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Journal of Information and Optimization Sciences cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Open Access Research Article

A novel deep learning technique for protecting web applications against XSS and SQLI attacks

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pp. 1911–1921Vol. 46Issue 6September 2025DOI: 10.47974/JIOS-2020XML
Received:
10 Dec 2024
Published Online:
30 Sep 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-2020
Pages:
1911–1921

Abstract

The proliferation of emerging technologies has increased vulnerabilities in web applications, making them susceptible to SQL injections and cross-site scripting attacks. Current detection methods often yield high false positive rates. This paper introduces a new technique that improves web application security by decoding and standardizing SQL and XSS_payloads. A hybrid deep learning model was proposed, combining CNN for feature extraction and LSTM for sequential data dependencies. This model achieved impressive results, with accuracies of 99.79% for SQL-XSS Payload, 99.94% for Testbed, and 99.24% for HTTP CSIC 2010 datasets, demonstrating its effectiveness over traditional machine learning methods.

Keywords

Subject Classifications

68T0568N1568M10

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