SQL injection

Search engine

Data mining

Computer science

Dropout (neural networks)

Web search query

Database

Mathematics

Stored procedure

Query by Example

Overfitting

Mathematical analysis

Artificial intelligence

Information retrieval

Artificial neural network

Generalization

Machine learning

Security and Communication Networks

Among the network security problems, SQL injection is a common and challenging network attack means, which can cause inestimable loop-breaking and loss to the database, and how to detect SQL injection statements is one of the current research hotspots. Based on the data characteristics of SQL statements, a deep neural network-based SQL injection detection model and algorithm are built. The core method is to convert the data into word vector form by word pause method, then form a sparse matrix and pass it into the model for training, build a multihidden layer deep neural network model containing ReLU function, optimize the traditional loss function, and introduce Dropout method to improve the generalization ability of this model. The accuracy of the final model is maintained at over 96%. By comparing the experimental results with traditional machine learning algorithms and LSTM algorithms, the proposed algorithm effectively solves the problems of overfitting in machine learning and the need for manual screening to extract features, which greatly improves the accuracy of SQL injection detection.

Deep Neural Network-Based SQL Injection Detection Method