Poisoning Attacks on ML Pipelines: Detection, Response, and Resilient Training

"Poisoning Attacks on ML Pipelines: Detection, Response, and Resilient Training"

Machine learning systems are increasingly attacked through the data, labels, feedback loops, and pipeline controls they depend on—not just through code or infrastructure. This book is written for experienced ML engineers, MLOps practitioners, security engineers, and technical leaders who need a rigorous, operational understanding of poisoning threats in modern ML environments. It treats poisoning as a full lifecycle security problem, spanning ingestion, training, evaluation, deployment, and continuous learning.

Readers will learn how to map the ML pipeline attack surface, build precise threat models, understand the mechanics of availability, targeted, backdoor, and federated poisoning attacks, and deploy practical defenses grounded in provenance, lineage, data validation, and secure MLOps controls. The book also develops a concrete detection and monitoring toolbox, structured incident response and forensic methods, and resilient training strategies with explicit trade-offs, evaluation protocols, and backdoor-specific validation gates. Throughout, the emphasis is on measurable outcomes: attack success rate, detection fidelity, rollback confidence, and recovery from trusted artifacts.

What sets this book apart is its artifact-centric, operations-first perspective. Rather than treating poisoning as a purely academic adversarial ML topic, it shows how to integrate prevention, detection, response, and long-term hardening into real training and deployment workflows, especially in high-risk continu

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E-bog: 30. april 2026