Traditional vulnerability detection methods have limitations due to their need for extensive
manual labor. Using automated means for vulnerability detection has attracted research …

As software vulnerabilities grow in volume and complexity, researchers proposed various
Artificial Intelligence (AI)-based approaches to help under-resourced security analysts to …

The use of learning-based techniques to achieve automated software vulnerability detection
has been of longstanding interest within the software security domain. These data-driven …

With the rapid growth of social media platforms, digitization of official records, and digital
publication of articles, books, magazines, and newspapers, lots of data are generated every …

Deep learning-based vulnerability prediction approaches are proposed to help under-
resourced security practitioners to detect vulnerable functions. However, security …

In order to secure software, it is critical to detect potential vulnerabilities. The performance of
traditional static vulnerability detection methods is limited by predefined rules, which rely …

With the increase in software vulnerabilities that cause significant economic and social
losses, automatic vulnerability detection has become essential in software development and …

Vulnerability detection is essential to protect software systems. Various approaches based
on deep learning have been proposed to learn the pattern of vulnerabilities and identify …

Security vulnerabilities in modern software are prevalent and harmful. While automated
vulnerability detection tools have made promising progress, their scalability and applicability …

Software system complexity and security vulnerability diversity are plausible sources of the
persistent challenges in software vulnerability research. Applying deep learning methods for …