Paper 2023/592
Blockchain Large Language Models
Abstract
This paper presents a dynamic, real-time approach to detecting anomalous blockchain transactions. The proposed tool, BlockGPT, 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, BlockGPT 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 BlockGPT through its use as an anomaly detection tool for Ethereum transactions. In our experiments, it effectively identifies abnormal transactions among a dataset of 68M transactions and has a batched throughput of 2284 trans- actions per second on average. Our results show that, BlockGPT identifies abnormal transactions by ranking 49 out of 124 attacks among the top-3 most abnormal transactions interacting with their victim contracts. This work makes contributions to the field of blockchain transaction analysis by introducing a custom data encoding compatible with the transformer architecture, a domain-specific tokenization technique, and a tree encoding method specifically crafted for the Ethereum Virtual Machine (EVM) trace representation.
Metadata
- Available format(s)
-
PDF
- Category
- Applications
- Publication info
- Preprint.
- Keywords
- blockchainlarge language modelintrusion detectionintrusion prevention
- Contact author(s)
- lzhou1110 @ gmail com
- History
- 2023-04-29: last of 3 revisions
- 2023-04-25: received
- See all versions
- Short URL
- https://ia.cr/2023/592
- License
-
CC BY-NC-ND
BibTeX
@misc{cryptoeprint:2023/592,
author = {Yu Gai and Liyi Zhou and Kaihua Qin and Dawn Song and Arthur Gervais},
title = {Blockchain Large Language Models},
howpublished = {Cryptology ePrint Archive, Paper 2023/592},
year = {2023},
note = {\url{https://eprint.iacr.org/2023/592}},
url = {https://eprint.iacr.org/2023/592}
}
