典型文献
Performance Analysis of Cross?Site Scripting Based on Natural Language Processing
文献摘要：
With the acceleration of network communication in the 5G era, the volume of data communicationin cyberspace has increased unprecedentedly. The speed of data transmission will accelerate. Subsequently, the security of network communication data becomes more and more serious. Among them, malicious cross?site scripting leading to the leakage of user information is very serious. This article uses URL attribute analysis method and YARA rule to process data for cross?site scripting based on the long short?term memory ( LSTM) characteristics of LSTM model. The results show that the LSTM classification model adopted in this paper has higher recall rate and F1?score than other machine learning methods, which proves that the method adopted in this paper is feasible.
文献关键词：
中图分类号：
作者姓名：
Mengda Xu;Luqun Li
作者机构：
The College of Information,Mechanical and Electrical Engineering,Shanghai Normal University,Shanghai 200234,China
文献出处：
引用格式：
[1]Mengda Xu;Luqun Li-.Performance Analysis of Cross?Site Scripting Based on Natural Language Processing)[J].哈尔滨工业大学学报（英文版）,2022(04):19-25
A类：
communicationin,scripting,YARA
B类：
Performance,Analysis,Cross,Site,Scripting,Based,Natural,Language,Processing,With,acceleration,network,volume,data,cyberspace,has,increased,unprecedentedly,speed,transmission,will,accelerate,Subsequently,security,becomes,more,serious,Among,them,malicious,cross,site,leading,leakage,user,information,very,This,article,uses,URL,attribute,analysis,rule,process,long,short,term,memory,characteristics,model,results,show,that,classification,adopted,this,paper,higher,recall,score,than,other,machine,learning,methods,which,proves,feasible
AB值：
0.611362
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