# Adversarial Attacks for Drift Detection

arXiv: https://arxiv.org/abs/2411.16591 · HTML: https://mlsanity.com/paper/2411.16591/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 8 references that are in the map.
- First posted on arXiv 25 Nov 2024.

## Builds on (most cited first, 2 of 2)

- [Understanding Concept Drift](https://mlsanity.com/paper/1704.00362/) (2017)
- [Investigating the Suitability of Concept Drift Detection for Detecting Leakages in Water Distribution Networks](https://mlsanity.com/paper/2401.01733/) (2024)

## Built on by (most cited first, 2 of 2)

- [Adversarial Evasion in Non-Stationary Malware Detection: Minimizing Drift Signals through Similarity-Constrained Perturbations](https://mlsanity.com/paper/2604.21310/) (2026)
- [Adversarial Vulnerability Under Temporal Concept Drift: A Longitudinal Study of Android Malware Detection](https://mlsanity.com/paper/2605.23623/) (2026)

## Abstract (arXiv)

Concept drift refers to the change of data distributions over time. While drift poses a challenge for learning models, requiring their continual adaption, it is also relevant in system monitoring to detect malfunctions, system failures, and unexpected behavior. In the latter case, the robust and reliable detection of drifts is imperative. This work studies the shortcomings of commonly used drift detection schemes. We show how to construct data streams that are drifting without being detected. We refer to those as drift adversarials. In particular, we compute all possible adversairals for common detection schemes and underpin our theoretical findings with empirical evaluations.