Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection

arXiv:2608.03642 · cs.CR · Submitted 2026-08-12 · Read on arXiv

Mingyue Yang, David Lie, Nicolas Papernot

cs.CR

Submitted: 2026-08-12

Updated: 2026-08-13

License: http://creativecommons.org/licenses/by/4.0/

The gist: As concept drift due to malware evolution presents challenges for malware classification, machine learning-based data drift detection tools are developed to mitigate this problem.

Terminology

Abstract

As concept drift due to malware evolution presents challenges for malware classification, machine learning-based data drift detection tools are developed to mitigate this problem. These data drift detector tools are designed for a different purpose and built with different techniques compared to malware classifiers. Although evasion and poisoning attacks against machine learning-based malware classifiers can cause misclassification of malware samples, it is not clear how these attacks work against data drift detectors and malware classifiers in combination. This work investigates the effect of evasion and poisoning attacks on the data drift detector along with the malware classifier. We demonstrate how unique characteristics of data drift detectors cause attacks against malware classifiers to work differently against them.

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