An analysis of SQL Injection and Cross-Site Scripting Attacks for Enhanced Security of the Website
Volume: 12 - Issue: 09 - Date: 01-09-2023
Approved ISSN: 2278-1412
Published Id: IJAECESTU162 | Page No.: 216-222
Author: Babul Kumar Thakur
Co- Author:
Abstract:- Website security is of paramount importance in today's digital landscape, where cyber threats
pose a constant challenge. Vulnerability scanning plays a critical role in identifying and mitigating potential
risks. In this paper, we introduce a novel approach that combines a Naive Bayes (NB) classifier with a
Neural Network (NN) to enhance the accuracy and efficiency of vulnerability scanning. Our proposed hybrid
method achieves a remarkable scanning time of 2.13, significantly reducing the time required for
comprehensive security assessments. We demonstrate the effectiveness of our approach by performing four
types of scanning, including tests for SQL injection, Cross-Site Scripting (XSS), and other vulnerabilities.
Through rigorous evaluation and real-world testing, we validate the superior performance of our hybrid
NB+NN method in identifying vulnerabilities, providing a robust solution to bolster website security in an
increasingly threat-prone environment.
Key Words:-Website Security Analysis, Sql Injection, Cross-Site Scripting, Webmining, Data Mining, Cybersecurity
Area:-Engineering
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