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

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

Vulnerability discovery in software is an important task in the field of computer security. As
vulnerabilities can be abused to enable cyber criminals and other malicious actors to exploit …

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

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 …

Context: Identifying potential vulnerable code is important to improve the security of our
software systems. However, the manual detection of software vulnerabilities requires expert …

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

Developing automated and smart software vulnerability detection models has been
receiving great attention from both research and development communities. One of the …

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

Everyyear, whether or not they may be discovered internally in personal code or are made
public, software program issues are found extra frequently. Those weaknesses might be …