SQL INJECTION PREDICTION USING MACHINE LEARNING

Authors

  • Doma Vinay Student, Department Of Electronics And Computer Engineering, JB Institute Of Engineering And Technology, Moinabad,Telangana, India Author
  • Sepoor Vinay Goud Student, Department Of Electronics And Computer Engineering, JB Institute Of Engineering And Technology, Moinabad,Telangana, India Author
  • Kuruva Deeksha Student, Department Of Electronics And Computer Engineering, JB Institute Of Engineering And Technology, Moinabad,Telangana, India Author
  • Mrs.M.Anusha 4Assistant Professor, Department Of Electronics And Computer Engineering, JB Institute Of Engineering And Technology Moinabad, Telangana, India. Author

Abstract

The web is currently the most reliable and popular form of commercial and personal
communication. On the web, users load millions of gigabytes of data every day through a variety of routes, and
user input might be malevolent. As a result, security becomes a crucial component of web applications. Due to
their accessibility, they are vulnerable to several flaws that, if ignored, might be harmful. These gaps are used by
the attackers to engage in a variety of illicit operations that allow them to get unauthorized access. One such
attack that is simple to carry out but challenging to detect due to its various forms and channels is SQL Injection.
This might lead to theft, a data breach, or property loss. The suggested classifier combines a role-based access
control system for detection with the Naive Bayes machine learning method. On the basis of test cases drawn
from the three SQLIA attacks comments, union, and tautology the suggested model is put to the test.

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Published

2024-03-25

Issue

Vol. 9 No. 3 (2024): March

Section

Articles

How to Cite

SQL INJECTION PREDICTION USING MACHINE LEARNING. (2024). International Journal of Multidisciplinary Engineering In Current Research, 9(3), 15-24. https://ijmec.com/index.php/multidisciplinary/article/view/428