Work

Security Vulnerability Detection Using Deep Learning Natural Language Processing

Year: 2021

Type: conference-paper

Abstract: 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 auto-mated 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.

Source: IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

DOI: 10.1109/infocomwkshps51825.2021.9484500

Authors: Noah Ziems, Shaoen Wu

Institutions: Ball State University, Illinois State University

Language: English

Topic: Software Engineering Research

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Cisco Systems

Awards: RUI: SpecEES: Collaborative Research: Enabling Secure, Energy-Efficient, and Smart In-Band Full Duplex Wireless, G3996949249