# Anomaly Detection in Blockchain: A Systematic Review of Trends, Challenges, and Future Directions
## 4 Discussion
### Main Findings  
Our findings based on the research questions defined can be summarized as follows. RQ1: We found that the results obtained in this study demonstrate an uneven yet dynamically growing scientific interest in the field of blockchain anomaly detection. Publication activity is primarily concentrated in countries with strong techno-scientific infrastructures. The highest research intensity is observed in China, which has emerged as a major hub for the generation of knowledge in the field of blockchain analytics. The United States and India also exhibit significant scholarly output, indicating the global relevance of security issues in distributed systems. Interestingly, the group of active contributors includes not only traditionally dominant scientific powers but also countries undergoing accelerated digital transformation over the past decade, such as Saudi Arabia. This suggests the emergence of a new map of international participation in blockchain-related research, where state-led initiatives and strategic investments in innovation play a pivotal role. The key institutions contributing to research development are primarily Asian technical universities. Identified academic centers in China hold central positions in the structure of institutional cooperation. At the same time, universities from other regions, notably Canada, India, and the Middle East, also demonstrate visible activity. However, institutional linkages remain underdeveloped, as evidenced by the low connectivity of the academic collaboration network. This may reflect the absence of well-established international consortia or the limited interregional coordination in this area. Instead, the landscape is dominated by isolated or regionally focused research groups formed around local thought leaders. A group of leading authors has been identified as actively shaping both theoretical and applied approaches to anomaly detection in blockchain networks. Researchers combining expertise in cybersecurity, artificial intelligence, and distributed computing play a particularly influential role. The most productive contributors include scholars from Canada, India, and China, who collectively constitute the core of the field's publication impact. Notably, a substantial number of these researchers have joined the academic discourse relatively recently - after 2020 - highlighting the emerging and innovative nature of this subfield. Author collaboration within individual publications reveals a tendency toward thematic clustering, though inter-cluster cooperation remains limited. Within the scope of citation analysis, special attention is drawn to the figure of [PERSON], who, despite not being directly involved in anomaly detection research, remains central in the historical and methodological context due to the foundational role in conceptualizing blockchain technology itself. RQ2: Using the CiteSpace software, an analysis was conducted of the keyword network and co-citation cohorts in studies dedicated to anomaly detection in blockchain. This enabled the identification of major research clusters and current hotspots and the tracing of evolving trends in the field. The largest research cluster is related to unsupervised learning methods. It includes more than fifty works focused on the application of unsupervised learning algorithms for detecting anomalies in blockchain. The second-largest cluster is dedicated to Bitcoin security. This area covers research on primary threats such as double-spending attacks, ransomware, and illicit transactions, thereby forming foundational knowledge about cryptocurrency security. Another important direction is lightweight federated learning, which is gaining popularity due to its ability to ensure privacy protection and enhance the efficiency of anomaly detection in distributed systems, particularly in the IoT domain, while minimizing computational overhead. Among the research hotspots, anomaly detection is the dominant concept most frequently encountered in studies.