Generating End-to-End Adversarial Examples for Malware Classifiers Using Explainability. (arXiv:2009.13243v2 [cs.CR] UPDATED)

In recent years, the topic of explainable machine learning (ML) has been
extensively researched. Up until now, this research focused on regular ML users
use-cases such as debugging a ML model. This paper takes a different posture
and show that adversaries can leverage explainable ML to bypass multi-feature
types malware classifiers. Previous adversarial attacks against such
classifiers only add new features and not modify existing ones to avoid harming
the modified malware executable's functionality. Current attacks use a single
algorithm …

adversarial end end-to-end malware