Blockchain Large Language Models. (arXiv:2304.12749v1 [cs.CR])

This paper presents a dynamic, real-time approach to detecting anomalous
blockchain transactions. The proposed tool, TXRANK, generates tracing
representations of blockchain activity and trains from scratch a large language
model to act as a real-time Intrusion Detection System. Unlike traditional
methods, TXRANK is designed to offer an unrestricted search space and does not
rely on predefined rules or patterns, enabling it to detect a broader range of
anomalies. We demonstrate the effectiveness of TXRANK through its use as an
anomaly …

act anomaly detection blockchain detect detection dynamic ethereum intrusion intrusion detection intrusion detection system language language models large large language model offer patterns rules search space system tool tracing trains transactions