SCAMM: detection and prevention of SQL injection attacks using a machine learning approach

Abstract

Importance of cyber-security in protecting our valuable data and information is huge in this era of technology. Since numerous amounts of cyber-attacks take place every day, the development of a more secured system so that it can predict and stop cyber-attacks from happening, has been our concern for years. This research paper is focused on developing such a means that will be able to detect and prevent SQL Injection Attack successfully. SQL Injection attack is a type of cyber-attack that uses malicious SQL queries for internal data manipulation and retrieving hidden information from the back-end database that were not intended to be displayed. SQL Injection Attack even makes a database vulnerable to other kinds of attacks. Since most of the organizations use a SQL based back end database to store data, all of their data is exposed to a simple form of attack if they are not properly defended. The aim of this research is to develop a model by finding out the best machine learning algorithm to predict and prevent SQL Injection Attack. A brief explanation of our work plan, our experimentation and the results of our experiments are discussed in this paper.