mlsanityData to 14 Oct 2026

Insights on Adversarial Attacks for Tabular Machine Learning via a Systematic Literature Review

arXiv 2506.15506 ↗First posted 18 Jun 2025cs.LGcited by 0

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Abstract (arXiv)

Adversarial attacks in machine learning have been extensively reviewed in areas like computer vision and NLP, but research on tabular data remains scattered. This paper provides the first systematic literature review focused on adversarial attacks targeting tabular machine learning models. We highlight key trends, categorize attack strategies and analyze how they address practical considerations for real-world applicability. Additionally, we outline current challenges and open research questions. By offering a clear and structured overview, this review aims to guide future efforts in understanding and addressing adversarial vulnerabilities in tabular machine learning.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 43 of its 131 references that are in the map. First posted on arXiv 18 Jun 2025.

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References · 43

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31 more
  1. 2019DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applicationscited by 123
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  26. 2025CaFA: Cost-aware, Feasible Attacks With Database Constraints Against Neural Tabular Classifierscited by 5
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  31. 2024Deep generative models as an adversarial attack strategy for tabular machine learningcited by 1

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