SQL injection vulnerability analysis
1 Department of Computer Science and Engineering, Government Polytechnic Channasandra-560067, Karnataka, India
2 Department of Computer Science and Engineering, DACG, Government Polytechnic, Chikkamagaluru 577101, Karnataka, India
3 Department of Electronics and Communication Engineering, DACG, Government Polytechnic, Chikkamagaluru 577101, Karnataka, India
Research Article
World Journal of Advanced Research and Reviews, 2021, 09(01), 312–318
Article DOI: 10.30574/wjarr.2021.9.1.0018
Publication history:
Received on 13 January 2021; Revised 25 January 2021; accepted on 29 January 2021
Abstract:
Web applications are an integral part of today's digital landscape, serving various functions from e-commerce to social networking. However, they are also prime targets for cyber-attacks, with SQL-Injection vulnerabilities posing a significant threat to their security. This project addresses the critical issue of SQL-Injection vulnerabilities in web applications by offering a comprehensive analysis, leveraging Python and classical machine learning algorithms such as Naïve Bayes. The research method employed in this project involves procuring real-world datasets, conducting data pre-processing, and using decision tree classifiers. These steps collectively provide an automated and scalable solution for identifying, understanding, and mitigating SQL-Injection riskslearning methods like Deep Neural Networks (DNN). A comprehensive comparative analysis of these algorithms has been carried out, assessing their performance based on accuracy metrics.
Keywords:
SQL-Injection; vulnerabilities; web Application; Machine Learning; AI
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Copyright © 2021 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0