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

TL;DR

This paper systematically reviews adversarial attacks on tabular machine learning models, highlighting key trends, challenges, and future research directions to improve understanding and robustness in real-world applications.

Contribution

It provides the first comprehensive review of adversarial attacks on tabular data, categorizing strategies and analyzing practical considerations for real-world deployment.

Findings

Identifies key attack strategies and their effectiveness

Highlights challenges in real-world applicability

Outlines open research questions in the field

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.