# Adversarial Attacks on Time Series

## TL;DR

This paper introduces a novel adversarial attack method on various time series classification models using an adversarial transformation network, revealing their vulnerability across multiple datasets and suggesting robustness improvements.

## Contribution

It presents the first known adversarial attack on time series classifiers using ATNs and demonstrates their effectiveness on 42 datasets, highlighting security concerns.

## Key findings

- All tested models were vulnerable to the attack.
- The attack was successful across 42 datasets.
- Incorporating adversarial samples can improve model robustness.

## Abstract

Time series classification models have been garnering significant importance in the research community. However, not much research has been done on generating adversarial samples for these models. These adversarial samples can become a security concern. In this paper, we propose utilizing an adversarial transformation network (ATN) on a distilled model to attack various time series classification models. The proposed attack on the classification model utilizes a distilled model as a surrogate that mimics the behavior of the attacked classical time series classification models. Our proposed methodology is applied onto 1-Nearest Neighbor Dynamic Time Warping (1-NN ) DTW, a Fully Connected Network and a Fully Convolutional Network (FCN), all of which are trained on 42 University of California Riverside (UCR) datasets. In this paper, we show both models were susceptible to attacks on all 42 datasets. To the best of our knowledge, such an attack on time series classification models has never been done before. Finally, we recommend future researchers that develop time series classification models to incorporating adversarial data samples into their training data sets to improve resilience on adversarial samples and to consider model robustness as an evaluative metric.