Automatic Source Code Vulnerability Detection, Classification, and Prioritization Using Deep Learning

The increasing complexity of modern software systems has elevated vulnerability detection to a critical challenge in software security. Current approaches predominantly focus on binary classification of vulnerabilities, which provides limited actionable intelligence for developers. This paper presents a comprehensive framework for multi-class vulnerability detection, classification, and prioritization specifically designed for PHP source code. We conduct a rigorous comparative analysis of three advanced neural architectures-Long Short-Term Memory (LSTM), Bayesian Neural Networks (BNN), and Autoencoders (AE)-trained on an extensive dataset of 13,000 samples. Our methodology introduces a novel prioritization metric that intelligently combines static code metrics with security severity assessments. Experimental results demonstrate that LSTM networks achieve superior performance with 94% classification accuracy, outperforming both BNN (84%) and AE (77%) architectures. The proposed multi-class approach shows only minimal accuracy degradation compared to binary classification systems while providing significantly more detailed vulnerability characterization. The prioritization framework effectively guides remediation efforts by identifying high-risk vulnerabilities, with 92% of critical-severity flaws correctly ranked in the top quintile. This work advances the state-of-the-art by demonstrating the feasibility of fine-grained vulnerability analysis through deep learning while maintaining high detection accuracy in real-world PHP applications.