  Time series anomaly detection is extensively studied in statistics,
economics, and computer science. Over the years, numerous methods have been
proposed for time series anomaly detection using deep learning-based methods.
Many of these methods demonstrate state-of-the-art performance on benchmark
datasets, giving the false impression that these systems are robust and
deployable in many practical and industrial real-world scenarios. In this
paper, we demonstrate that the performance of state-of-the-art anomaly
detection methods is degraded substantially by adding only small adversarial
perturbations to the sensor data. We use different scoring metrics such as
prediction errors, anomaly, and classification scores over several public and
private datasets ranging from aerospace applications, server machines, to
cyber-physical systems in power plants. Under well-known adversarial attacks
from Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD)
methods, we demonstrate that state-of-the-art deep neural networks (DNNs) and
graph neural networks (GNNs) methods, which claim to be robust against
anomalies and have been possibly integrated in real-life systems, have their
performance drop to as low as 0%. To the best of our understanding, we
demonstrate, for the first time, the vulnerabilities of anomaly detection
systems against adversarial attacks. The overarching goal of this research is
to raise awareness towards the adversarial vulnerabilities of time series
anomaly detectors.
