Affiliation:
1. University of Electronic Science and Technology of China, Chengdu, China
2. The Hong Kong Polytechnic University, Hong Kong, China
3. University of Guelph, Guelph, Canada
Abstract
Tokens have become an essential part of blockchain ecosystem, so recognizing token transfer behaviors is crucial for applications depending on blockchain. Unfortunately, existing solutions cannot recognize token transfer behaviors accurately and efficiently because of their incomplete patterns and inefficient designs. This work proposes
TokenAware
, a novel online system for recognizing token transfer behaviors. To improve accuracy,
TokenAware
infers token transfer behaviors from modifications of internal bookkeeping of a token smart contract for recording the information of token holders (e.g., their addresses and shares). However, recognizing bookkeeping is challenging, because smart contract bytecode does not contain type information.
TokenAware
overcomes the challenge by first learning the instruction sequences for locating basic types and then deriving the instruction sequences for locating sophisticated types that are composed of basic types. To improve efficiency,
TokenAware
introduces four optimizations. We conduct extensive experiments to evaluate
TokenAware
with real blockchain data. Results show that
TokenAware
can automatically identify new types of bookkeeping and recognize 107,202 tokens with 98.7% precision.
TokenAware
with optimizations merely incurs 4% overhead, which is 1/345 of the overhead led by the counterpart with no optimization. Moreover, we develop an application based on
TokenAware
to demonstrate how it facilitates malicious behavior detection.
Funder
Hong Kong ITF Project
Research and Development Program of Shenzhen
Hong Kong RGC Projects
National Natural Science Foundation of China
National Key R&D Program of China
Natural Science Foundation of Sichuan Province
Publisher
Association for Computing Machinery (ACM)
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