Adversarial Attacks on Multivariate Time Series

TL;DR

This paper introduces a novel adversarial attack method on multivariate time series classification models using a transformed adversarial network, revealing vulnerabilities across multiple datasets and proposing robustness improvements.

Contribution

It is the first to demonstrate adversarial attacks on multivariate time series models, adapting the adversarial transformation network for this domain.

Findings

Models are vulnerable to adversarial attacks across all tested datasets.

The attack method successfully fools both classical and deep learning models.

Recommends incorporating adversarial samples to enhance model robustness.

Abstract

Classification models for the multivariate time series have gained significant importance in the research community, but not much research has been done on generating adversarial samples for these models. Such samples of adversaries could become a security concern. In this paper, we propose transforming the existing adversarial transformation network (ATN) on a distilled model to attack various multivariate 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 multivariate time series classification models. The proposed methodology is tested onto 1-Nearest Neighbor Dynamic Time Warping (1-NN DTW) and a Fully Convolutional Network (FCN), all of which are trained on 18 University of East Anglia (UEA) and University of California Riverside (UCR) datasets. We show both models were susceptible to attacks on all 18 datasets. To the best of our knowledge, adversarial attacks have only been conducted in the domain of univariate time series and have not been conducted on multivariate time series. such an attack on time series classification models has never been done before. Additionally, 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.