Adversarial Machine Learning -- Industry Perspectives
arXiv:2002.05646
Abstract
Based on interviews with 28 organizations, we found that industry practitioners are not equipped with tactical and strategic tools to protect, detect and respond to attacks on their Machine Learning (ML) systems. We leverage the insights from the interviews and we enumerate the gaps in perspective in securing machine learning systems when viewed in the context of traditional software security development. We write this paper from the perspective of two personas: developers/ML engineers and security incident responders who are tasked with securing ML systems as they are designed, developed and deployed ML systems. The goal of this paper is to engage researchers to revise and amend the Security Development Lifecycle for industrial-grade software in the adversarial ML era.
Minor Typos corrected 7 pages, 1 figure
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Cited by in corpus (9)
- Challenges in Deploying Machine Learning: a Survey of Case Studies
- SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection
- Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching
- Image and Model Transformation with Secret Key for Vision Transformer
- The Duo of Artificial Intelligence and Big Data for Industry 4.0: Review of Applications, Techniques, Challenges, and Future Research Directions
- Green Lighting ML: Confidentiality, Integrity, and Availability of Machine Learning Systems in Deployment
- Effect of backdoor attacks over the complexity of the latent space distribution
- Feature Importance Guided Attack: A Model Agnostic Adversarial Attack
- Mental Models of Adversarial Machine Learning