Cloud Engineering-based on Machine Learning Model for SQL Injection Attack
Kavita Singh, Sakshi Hemant Kokardekar, Gunjan Khonde, Prajakta Dekate, Nishita Badkas, Sagar Lachure · 2023
The entire IT sector has been altered by Cloud Engineering, which is also continually coming up with new, creative solutions to everyday issues. A systematic method for the commoditization, standardization, and governance of cloud computing applications is provided by cloud engineering, which is the application of the engineering discipline to cloud computing. These days, a lot of businesses and individual users keep enormous amounts of data on the cloud. The information in the data could be sensitive or could be pertinent to the client or the user. Through a variety of harmful methods, the attackers attempt to obtain private data. One such attack is a SQL Injection Attack, in which a hostile user attempts to get unauthorized access to sensitive cloud data by injecting malicious SQL queries. In this paper, a machine learning model is proposed to identify this kind of attack. The Count-Vectorizer approach and the TF-IDF Vectorizer approach are used to test 3 different machine learning models on two different datasets, one with 4200 data items and the other with 30100 data items, both of which contain SQL queries. The models were built using the Random Forest, XgBoost, and Extra Trees Classifier algorithms, respectively. After deploying the models to the test, it was found that with the test size 0.2, Extra trees classifier shows the highest accuracy on dataset of 4200 entries on both the approaches TF-IDF Vectorizer (TV) and Count Vectorizer (CV) and for the dataset of 30100 entries the XgBoost Classifier gives high accuracy with Count Vectorizer approach while XgBoost gives highest accuracy with TF-IDF Vectorizer approach.