An Efficient SQL Injection Detection System Using Deep Learning

An Efficient SQL Injection Detection System Using Deep Learning

연구 분야: Databases

학회: 2021 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)

SQL Injection makes most of the applications that are based on different types of databases be it used in any devices vulnerable to cyber threat. SQL Injection is said to be one of the top most threat that database-based applications on the web. SQL Injection makes all the user‘s information present in the database vulnerable and the user‘s data may be either sold in black market or may be misused. The disadvantages of previously implemented SQLI model is that they will not know how will they be able to categorize new patterns, they will only be able to detect the patterns which they have experienced before or trained on, But our model will be able to identify whether the data entered is SQL injected or not identifying patterns in the input. The advantages to our system will be that it will be able to detect all and every type of Injection techniques. All the feature extraction and selection will be done by the model itself. Just the user should need to enter the text. It is also scalable and can extend it to a wide variety of applications. With the help of MLP model, we have achieved a cross-validated accuracy of 98% with a precision of 98% and recall of 97%.