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Author(s):

Darshan Karkar | Prof. Sweta Katariya
Keywords:
Abstract:
The Increasing Sophistication Of Web-based Attacks Such As SQL Injection, Cross-Site Scripting (XSS), And Emerging Zero-day Threats Has Necessitated The Evolution Of Intelligent And Adaptive Defence Mechanisms. This Research Presents The Design And Development Of A Hybrid Web Application Firewall That Integrates Both Signature-based And Anomaly-based Detection Techniques To Enhance Web Application Security. The Proposed System Leverages Machine Learning Algorithms For Anomaly Detection And Pattern-matching Methods For Known Attack Signatures, Achieving A Balanced Trade-off Between Accuracy And Efficiency. The Architecture Incorporates Real-time Traffic Monitoring, Feature Extraction, And Multi-layer Threat Analysis, Enabling Proactive Defence Against Evolving Attack Patterns. Experimental Evaluation Demonstrates That The Hybrid WAF Significantly Improves Detection Accuracy While Reducing False Positives Compared To Traditional Approaches. The System Also Exhibits Scalability And Adaptability For Modern Web Environments. This Study Contributes To The Advancement Of Intelligent WAF Architectures Capable Of Mitigating Both Known And Unknown Web Threats, Thereby Strengthening Overall Cybersecurity Resilience.