Insights on Adversarial Attacks for Tabular Machine Learning via a Systematic Literature Review
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866918062948614144 |
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| author | Dyrmishi, Salijona Djilani, Mohamed Simonetto, Thibault Ghamizi, Salah Cordy, Maxime |
| author_facet | Dyrmishi, Salijona Djilani, Mohamed Simonetto, Thibault Ghamizi, Salah Cordy, Maxime |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_15506 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Insights on Adversarial Attacks for Tabular Machine Learning via a Systematic Literature Review Dyrmishi, Salijona Djilani, Mohamed Simonetto, Thibault Ghamizi, Salah Cordy, Maxime Machine Learning 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. |
| title | Insights on Adversarial Attacks for Tabular Machine Learning via a Systematic Literature Review |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.15506 |