Attacking and Defending Kubernetes Clusters: A Guided Tour

Shared read-side contract every read-only CRITIC imports — adversarial stance, empirical verification of runtime-behavior claims, the mandatory self-red-team before APPROVE, spec-UB sweeps, and the miss-ledger mechanism. The review-side mirror of author-contract. Load at the start of any review task.

The goal of this survey is two-fold: (i) to present recent advances on adversarial machine learning (AML) for the security of RS (i.e., attacking and defense recommendation models), (ii) to show another successful application of AML in generative adversarial networks (GANs) for generative applications, thanks to their ability for learning (high-dimensional) data distributions. In this survey, we provide an exhaustive literature review of 74 articles published in major RS and ML journals and conferences. This review serves as a reference for the RS community, working on the security of RS or on generative models using GANs to improve their quality.