基于Bi-LSTM模型的恶意JavaScript代码检测方法  

MALICIOUS JAVASCRIPT CODE DETECTION METHOD BASED ON BI-LSTM MODEL

作　　者：纪育青 方艳红[1,2] 谭顺华[1] 王学渊[1,2] Ji Yuqing;Fang Yanhong;Tan Shunhua;Wang Xueyuan(School of Information Engineering,Southwest University of Science and Technology,Mianyang 621010,Sichuan,China;Key Laboratory of Robot Technology Used for Special Environment of Sichuan Province,Southwest University of Science and Technology,Mianyang 621010,Sichuan,China)

机构地区：[1]西南科技大学信息工程学院,四川绵阳621010 [2]西南科技大学特殊环境机器人技术四川省重点实验室,四川绵阳621010

出　　处：《计算机应用与软件》2024年第9期357-362,共6页Computer Applications and Software

基　　金：国家自然科学基金项目(61771411)。

摘　　要：传统的静态检测恶意JavaScript代码方法十分依赖于已有的恶意代码特征,无法有效提取混淆恶意代码特征,导致检测混淆恶意JavaScript代码的精确率低。针对该问题提出基于双向长短期记忆网络(Bidirectional Long Short-term Memory, Bi-LSTM)的恶意代码检测模型。通过抽象语法树将JavaScript代码转化为句法单元序列,通过Doc2Vec算法将句法单元序列用分布式向量表示,将句向量矩阵送入Bi-LSTM模型进行检测。实验结果表明,该方法对于混淆恶意JavaScript代码具有良好的检测效果且检测效率高,准确率为97.03%,召回率为97.10%。syntax tree,and used the Doc2Vec algorithm to represent the syntactic unit sequence with distributed vectors.The sentence vector matrix was sent to the Bi-LSTM model for detection.The experimental results show that this method has good detection effect and high detection efficiency for obfuscated malicious JavaScript code,with the accuracy rate of 97.03%and the recall rate of 97.10%.

关 键 词：恶意JavaScript代码检测 Bi-LSTM 深度学习 Doc2Vec 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]