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

• URL: http://arxiv.org/abs/2506.15506v1
• Date: Wed, 18 Jun 2025 14:43:26 GMT
• Title: Insights on Adversarial Attacks for Tabular Machine Learning via a Systematic Literature Review
• Authors: Salijona Dyrmishi, Mohamed Djilani, Thibault Simonetto, Salah Ghamizi, Maxime Cordy,
• Abstract summary: Adversarial attacks in machine learning have been extensively reviewed in areas like computer vision and NLP.<n>We highlight key trends, categorize attack strategies and analyze how they address practical considerations for real-world applicability.
• Score: 13.11649527605611
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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