With rapid progress and significant successes in a wide spectrum of applications, deep
learning is being applied in many safety-critical environments. However, deep neural …

The amount of time in which a sample is executed is one of the key parameters of a malware
analysis sandbox. Setting the threshold too high hinders the scalability and reduces the …

Malware open-set recognition (MOSR) is an emerging research domain that aims at jointly
classifying malware samples from known families and detecting the ones from novel …

With the coming of the Internet of Things (IoT) era, malware attacks targeting IoT networks
have posed serious threats to users. Recently, the emerging of edge computing have paved …

The widespread usage of Android-powered devices in the Internet of Things (IoT) makes
them susceptible to evolving cybersecurity threats. Most healthcare devices in IoT networks …

In real life, distinct runs of the same artifact lead to the exploration of different paths, due to
either system's natural randomness or malicious constructions. These variations might …

A long-term goal of Artificial Intelligence (AI) is to provide machines with the capability of
understanding natural language. Understanding natural language may be referred as the …

A promising avenue for improving the effectiveness of behavioral-based malware detectors
is to leverage two-phase detection mechanisms. Existing problem in two-phase detection is …

With the development of the Internet of Things (IoT), mobile devices are playing an
increasingly important role in our daily lives. There are various malware threats present in …

The rapid increase in new malware necessitates effective detection methods. While machine
learning techniques have shown promise for malware detection, most research focuses on …