A CNN-based Approach to the Detection of SQL Injection Attacks
Ao Mei Luo, Wei Feng Huang, Wenqing Fan · 2019
SQL injection has always been a major threat in the field of web application security. Traditional methods such as the rule-matching-based SQL injection detection solutions, which are inefficient to cope with the ever-changing SQL injection techniques and there is always a risk of bypassing variants. In this paper, we extract SQL injection attack related payloads from network flow and propose a SQL injection detection model based on Convolutional Neural Network (CNN), which can take the advantages of high-dimensional features of SQL injection behavior to deal with this issue. The proposed approach was tested in a real-traffic case study along with ModSecurity, which is the representative rule-matching-based method. The experimental results show that the CNN based model has higher accuracy, precision and recall rate, which validate its detection effectiveness and robustness against obfuscation of attacks.