TokenAware: Accurate and Efficient Bookkeeping Recognition for Token Smart Contracts

Zheyuan He, Shuwei Song, Yang Bai, Xiapu Luo, Ting Chen, Wensheng Zhang, Peng He, Hongwei Li, Xiaodong Lin, Xiaosong Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

7 Citations (Scopus)

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.
Original languageEnglish
Article number26
Pages (from-to)1–35
JournalACM Transactions on Software Engineering and Methodology (TOSEM)
Volume32
Issue number1
Publication statusPublished - 13 Feb 2023

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