Backdoor Attack Against One-Class Sequential Anomaly Detection Models

Abstract

We propose BA-OCAD, a clean-label backdoor attack framework targeting one-class sequential anomaly detection models. By injecting stealthy triggers into training data, BA-OCAD enables adversaries to mislead anomaly detectors at inference while maintaining benign performance.