Software Vulnerabilities (SVs) are increasing in complexity and scale, posing great security
risks to many software systems. Given the limited resources in practice, SV assessment and …

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

Abstract Many Machine Learning (ML)-based approaches have been proposed to
automatically detect, localize, and repair software vulnerabilities. While ML-based methods …

We consider the problem of automating the mapping of observed vulnerabilities in software
listed in Common Vulnerabilities and Exposures (CVE) reports to weaknesses listed in …

Software vulnerabilities are the root causes of various security risks. Once a vulnerability is
exploited by malicious attacks, it will greatly compromise the safety of the system, and may …

Vulnerability reports play an important role in cybersecurity. Mitigation of software
vulnerabilities that can be exploited by attackers depends on disclosure of vulnerabilities …

Identifying security patches via code commits to allow early warnings and timely fixes for
Open Source Software (OSS) has received increasing attention. However, the existing …

Software vulnerabilities constitute a great risk for the IT community. The specification of the
vulnerability characteristics is a crucial procedure, since the characteristics are used as input …

Dynamic data-driven vulnerability assessments face massive heterogeneous data contained
in, and produced by SOCs (Security Operations Centres). Manual vulnerability assessment …

To address the problem that the traditional vectored representation of software vulnerability
data has high-dimensional sparsity and leads to unsatisfactory automatic classification, this …