Assessing the Impact of Packing on Machine Learning-Based Malware Detection and Classification Systems

TL;DR

This paper investigates how packing techniques affect the performance of static machine learning-based malware detection and classification systems, revealing limitations and emphasizing the need for more robust methods.

Contribution

It provides a comprehensive analysis of packing effects on static ML malware detectors, highlighting challenges and guiding future improvements.

Findings

Packing significantly reduces detection accuracy.

Certain visualization-based models are more affected.

Current static systems have notable limitations.

Abstract

The proliferation of malware, particularly through the use of packing, presents a significant challenge to static analysis and signature-based malware detection techniques. The application of packing to the original executable code renders extracting meaningful features and signatures challenging. To deal with the increasing amount of malware in the wild, researchers and anti-malware companies started harnessing machine learning capabilities with very promising results. However, little is known about the effects of packing on static machine learning-based malware detection and classification systems. This work addresses this gap by investigating the impact of packing on the performance of static machine learning-based models used for malware detection and classification, with a particular focus on those using visualisation techniques. To this end, we present a comprehensive analysis of various packing techniques and their effects on the performance of machine learning-based detectors and classifiers. Our findings highlight the limitations of current static detection and classification systems and underscore the need to be proactive to effectively counteract the evolving tactics of malware authors.