Effective Detection of Fileless Malware: A Review and Comparative Analysis of Detection Techniques

With the increasing sophistication of cyber threats, fileless malware has emerged as a particularly insidious form of cyberattack. Unlike traditional malware, fileless malware operates using unconventional approaches such as running entirely in memory, thus evading detection by traditional antivirus software. Whilst innovative approaches are essential to effectively detect fileless malware and mitigate potential consequences, limited studies have been conducted to examine and compare between detection approaches. To address this gap in published literature, this paper critically reviews and compares different fileless malware detection approaches. By evaluating six selected approaches, including behavioural analysis and machine learning, the study aims to provide valuable insights for enhancing cybersecurity defences against this growing threat, offering guidance for practitioners, researchers, and cybersecurity professionals.