# Anomaly Detection in Blockchain: A Systematic Review of Trends, Challenges, and Future Directions
## 1 Introduction
We apply the PRISMA methodology to select 363 high-quality research papers from the Web of Science Core Collection (WoSCC) and utilize CiteSpace v6.4.R1 to generate quantitative knowledge maps. Through co-citation clustering, keyword timeline analysis, and citation bursts, we capture the intellectual foundations and research frontiers of blockchain anomaly detection. In addition, our analysis of author and institutional collaboration networks reveals how research efforts are distributed and interconnected globally. The study focuses on the following research questions (RQs):  
RQ1: Which countries, institutions, and authors collaborate in research on blockchain anomaly detection, and what are their key contributions to advancing this field? RQ2: What are the main research clusters, hotspots, and evolving trends in the application of blockchain anomaly detection techniques? RQ3: What are the emerging challenges and future research directions for anomaly detection in blockchain networks? The main contributions of this paper are as follows:  
1. We present a bibliometric mapping of blockchain anomaly detection research from 2017 to 2024 based on 363 articles indexed in WoSCC. The analysis outlines the global research landscape, major thematic areas, and the evolution of key topics in this rapidly developing field. * Using CiteSpace v6.4.R1, we conducted analyses of country and institutional collaboration networks, co-citation networks, references with citation bursts, and keyword co-occurrence. These results uncover the intellectual structure of the field and identify influential authors, leading institutions, and methodological innovations. * Through keyword timeline and burst detection, we highlight a shift in research focus from foundational mechanisms, such as rule-based and consensus-level detection--to advanced approaches including unsupervised learning, lightweight federated learning, and graph neural networks. These insights help researchers to understand how the field is evolving and where innovation is emerging. * Our analysis reveals distinct geographical clusters of research activity, with China, the United States, and India dominating publication output. Key institutions identified as major contributors include Beijing University of Posts and Telecommunications (China), Brandon University (Canada), and Nirma University (India). The most productive scholars hail predominantly from Canada, India, and China, collectively forming the core group driving the field's publication impact. * We outline open challenges and future research directions in blockchain anomaly detection, including federated learning and privacy-preserving techniques, the integration of multimodal and heterogeneous data sources, the development of explainable and interpretable AI models, real-time adaptive detection systems, cross-domain specialized applications, and the imperative need for standardization and regulatory frameworks. The structure of the remaining part of the paper is as follows. Section 2 provides details on the research methodology employed in this study. Section 3 presents the findings of the bibliometric analysis, including insights from reference co-citation clustering, emerging research trends, highly cited references, and a network map that revealed major distinct clusters, which were subsequently evaluated. Section 4 discusses the study's findings, noting limitations and outlining directions for future work. The Section 5 provides the conclusions of this study.