Security Vulnerability Detection Using Deep Learning Natural Language Processing

Autor:	Ziems, Noah, Wu, Shaoen
Rok vydání:	2021
Předmět:	Computer Science - Cryptography and Security Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	Detecting security vulnerabilities in software before they are exploited has been a challenging problem for decades. Traditional code analysis methods have been proposed, but are often ineffective and inefficient. In this work, we model software vulnerability detection as a natural language processing (NLP) problem with source code treated as texts, and address the automated software venerability detection with recent advanced deep learning NLP models assisted by transfer learning on written English. For training and testing, we have preprocessed the NIST NVD/SARD databases and built a dataset of over 100,000 files in $C$ programming language with 123 types of vulnerabilities. The extensive experiments generate the best performance of over 93\% accuracy in detecting security vulnerabilities. Comment: IEEE INFOCOM
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2105.02388 Zobrazit plný text záznamu View this record from Arxiv