Automated detection of software vulnerabilities is a fundamental problem in software
security. Existing program analysis techniques either suffer from high false positives or false …

Deep Learning (DL) is a disruptive technology that has changed the landscape of cyber
security research. Deep learning models have many advantages over traditional Machine …

The constantly increasing number of disclosed security vulnerabilities have become an
important concern in the software industry and in the field of cybersecurity, suggesting that …

The detection of software vulnerabilities (or vulnerabilities for short) is an important problem
that has yet to be tackled, as manifested by the many vulnerabilities reported on a daily …

Fine-grained software vulnerability detection is an important and challenging problem.
Ideally, a detection system (or detector) not only should be able to detect whether or not a …

Since deep learning (DL) can automatically learn features from source code, it has been
widely used to detect source code vulnerability. To achieve scalable vulnerability scanning …

Automatically detecting software vulnerabilities is an important problem that has attracted
much attention from the academic research community. However, existing vulnerability …

Machine learning is now widely used to detect security vulnerabilities in the software, even
before the software is released. But its potential is often severely compromised at the early …

As a key service of the future 6G network, healthcare digital twin is the virtual replica of a
person, which employs Internet of Things (IoT) technologies and AI-powered models to …

Social and Internet traffic analysis is fundamental in detecting and defending cyber attacks.
Traditional approaches resorting to manually defined rules are gradually replaced by …