Machine learning is of rising importance in cybersecurity. The primary objective of applying
machine learning in cybersecurity is to make the process of malware detection more …

The aim of this systematic literature review (SLR) is to provide a comprehensive overview of
the current state of Windows malware detection techniques, research issues, and future …

Machine learning methods can detect Android malware with very high accuracy. However,
these classifiers have an Achilles heel, concept drift: they rapidly become out of date and …

Recent security attack reports show that the number of malware attacks is gradually growing
over the years due to the rapid adoption of smart healthcare systems. The development of a …

Portable document format (PDF) files are one of the most universally used file types. This
has incentivized hackers to develop methods to use these normally innocent PDF files to …

Concept drift is one of the most frustrating challenges for learning-based security
applications built on the closeworld assumption of identical distribution between training and …

In recent years, the increase in non-Windows malware threats had turned the focus of the
cybersecurity community. Research works on hunting Windows PE-based malwares are …

In this survey, we review the key developments in the field of malware detection using AI and
analyze core challenges. We systematically survey state-of-the-art methods across five …

Existing malware classification methods cannot handle the open-ended growth of new or
unknown malware well because it only focuses on pre-defined malware classes with …

Cyber crimes related to malware families are on the rise. This growth persists despite the
prevalence of various antivirus software and approaches for malware detection and …