The use of learning-based techniques to achieve automated software vulnerability detection
has been of longstanding interest within the software security domain. These data-driven …

In recent years, we have witnessed an important increase in research focusing on how
machine learning (ML) techniques can be used for software quality assessment and …

Software vulnerability detection aims to proactively reduce the risk to software security and
reliability. Despite advancements in deep-learning-based detection, a semantic gap still …

Open Source Software (OSS) security and resilience are worldwide phenomena hampering
economic and technological innovation. OSS vulnerabilities can cause unauthorized …

Packing is a widely used obfuscation technique by which malware hides content and
behavior. Much research explores how to detect a packed program via such varied …

Security vulnerabilities are weaknesses of software due for instance to design flaws or
implementation bugs that can be exploited and lead to potentially devastating security …

Software vulnerability detection plays a proactive role in reducing risks to software security
and reliability. Despite advancements in deep learning-based detection, a semantic gap …

Abstract Machine Learning algorithms provide astonishing performance in a wide range of
tasks, including sensitive and critical applications. On the other hand, it has been shown that …

This chapter discuss existing machine learning and symbolic execution approaches that
have been developed in the context of malware detection. It then presents our malware …