An Intelligent predictive model system for Real-Time identification and Risk Scoring of Web Application Vulnerabilities

CMR Institute of Technology Bengaluru, Karnataka, India
Presidency University Bengaluru, Karnataka, India
Presidency University Bengaluru, Karnataka, India
Presidency University Bengaluru, Karnataka, India
Presidency University Bengaluru, Karnataka, India

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Abstract

In this study, we designed and implemented a machine learning based system for detecting web application attacks using HTTP request behaviour. Unlike traditional signature-driven scanners, the proposed approach learns traffic characteristics from collected request logs and evaluates threat severity through a dynamic risk scoring model. Experiments were conducted on a dataset containing 10,000 real and synthetically generated requests covering SQL injection, cross-site scripting, and command injection attacks. The developed system achieved improved detection accuracy while reducing false alarms observed in rule-based security tools. The framework demonstrates the feasibility of integrating intelligent threat detection with automated risk prioritization for practical web security monitoring.