Analyzing SQL payloads using logistic regression in a big data environment

Abstract Protecting big data from attacks on large organizations is essential because of how vital such data are to organizations and individuals. Moreover, such data can be put at risk when attackers gain unauthorized access to information and use it in illegal ways. One of the most common such attacks is the structured query language injection attack (SQLIA). This attack is a vulnerability attack that allows attackers to illegally access a database quickly and easily by manipulating structured query language (SQL) queries, especially when dealing with a big data environment. To address these risks, this study aims to build an approach that acts as a middle protection layer between the client and database server layers and reduces the time consumed to classify the SQL payload sent from the user layer. The proposed method involves training a model by using a machine learning (ML) technique for logistic regression with the Spark ML library that handles big data. An experiment was conducted using the SQLI dataset. Results show that the proposed approach achieved an accuracy of 99.04, a precision of 98.87, a recall of 99.89, and an F-score of 99.04. The time taken to identify and prevent SQLIA is 0.05 s. Our approach can protect the data by using the middle layer. Moreover, using the Spark ML library with ML algorithms gives better accuracy and shortens the time required to determine the type of request sent from the user layer.