SecureCodeBERT: An Ai-Powered Model for Identifying and Categorizing High-Risk Security Vulnerabilities in Php-Based Critical Infrastructure Applications
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Abstract
Critical infrastructure systems extensively utilize PHP applications which face significant security challenges that traditional detection methods inadequately address. This paper presents SecureCodeBERT, a specialized transformer-based model for detecting and classifying high-risk security vulnerabilities in PHP applications deployed within critical infrastructure environments. The architecture incorporates PHP-specific adaptations through specialized tokenization strategies and contextual code understanding mechanisms. A comprehensive multi-stage detection framework combines syntactic parsing, semantic analysis, and contextual vulnerability pattern recognition to identify complex exploitation vectors. The multi-level classification system categorizes vulnerabilities based on both technical severity and operational impact, enabling prioritized remediation. Experimental evaluation on a dataset comprising 140 applications across five critical infrastructure sectors demonstrates SecureCodeBERT's superior performance with precision rates of 0.892 and recall rates of 0.867, representing significant improvements over traditional static analysis tools (+21.0%) and generic code analysis models (+7.6%). Sector-specific vulnerability pattern analysis reveals distinct security challenges across energy management, healthcare, financial services, transportation, and water management applications. Case studies validate the model's effectiveness in production environments, demonstrating particular strengths in detecting sophisticated authentication bypass, SQL injection, and command injection vulnerabilities that conventional tools frequently miss.
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