainian National University, 46009 Ternopil, Ukraine
Aston Business School, Aston University, Birmingham B4 7ET, UK
Department of Clinical Engineering, Academy of Silesia, 40-555 Katowice, Poland
References (100+)
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