Design of an efficient model for enhanced blockchain forensics through anomaly detection, graph neural networks, and cross-blockchain analysis

The emergent necessity for sophisticated forensic methodologies in blockchain technology stems from its burgeoning utilization as a decentralized ledger, juxtaposed with a parallel increase in its exploitation for illicit activities. Existing forensic techniques in blockchain investigations often grapple with limitations such as inadequate detection of anomalies, insufficient analysis across different blockchain networks, and a lack of effective tools to analyze complex transaction relationships. Addressing these gaps, this research pioneers a novel framework integrating Anomaly Detection, Graph Neural Networks (GNNs), and Cross-Blockchain Analysis to significantly enhance forensic capabilities in blockchain investigations & operations. Innovatively, the proposed model employs Long Short-Term Memory (LSTM) networks for Anomaly Detection, capitalizing on their proficiency in modeling the temporal dynamics of blockchain transactions. The integration of LSTM addresses the critical challenge of detecting deviations in transaction patterns, a common shortcoming in current methods. Concurrently, the utilization of Graph Neural Networks (GNNs) facilitates a nuanced analysis of transaction graphs, enabling effective clustering of wallet addresses. This aids in tracing the trajectory of funds and unveiling illicit actors within the blockchain network, a capability often underexplored in existing models. Furthermore, the introduction of Cross-Blockchain Analysis marks a significant stride in blockchain forensics, allowing for the amalgamation and examination of data across diverse blockchain networks, thereby offering a more comprehensive forensic view for different scenarios. The empirical validation of this integrated framework on CSAFE & CFReDS datasets exhibits an enhancement in precision by 4.9%, accuracy by 4.5%, recall by 4.3%, AUC by 5.9%, and processing speed by 5.5% in comparison to existing methods. These results underscore the superiority of the proposed model in terms of its efficiency and effectiveness in identifying suspicious transactions and wallet address clusters. The impacts of this work are manifold and far-reaching. By augmenting the precision and scope of blockchain forensics, it empowers law enforcement and regulatory bodies in their pursuit against financial crimes within the blockchain space sets. The framework’s comprehensive approach in investigating blockchain-based illicit activities also provides valuable insights for improving regulat