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Detecting security vulnerabilities in software before they are exploited hasbeen a challenging problem for decades. Traditional code analysis methods havebeen proposed, but are often ineffective and inefficient. In this work, wemodel software vulnerability detection as a natural language processing (NLP)problem with source code treated as texts, and address the automated softwarevenerability detection with recent advanced deep learning NLP models assistedby transfer learning on written English. For training and testing, we havepreprocessed the NIST NVD/SARD databases and built a dataset of over 100,000files in $C$ programming language with 123 types of vulnerabilities. Theextensive experiments generate the best performance of over 93\% accuracy indetecting security vulnerabilities.

Security Vulnerability Detection Using Deep Learning Natural Language Processing