Current machine-learning based software vulnerability detection methods are primarily
conducted at the function-level. However, a key limitation of these methods is that they do …

Deep learning (DL) models of code have recently reported great progress for vulnerability
detection. In some cases, DL-based models have outperformed static analysis tools …

To date, being benefited from the ability of automated feature extraction and the
performance of software vulnerability identification, deep learning techniques have attracted …

Due to multitudinous vulnerabilities in sophisticated software programs, the detection
performance of existing approaches requires further improvement. Multiple vulnerability …

Software vulnerabilities have always been an essential issue in cyberspace, for which many
vulnerability detection techniques have been investigated. Among them, deep learning …

Improving and enhancing the effectiveness of software vulnerability detection methods is
urgently needed today. In this study, we propose a new source code vulnerability detection …

Though many deep learning-based models have made great progress in vulnerability
detection, we have no good understanding of these models, which limits the further …

Exploitable vulnerabilities in software systems are major security concerns. To date,
machine learning (ML) based solutions have been proposed to automate and accelerate the …

In computer security, semantic learning is helpful in understanding vulnerability
requirements, realizing source code semantics, and constructing vulnerability knowledge …

Vulnerability identification is crucial to protecting software systems from attacks. Although
numerous learning-based solutions have been suggested to assist in vulnerability …