# Anomaly Detection in Blockchain: A Systematic Review of Trends, Challenges, and Future Directions
## 4 Discussion
### Open Challenges and Future Directions  
This section explores the anticipated future directions in the realm of anomaly detection in blockchain, based on the analysis of co-citation clusters, top-cited papers, their abstracts, and emerging trends identified in Section 3. These directions reflect not only current technological capabilities but also fundamental challenges that require comprehensive solutions to ensure the resilience and security of blockchain systems. We identified six key areas encompassing critical challenges and future research opportunities:  
1. _Federated learning and privacy-preserving technologies._ The analysis of the keyword map (Figure 5), timeline map of reference co-citation (Figure 8), citation bursts (Figure 9), and core publications in cluster #2 demonstrates a growing interest in federated learning and highlights open challenges within the context of blockchain security [27; 78; 79; 80; 81; 82; 83; 87; 88; 89; 90]. Combining blockchain with federated learning offers a unique opportunity to create transparent, auditable anomaly detection models without centralizing training data. However, several open challenges remain, which future research will need to address to advance the domain: * developing attack-resistant federated models for anomaly detection in scalable blockchain networks; * implementing differential privacy techniques in federated models to balance detection accuracy with user privacy protection. 2. _Integration of multimodalata and heterogeneous sources._ The analysis of the keyword map (Figure 5), timeline map of reference co-citation (Figure 8), citation bursts (Figure 9), and core publications in clusters related to machine and deep learning (specifically, clusters #0, #3, #4) demonstrates a growing interest in comprehensive approaches to anomaly detection. Current research reveals a significant shift from one-dimensional transaction analysis, based on individual data types, to an all-encompassing approach that considers diverse aspects of blockchain data [26; 61; 66; 67; 68; 73; 91; 95; 96; 98; 100; 101; 103; 140]. This shift underscores the need for integrating heterogeneous data to enhance the effectiveness of detecting complex anomalies that might be unnoticeable when analyzing only one type of information. Achieving such integration in blockchain anomaly detection requires robust systems capable of securely managing and processing diverse datasets. In this context, innovative data storage and processing systems such as SecuDB [141], LedgerDB [142], and VeDB [143] can play a pivotal role. These technologies provide a high level of security, data integrity, and verifiability, which are critically important for identifying deviations from normal behavior in decentralized environments. Future research in this direction may include the following: * developing algorithms that simultaneously analyze on-chain and off-chain data for comprehensive anomaly detection;* creating systems that integrate blockchain data with traditional financial transactions to detect cross-platform fraud schemes;
* applying natural language processing methods to analyze smart contracts along with user behavioral patterns;
* leveraging secure database technologies to provide scalable and tamper-resistant analytics platforms. * _Explainable AI and interpretable detection models._ The results of our study demonstrate a clear shift from basic security mechanisms to sophisticated machine learning and graph neural network approaches in blockchain anomaly detection. Specifically, some publications in clusters related to machine and deep learning (e.g., clusters #0, #3, #4) reveal a growing interest in employing these advanced methods [66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 100; 99; 101; 102; 103; 104; 105; 106].