Cybersecurity: Using Machine Learning Algorithms to Detect SQL Injection Attacks




Subburaj, Vinitha Hannah
Pham, Binh A.

Journal Title

Journal ISSN

Volume Title


WTAMU Cornette Library


Cybersecurity is a prevailing issue across the nation. In the twenty-first century, almost everyone around the globe is using at least one of the internet websites that contain his/her private information. Since privacy concerns us, this research effort focuses on one of the most recent cyber-attacks which is the SQL injection (SQLi) attacks. As a result of SQL injection attack on websites, data could be destroyed, stolen, or manipulated. SQL injection attacks are done by injecting despicable SQL statements through the entry field of the website or the application; thus, manipulating the database. SQL injection attacks had proven their danger on several website types such as social media, e-shopping, etc... In order to prevent such attacks from occurring, this research effort investigates on efficient ways of detection and prevention, so we can preserve each cyber-user’s right to privacy. This research effort is aimed at investigating and looking at different ways to protect websites from SQL injection attacks. In this research effort, machine learning algorithms were used to detect such SQLi attacks. Machine Learning (ML) algorithms are algorithms that can learn from the data provided and infer interesting results from the dataset. We have used SQL code as our data and ML algorithms to detect malicious code. The machine learning model developed in this research can detect such attacks from happening in the future. The precision and accuracy of the machine learning algorithms in terms of predicting the SQLi attacks have been calculated and reported in this research paper.


Expected findings: A software module that predicts and prevents cyber attacks.



Permalink for this item. Use this when sharing or citing this source.