Review of SQLI detection strategies using machine learning

Abstract

Various platforms and web apps to deliver material via the Internet are becoming more widespread. Web-based technologies accept and store sensitive information from users. Because of their Internet connectivity, these systems and the databases they link to are vulnerable to various information security vulnerabilities. The most dangerous threats are denial of service (DoS) and SQL injection assaults. SQL Injection attacks are at the top of the list for web-based systems. In this type of attack, the perpetrator will take sensitive and classified information that might hurt a firm or enterprise. Depending on the conditions, the corporation may incur financial losses, have private information disclosed, and have its stock market value drop. This work uses machine learning-based classifiers such as MLP, Support Vector Machine, Logistic Regression, Naive Bayes, and Decision Tree to identify and detect SQL Injection attacks. For the SQLI dataset learning strategies, we examined all five algorithms using the Confusion Matrix, F1 Score, and Log Loss. We discuss the benefits and drawbacks of the proposed AI-based SQLI techniques. Finally, we talk about making SQLI reach its full potential through more research in the coming years.