Adversarial Attacks for Drift Detection

2024-11-25

arXiv.org

AI Summary

This paper identifies a critical robustness deficiency in mainstream concept drift detectors—such as KS, ADWIN, and Page-Hinkley—when confronted with adversarially crafted data streams. It introduces the novel concept of “drift adversarial examples”: stealthy data streams that induce genuine distributional shifts yet evade detection. Method: Leveraging theoretical modeling and optimization-based construction, the work systematically characterizes the fundamental detectability boundary of concept drift and designs detector-specific adversarial perturbation generation strategies. Contribution/Results: Extensive experiments across diverse synthetic and real-world data streams demonstrate significant undetection rates, quantitatively revealing the vulnerability of existing detectors. The study further releases an open-source, reproducible framework for generating drift adversarial examples. This work establishes both a theoretical foundation and practical toolkit for advancing the robustness of concept drift detection in dynamic environments.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• Study limitations of drift detection methods
• Construct undetectable drifting data streams
• Compute adversarials for common detection schemes

Innovation

• Adversarial attack generation
• Drift detection bypass
• Empirical theoretical validation