# Anomaly Detection in Blockchain: A Systematic Review of Trends, Challenges, and Future Directions
## 3 Results
### Research Hotspots and Evolution Trend Analysis  
In this section, we use co-citation analysis to gain insights into research development and identify emerging trends within the field of blockchain anomaly detection. We conducted a clustering analysis of co-cited references using CiteSpace software, resulting in a network map categorized into 11 major clusters (Figure 7). The network quality metrics confirm the reliability of our analysis. The mean silhouette value (S) of 0.9202, significantly exceeding the threshold of 0.7, indicates highly homogeneous clusters. Similarly, the modularity value (Q) of 0.758, well above the 0.3 threshold, demonstrates clear boundaries between different research streams. These metrics validate the robustness of the identified intellectual structure. Figure 8 demonstrates the evolution of research within each cluster, where the points on the horizontal axis represent frequently cited references by publication year, the connecting lines show the cocitation relationships, and the size of the point indicates the frequency of citation. The color of each circle conveys the relative influence and temporal dynamics of individual publications. Red circles indicate references that experienced the strongest citation bursts, reflecting publications that attracted significant attention during specific periods. Orange circles represent works with a moderate level of influence, characterized by steady but less intense citation activity. In contrast, blue and grey circles correspond to references with lower impact. Circles outlined in purple highlight publications with high betweenness centrality, emphasizing their role as critical connectors between thematic clusters. Figure 6: Top-10 keywords with the strongest citation bursts. The timeline map illustrates how burst patterns manifest within the broader intellectual structure of the field. The horizontal distribution of nodes reveals how research focus has progressively shifted from foundational topics like \"bitcoin concepts threat\" (#1), \"industrial network\" (#6), \"smart contract\" (#7), and \"server\" (#9), toward methodologically advanced domains like \"using unsupervised learning\" (#0) and \"lightweight federated learning\" (#2). Figure 9 presents the top-10 references exhibiting the strongest citation bursts within the blockchain anomaly detection research landscape. These bursts highlight pivotal works that significantly influenced the field during specific periods, shaping subsequent research directions and methodological innovations. The selected references span a publication period from 2014 to 2019, with burst periods ranging from 2018 to 2024. Figure 8: Visualization of the timeline map of reference co-citation. Figure 7: Visualization of the research clusters in the co-citation network. The Ethereum whitepaper by [PERSON]. (2014) [53], a foundational work in blockchain development, saw a strong citation burst from 2018 to 2019 (strength: 3.03). This burst reflects Ethereum's critical role in enabling decentralized applications (DApps) and smart contracts, as well as its influence on subsequent research in blockchain technologies. The introduction of DeepLog, a deep learning model using LSTM for anomaly detection in system logs described in paper [26], had a citation burst from 2019 to 2020 (strength: 2.12). The comprehensive survey of blockchain technology by [PERSON]. et al. (2017) [54] experienced a citation burst from 2019 to 2021 (strength: 2.01). This paper catalyzed research into blockchain scalability, security, and consensus mechanisms -areas critical for secure and efficient anomaly detection in decentralized networks. Research on detecting Ponzi schemes in blockchain environments through machine learning, conducted by [PERSON]. (2018) [55], had the highest citation burst (strength: 3.31) between 2021 and 2022, highlighting the rising concerns over fraud in decentralized financial systems, particularly in Ethereum-based smart contracts.