Although ransomware has been around since the early days of personal computers, its
sophistication and aggression have increased substantially over the years. Ransomware, as …

Ransomware remains an alarming threat in the 21st century. It has evolved from being a
simple scare tactic into a complex malware capable of evasion. Formerly, end-users were …

Deep learning (DL) has had unprecedented success and is now entering scientific
computing with full force. However, current DL methods typically suffer from instability, even …

Ransomware attacks increased in recent years causing significant damages and disruptions
to businesses. Forensic analysis such as reverse engineering of executables (or binary files) …

Adversarial machine learning is a recent area of study that explores both adversarial attack
strategy and detection systems of adversarial attacks, which are inputs specially crafted to …

Ransomware attacks are often catastrophic, yet existing reactive and preventative measures
could only partially mitigate ransomware damage, often not in a timely manner, and often …

Neural networks demonstrate vulnerability to small, non-random perturbations, emerging as
adversarial attacks. Such attacks, born from the gradient of the loss function relative to the …

Ransomware attacks have rapidly proliferated, inflicting severe financial damages on
businesses and individuals. Machine learning approaches to automate ransomware …

Malware is an emerging and popular threat flourishing in the underground economy. The
commoditization of Malware-as-a-Service (MaaS) allows criminals to obtain financial …

The study in this paper presents the results of ransomware analysis to identify the
characteristic properties that distinguish ransomware executable from other malware and …