Software vulnerabilities have been posing tremendous reliability threats to the general
public as well as critical infrastructures, and there have been many studies aiming to detect …

With the advancement of deep learning (DL) in various fields, there are many attempts to
reveal software vulnerabilities by data-driven approach. Nonetheless, such existing works …

Vulnerability detection is imperative to protect software systems from cyber attacks.
However, existing methods either rely on experts to directly define vulnerability patterns or …

Deep learning has shown effectiveness in binary software vulnerability detection due to its
outstanding feature extraction capability independent of human expert experience …

The advances of machine learning (ML) including deep learning (DL) have enabled several
approaches to implicitly learn vulnerable code patterns to automatically detect software …

Due to the powerful automatic feature extraction, deep learning-based vulnerability
detection methods have evolved significantly in recent years. However, almost all current …

The explosive growth of vulnerabilities poses a significant threat to the security of software
systems. While various deep-learning-based vulnerability detection methods have emerged …

Context Software vulnerability detection is essential to ensure cybersecurity. Currently, most
software is published in binary form, thus researchers can only detect vulnerabilities in these …

Internet of Things (IoT) integrates a variety of software (eg, autonomous vehicles and military
systems) in order to enable the advanced and intelligent services. These software increase …

This study presents VDTriplet, a novel learning framework for building vulnerability detection
models. VDTriplet is the first attempt using deep learning to avoid the potential known …