Detecting SQL Injection and XSS Attack

Vijla, Nitin Rajesh (2024) Detecting SQL Injection and XSS Attack. Masters thesis, Dublin, National College of Ireland.

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Abstract

This study aims to enhance the identification of XSS and SQL injection as threats by developing a CNN-LSTM model. XSS and SQL injection threats remain apparent when it comes to the security of web applications. Accordingly, for enhancing the detection accuracy and the model’s ability to maintain invariance, CNNs are adopted for performing local feature extraction from the input sequences and LSTMs for modeling sequential relationships. As a result, the study works out and compares the model most notably with the tests on the accuracy and the real-time responses. The study shows that these techniques have significant improvements in recognizing such assaults in contrast to the conventional approaches, that is, devising a more reliable method to protect the web-based applications against new and developing threats from hackers.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Pantridge, Michael UNSPECIFIED
Uncontrolled Keywords:	CNN; RNN; XSS; SQL; LSTMs
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Ciara O'Brien
Date Deposited:	31 Jul 2025 13:18
Last Modified:	31 Jul 2025 13:18
URI:	https://norma.ncirl.ie/id/eprint/8384

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