Research on Semantic Automated Transformation of Automotive Vulnerabilities Based on NLP

The rapid development of automotive intelligence and networking has rendered traditional manual analysis methods insufficient to meet the growing demand for vulnerability analysis. This paper proposes a semantic automated transformation method for automotive vulnerabilities based on Natural Language Processing (NLP), aiming to enhance the efficiency of vulnerability analysis and remediation. The method conducts deep semantic analysis on the natural language descriptions in vulnerability reports, automatically extracting key features and converting them into standardized, actionable formats. The research employs NLP models based on the Transformer architecture, including data preprocessing, model construction, and optimization. An end-to-end vulnerability semantic transformation model was constructed. Experimental results demonstrate that this method achieves high accuracy and efficiency in vulnerability feature extraction and semantic transformation, improving efficiency by 30% compared to traditional vulnerability analysis methods. The study provides a new technical path for enhancing automotive software security, significantly contributing to the safe development of the automotive industry.