Zareena Begum et al 84-98 Reinforcing Web Application Security: A Modified Scheme against SQL Injection Attacks

Authors

  • Zareena Begum, Peddapally Sravani, Mamidi Sai Priya, Poddar Nandini Kumari, Gajjala Rakesh

Keywords:

Keywords: SQL Injection, Web application, Cyber Security, Logistic Regression, DNN, Attacks.

Abstract

Web application security remains a critical concern as cyberattacks, particularly SQL injection attacks,continue to rise. According to recent reports, over 65% of web applications are vulnerable to SQLinjection, and nearly 30% of data breaches involve SQL-based attacks. Traditional manual detectionmethods are inefficient due to their reliance on static rules and regular expressions, which fail to adapt to evolving attack patterns.

References

Martins, N.; Cruz, J.M.; Cruz, T.; Abreu, P.H. Adversarial Machine Learning Applied to Intrusion and Malware Scenarios: ASystematic Review. IEEE Access 2020,8, 35403–35419.

Mishra, P.; Varadharajan, V.; Tupakula, U.; Pilli, E.S. A Detailed Investigation and Analysis of using Machine Learning Techniquesfor Intrusion Detection. IEEE Commun. Surv. Tutor. 2018,21, 686–728.

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Published

2025-04-16

How to Cite

Zareena Begum, Peddapally Sravani, Mamidi Sai Priya, Poddar Nandini Kumari, Gajjala Rakesh. (2025). Zareena Begum et al 84-98 Reinforcing Web Application Security: A Modified Scheme against SQL Injection Attacks . Journal of Computational Analysis and Applications (JoCAAA), 34(4), 84–98. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2277

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