Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection

2026-08-04

AI Summary

This study addresses the challenge posed by concept drift induced by malware evolution to classification systems, particularly under adversarial conditions where evasion and poisoning attacks target the classifier. While data drift detectors are commonly deployed to maintain model reliability, their resilience against such classifier-focused attacks remains unclear. This work presents the first systematic evaluation of how these adversarial attacks impact the joint system comprising a drift detector and a malware classifier. Through empirical analysis integrating representative attack strategies with machine learning–based drift detection techniques, the study reveals significant disparities in attack effectiveness across system components—demonstrating that attacks optimized for classifiers do not necessarily compromise drift detectors. Furthermore, it elucidates how intrinsic properties of drift detectors modulate adversarial success, offering critical insights and design principles for building robust malware detection systems.

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.

Problem

• evasion attacks
• poisoning attacks
• malware classification
• data drift detection
• concept drift

Innovation

• evasion attacks
• poisoning attacks
• data drift detection
• malware classification
• concept drift