Insights on Adversarial Attacks for Tabular Machine Learning via a Systematic Literature Review
It addresses the scattered research on adversarial vulnerabilities in tabular data, which is crucial for practitioners and researchers in machine learning security, though it is incremental as a review.
This paper tackles the lack of a comprehensive overview of adversarial attacks on tabular machine learning models by conducting the first systematic literature review, highlighting key trends and categorizing attack strategies to guide future research.
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.