mlsanityData to 14 Oct 2026

MLSMM: Machine Learning Security Maturity Model

arXiv 2306.16127 ↗First posted 28 Jun 2023cs.SE cs.CR cs.LGcited by 0

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Abstract (arXiv)

Assessing the maturity of security practices during the development of Machine Learning (ML) based software components has not gotten as much attention as traditional software development. In this Blue Sky idea paper, we propose an initial Machine Learning Security Maturity Model (MLSMM) which organizes security practices along the ML-development lifecycle and, for each, establishes three levels of maturity. We envision MLSMM as a step towards closer collaboration between industry and academia.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 15 references that are in the map. First posted on arXiv 28 Jun 2023.

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References · 6

  1. 2017Wild Patterns: Ten Years After the Rise of Adversarial Machine Learningcited by 414
  2. 2020Adversarial Machine Learning -- Industry Perspectivescited by 122
  3. 2022Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoningcited by 94
  4. 2021Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systemscited by 41
  5. 2022Machine Learning Security in Industry: A Quantitative Surveycited by 21
  6. 2022Threat Assessment in Machine Learning based Systemscited by 9

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