In this article, we provide an introduction to simulation for cybersecurity and focus on three
themes:(1) an overview of the cybersecurity domain;(2) a summary of notable simulation …

Malware is one of the most common and severe cyber threats today. Malware infects
millions of devices and can perform several malicious activities including compromising …

Recent ransomware attacks threaten not only personal files but also critical infrastructure
like smart grids, necessitating early detection before encryption occurs. Current methods …

In recent years, many efforts have been made to develop efficient deep‐learning‐based
structural health monitoring (SHM) methods. Most of the proposed methods employ …

We study the challenging task of malware recognition on both known and novel unknown
malware families, called malware open-set recognition (MOSR). Previous works usually …

Generative Models have been very accommodating when it comes to generating artificial
data. Two of the most popular and promising models are the Generative Adversarial …

To detect new malware, machine learning approaches require many training samples.
These training samples contribute to build an accurate model. To maintain the accuracy …

Many current malware detection methods are based on supervised learning techniques,
which however have certain limitations. First, these techniques require a large amount of …

We study the challenging task of malware recognition on both known and novel unknown
malware families, called malware open-set recognition (MOSR). Previous works usually …

Malware detectors based on machine learning are vulnerable to adversarial attacks.
Generative Adversarial Networks (GAN) are architectures based on Neural Networks that …