Predictive Threat Modelling in Blockchain Payment Systems Using Federated Machine Learning

Abstract

This continues from the previous discussion regarding threats and security issues in blockchain payment systems. The insurgence of these vector threats has requisitioned a new set of challenges encompassing double spending, Sybil attacks, consensus manipulation, front-running, and breaches of the smart contract. With the expansion of DeFi ecosystems spanning heterogeneous blockchains, it has become proportionally stringent for securing financial transactions and execution. The traditional security framework does not countenance the distributed nature and the regulatory standards demanded by these systems, especially when these are supposed to raise suspicions, request user anomalies, or respond to threat queries. There is an increased latency with this centralized approach, and there are also possible privacy concerns for the users where transactional metadata needs to be collected for analysis. To tackle these obstacles, we propose a novel predictive threat modelling framework by intertwining Federated Machine Learning (FML) into blockchain payment infrastructures. With such technology, distributed nodes (miners, validators, wallet providers) may together train models for anomaly detection without ever having to share their raw transaction data with one another-an identity-preserving way of identification and real-time prediction of malicious conduct. The framework uses a simulated multi-chain dataset containing a host of threat vectors and federated versions of machine learning models, including Random Forests, CNNs, and LSTMs. Besides proposing a blockchain-specific threat taxonomy, the study evaluated the federated models in terms of detection accuracy, convergence time, model scalability, and communication overhead. Results indicate that FML models come close to having the same performance as their centralized counterparts while giving a major advantage when it comes to data sovereignty and system resiliency. The architecture further conveys a much-needed fill to blockchain security with an acute focus on aligning predictive analytics to decentralization and privacy. This framework stands as a resilient regulation-aware bedrock for digital assets and transaction security in modern financial milieu.