Proceedings of the MULTINOVA: First International Conference on Artificial Intelligence in Engineering, Healthcare and Sciences (ICAIEHS- 2025)

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Enhancing Web Security Detection with AI-Driven Mitigation Techniques

Authors
Muhammed Ismaeel1, *, Prashant Lokhande2, Bandanawaz Kotiyal3
1Computer Department, Pillai College of Engineering, New Panvel, Mumbai, Maharashtra, India, 410206
2Information Technology Department, Pillai College of Engineering, New Panvel, Mumbai, Maharashtra, 410206, India
3Professor, Electronics and Computer Science Engineering Department, Anjuman-I-Islam, Kalsekar Technical Campus, New Panvel, Mumbai, Maharashtra, 410206, India
*Corresponding author. Email: sismaeel23mtcomp@student.mes.ac.in
Corresponding Author
Muhammed Ismaeel
Available Online 7 October 2025.
DOI
10.2991/978-94-6463-852-3_12How to use a DOI?
Keywords
Web Application Security; Vulnerability Scanner; AI-Powered Mitigation; Command Injection; OWASP ZAP
Abstract

Web applications face constant threats from cyber attackers exploiting vulnerabilities like Command Injection, and Outdated Components. There are several popular scanners for finding vulnerabilities, but they do not often find these two OWASP TO 10 vulnerabilities. In this research work, our proposed algorithm effectively identifies vulnerabilities like these scanners but achieves 100% accuracy. The system is designed to detect 2 types of Top 10 OWASP vulnerabilities: Command Injection, and Outdated Components, ensuring comprehensive security analysis. The proposed algorithm integrates Naïve HTML Parsing & Heuristic-Based Detection, and Naïve Vulnerability Checking in JavaScript Libraries techniques to automate the scanning process. Additionally, it leverages the Gemini API to provide AI-powered mitigation strategies. Identified vulnerabilities are displayed on-screen. Experimental results confirm 100% detection accuracy, validated through manual penetration testing. The system also consumes less memory than the base paper algorithm, making it highly efficient for resource-constrained environments. By integrating AI-driven mitigation with automated vulnerability detection, the proposed system enhances web security while minimizing response time. Future improvements will focus on more OWASP TOP 10 Vulnerabilities with 100% accuracy using naive techniques/algorithms and lightweight cybersecurity solution for modern web applications.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the MULTINOVA: First International Conference on Artificial Intelligence in Engineering, Healthcare and Sciences (ICAIEHS- 2025)
Series
Advances in Intelligent Systems Research
Publication Date
7 October 2025
ISBN
978-94-6463-852-3
ISSN
1951-6851
DOI
10.2991/978-94-6463-852-3_12How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Muhammed Ismaeel
AU  - Prashant Lokhande
AU  - Bandanawaz Kotiyal
PY  - 2025
DA  - 2025/10/07
TI  - Enhancing Web Security Detection with AI-Driven Mitigation Techniques
BT  - Proceedings of the MULTINOVA: First International Conference on Artificial Intelligence in Engineering, Healthcare and Sciences (ICAIEHS- 2025)
PB  - Atlantis Press
SP  - 181
EP  - 194
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-852-3_12
DO  - 10.2991/978-94-6463-852-3_12
ID  - Ismaeel2025
ER  -