摘要
针对当前检测方法准确率不高以及模型泛化性较差的问题,提出了基于KOLSTM深度学习模型的蜜罐陷阱合约检测方法。首先,通过分析蜜罐陷阱合约的特点,提出了关键操作码的概念,并设计了可用于选取智能合约中关键操作码的关键词提取方法;其次,在传统的LSTM模型中加入关键操作码权重机制,构建了可以同时捕获蜜罐陷阱合约中隐藏的序列特征以及关键操作码特征的KOLSTM模型。最后,通过实验表明,该模型具有较高的识别精确率,在二分类和多分类检测场景下的F值较LightGBM模型分别提升2.39%与19.54%。
Aiming at the problems of low accuracy of current detection methods and poor generalization of model,a honeypot contract detection method based on KOLSTM deep learning model was proposed.Firstly,by analyzing the characteristics of honeypot contract,the concept of key opcode was proposed,and a keyword extraction method which could be used to select the key opcode in smart contract was designed.Secondly,by adding the key opcode weight mechanism to the traditional LSTM model,a KOLSTM model which could simultaneously capture the sequence features and key opcode features hidden in the honeypot contract was constructed.Finally,the experimental results show that the model had a high recognition accuracy.Compared with the existing methods,the F-score is improved by 2.39%and 19.54%respectively in the two classification and multi-classification detection scenes.
作者
张红霞
王琪
王登岳
王奔
ZHANG Hongxia;WANG Qi;WANG Dengyue;WANG Ben(Qingdao Institute of Software,College of Computer Science and Technology,China University of Petroleum,Qingdao 266580,China)
出处
《通信学报》
EI
CSCD
北大核心
2022年第1期194-202,共9页
Journal on Communications
基金
中石油重大科技基金资助项目(No.ZD2019-183-004)
中央高校基本科研业务费专项资金资助项目(No.20CX05019A)。
