BBVD: A BERT-based Method for Vulnerability Detection
Wei‐Chang Huang, Shuyuan Lin, Chen Li
2022International Journal of Advanced Computer Science and Applications15 citationsDOIOpen Access PDF
Abstract
Software vulnerability detection is one of the key tasks in the field of software security. Detecting vulnerability in the source code in advance can effectively prevent malicious attacks. Traditional vulnerability detection methods are often ineffective and inefficient when dealing with large amounts of source code. In this paper, we present the BBVD approach, which treats high-level programming languages as another natural language and uses BERT-based models in the natural language processing domain to automate vulnerability detection. Our experimental results on both SARD and Big-Vul datasets demonstrate the good performance of the proposed BBVD in detecting software vulnerability.
Topics & Concepts
Computer scienceVulnerability (computing)Vulnerability managementSoftwareKey (lock)Source codeCode (set theory)Field (mathematics)Domain (mathematical analysis)Vulnerability assessmentSecure codingComputer securitySoftware security assuranceProgramming languageInformation securityPure mathematicsMathematicsMathematical analysisPsychological resiliencePsychologyPsychotherapistSecurity serviceSet (abstract data type)Software Engineering ResearchSoftware Reliability and Analysis ResearchWeb Application Security Vulnerabilities