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SQL Injection Detection for Web Applications Based on Elastic-Pooling CNN
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DOI：10.1109/ACCESS.2019.2947527.png)
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Abstract
En 中文
An enterprises data can be one of its most important assets and often critical to the firms development and survival. SQL injection attack is ranked first in the top ten risks to network applications by the Open Web Application Security Project (OWASP). Its harmfulness, universality, and severe situation are self-evident. This paper presents a method of SQL injection detection based on Elastic-Pooling CNN (EP-CNN) and compares it with traditional detection methods. This method can output a fixed two-dimensional matrix without truncating data and effectively detects the SQL injection of web applications. Based on the irregular matching characteristics, it can identify new attacks and is harder to bypass.
Keywords:
SQL injection
Convolution
Training
Feature extraction
Kernel
Databases
Data preprocessing
Deep learning
neural network
CNN
network security
SQL injection
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