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Volume 12 Issue 07
July 2026
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Abstract
SQL Injection Remains One Of The Most Critical Security Threats To Web Applications, Enabling Attackers To Manipulate Database Queries And Gain Unauthorized Access To Sensitive Information. Traditional Detection Methods Often Fail To Identify Complex And Evolving Attack Patterns. This Paper Proposes An Intelligent SQL Injection Detection System Using A Cost-sensitive Stacked Generalization Learning (CSSGL)-based Hybrid Approach That Integrates Machine Learning And Deep Learning Techniques. The Proposed Approach Analyzes Query Structures, Performs Feature Extraction, And Classifies Queries As Normal Or Malicious With High Accuracy. Experimental Results Demonstrate That The Model Achieves An Accuracy Of 96.8% With Reduced False Positive Rates, Outperforming Conventional Detection Methods. The System Is Capable Of Detecting Both Known And Unknown Attacks Efficiently, Making It Suitable For Real-time Web Application Security.
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Paper ID
IJSARTV12I5105276
Publication Date
May 5, 2026
Research Area
Computer Science And Engineering Specializing In Cyber Security