Computer Science ›› 2021, Vol. 48 ›› Issue (7): 70-76.doi: 10.11896/jsjkx.200600010

Special Issue: Artificial Intelligence Security

• Artificial Intelligence Security • Previous Articles     Next Articles

SQL Injection Attack Detection Method Based on Information Carrying

CHENG Xi, CAO Xiao-mei   

  1. School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
  • Received:2020-05-31 Revised:2020-09-19 Online:2021-07-15 Published:2021-07-02
  • About author:CHENG Xi,born in 1996,postgraduate.Her main research interests include Web security,machine learning.(
    CAO Xiao-mei,born in 1974,Ph.D.Her main research interests include wireless network security,mobile computing technology and security.

Abstract: At present,the accuracy of SQL injection attack detection based on traditional machine learning still needs to be improved.The main reason behind this phenomenon is that if too many features are selected when extracting feature vectors,it will cause the overfitting of the model and negatively affect the efficiency of the algorithm,whereas a large number of false and missed number will be generated if too little features are selected.To solve this problem,the paper proposes SQLIA-IC,a SQL injection attack detection method based on information carrying.The SQLIA-IC adds a marker and content matching module on the basis of machine learning detection.The marker is used to detect sensitive information in the sample,and the content matching module is used to match the feature items of the sample to achieve the purpose of secondary judgment.In order to improve the efficiency of SQL injection attack detection,the information value is used to simplify the detection results of machine learning and markers.In the content matching module,the dynamic matching is performed according to the information value carried by the sample.The simulation experiment results show that compared with the traditional machine learning methods,the accuracy rate of the method proposed in this paper is 2.62% higher on average,the precision ratio is 4.35% higher on average,the recall rate is 0.96%higheron average while the time loss has only increased by about 5 ms,which reveals that the method proposed can detect SQL injection attacks efficiently and effectively.

Key words: Feature matching, Information carrying, Intrusion detection, Machine learning, SQL injection attack

CLC Number: 

  • TP181
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