李铭1,邢光升2,王芝辉2,王晓东2.SQL注入行为实时在线智能检测技术研究[J].湖南大学学报:自然科学版,2020,(8):31~41
SQL注入行为实时在线智能检测技术研究
Research on Real-time Online Intelligent Detection Technology of SQL Injection Behavior
  
DOI:
中文关键词:  SQL注入  实时检测  卷积神经网络  快速傅里叶变换
英文关键词:SQL injection  real-time detection  Convolutional Neural Networks(CNN)  Fast Fourier Transformation(FFT)
基金项目:
作者单位
李铭1,邢光升2,王芝辉2,王晓东2 (1. 国防科技大学 智能科学学院湖南 长沙 410073 2. 国防科技大学 计算机学院湖南 长沙 410073) 
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中文摘要:
      为了解决传统手段在实时高速网络流量环境下SQL注入行为检测准确度和效率之间无法达到较好平衡的问题,提出一种基于深度学习模型的SQL注入行为实时在线检测的方法,构建了以卷积神经网络为基础并引入快速傅里叶变换层的复合检测网络模型SQLNN,并以此模型为基础提出SQL注入行为在线检测与自适应训练框架. 该框架对于SQL注入语句的检测正确率达到了99.98%,每秒可完成对一万条左右含有SQL语句的数据包的检测,满足了SQL注入攻击的实时在线检测对检测准确度和效率的要求.
英文摘要:
      In order to solve the problem that traditional methods cannot achieve a good balance between the accuracy and efficiency of SQL injection behavior detection in the real-time high-speed network traffic environment, this paper proposes a method for real-time detection method of SQL injection behavior based on deep learning construction model, and constructs a detection network model called SQLNN based on Convolutional Neural Networks (CNN) and introduces a fast Fourier transform layer. Based on this model, an online detection and adaptive training framework for SQL injection behavior is proposed. For our detection framework, the detection accuracy of the SQL injection statements reaches 99.98%, and it can detect about 10 000 packets containing SQL statements per second. Therefore, it can satisfy the requirements of real-time online detection of SQL injection attacks for detection accuracy and efficiency.
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