We review machine learning approaches for detecting (and correcting) vulnerabilities in
source code, finding that the biggest challenges ahead involve agreeing to a benchmark …

The awareness of writing secure code rises with the increasing number of attacks and their
resultant damage. But often, software developers are no security experts and vulnerabilities …

Abstract Machine learning methods are widely used to identify common, repeatedly
occurring bugs and code vulnerabilities. The performance of a machine-learned model is …

Each year, software vulnerabilities are discovered, which pose significant risks of
exploitation and system compromise. We present a convolutional neural network model that …

Thousands of security vulnerabilities are discovered in production software each year, either
reported publicly to the Common Vulnerabilities and Exposures database or discovered …

Software vulnerability detection is a critical aspect of ensuring the security and reliability of
software systems. However, traditional vulnerability detection approaches often have …

As the role of information and communication technologies gradually increases in our lives,
software security becomes a major issue to provide protection against malicious attempts …

Software vulnerability detection is critical in software security because it identifies potential
bugs in software systems, enabling immediate remediation and mitigation measures to be …

This paper presents FUNDED (Flow-sensitive vUl-Nerability coDE Detection), a novel
learning framework for building vulnerability detection models. Funded leverages the …

Background: Software vulnerabilities are weaknesses in source code that might be exploited
to cause harm or loss. Previous work has proposed a number of automated machine …