An Approach to Detect Fileless Malware that Maintains Persistence in Windows Environment

Abstract

The rapid enhancement of the Internet in the past few years has increasingly impacted the general public’s work and life. As a drawback, this enhancement has also led to a major increase in malicious software on the internet causing great security threats to the consumers of the internet. Currently, a new type of malware class called Fileless malware has come into action causing more destructive damages. As the name Fileless suggests, these types of malware programs are not files or executables, but a malicious activity that runs entirely in the memory, leaving the slightest evidence on the targeted host machine. Microsoft Windows is one of the most widely used operating systems both in personal desktop computers and enterprise computer systems and is highly targeted by Fileless malware. This paper provides an approach to detect fileless malware that maintains persistence in the Windows environment using Fileless malware behavioural data and deep learningbased classification models.