Detecting Ethereum Ponzi Schemes Through Opcode Context Analysis and Oversampling-Based AdaBoost Algorithm  

作　　者：Mengxiao Wang Jing Huang 

机构地区：[1]Faculty of Information Technology,Beijing University of Technology,Beijing,100124,China [2]Beijing Key Laboratory of Computational Intelligence and Intelligence System,Beijing,100124,China

出　　处：《Computer Systems Science & Engineering》2023年第10期1023-1042,共20页计算机系统科学与工程（英文）

基　　金：This work was supported by National Key R&D Program of China(Grant Numbers 2020YFB1005900,2022YFB3305802).

摘　　要：Due to the anonymity of blockchain,frequent security incidents and attacks occur through it,among which the Ponzi scheme smart contract is a classic type of fraud resulting in huge economic losses.Machine learningbased methods are believed to be promising for detecting ethereum Ponzi schemes.However,there are still some flaws in current research,e.g.,insufficient feature extraction of Ponzi scheme smart contracts,without considering class imbalance.In addition,there is room for improvement in detection precision.Aiming at the above problems,this paper proposes an ethereum Ponzi scheme detection scheme through opcode context analysis and adaptive boosting(AdaBoost)algorithm.Firstly,this paper uses the n-gram algorithm to extract more comprehensive contract opcode features and combine them with contract account features,which helps to improve the feature extraction effect.Meanwhile,adaptive synthetic sampling(ADASYN)is introduced to deal with class imbalanced data,and integrated with the Adaboost classifier.Finally,this paper uses the improved AdaBoost classifier for the identification of Ponzi scheme contracts.Experimentally,this paper tests our model in real-world smart contracts and compares it with representative methods in the aspect of F1-score and precision.Moreover,this article compares and discusses the state of art methods with our method in four aspects:data acquisition,data preprocessing,feature extraction,and classifier design.Both experiment and discussion validate the effectiveness of our model.

关 键 词：Blockchain smart Ponzi scheme N-GRAM OVERSAMPLING ensemble learning 

分 类 号：TP31[自动化与计算机技术—计算机软件与理论]