# Adversarial Attacks on Multivariate Time Series

arXiv: https://arxiv.org/abs/2004.00410 · HTML: https://mlsanity.com/paper/2004.00410/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2020.

## Built on by (most cited first, 13 of 13)

- [BASAR:Black-box Attack on Skeletal Action Recognition](https://mlsanity.com/paper/2103.05266/) (2021)
- [Targeted Adversarial Attacks on Wind Power Forecasts](https://mlsanity.com/paper/2303.16633/) (2023)
- [Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms](https://mlsanity.com/paper/2207.09572/) (2022)
- [Understanding the Vulnerability of Skeleton-based Human Activity Recognition via Black-box Attack](https://mlsanity.com/paper/2211.11312/) (2022)
- [Towards an Awareness of Time Series Anomaly Detection Models' Adversarial Vulnerability](https://mlsanity.com/paper/2208.11264/) (2022)
- [Universal Adversarial Attack on Deep Learning Based Prognostics](https://mlsanity.com/paper/2109.07142/) (2021)
- [On the Susceptibility and Robustness of Time Series Models through Adversarial Attack and Defense](https://mlsanity.com/paper/2301.03703/) (2023)
- [Adversarial Attacks and Defenses in Multivariate Time-Series Forecasting for Smart and Connected Infrastructures](https://mlsanity.com/paper/2408.14875/) (2024)
- [Concealed Adversarial attacks on neural networks for sequential data](https://mlsanity.com/paper/2502.20948/) (2025)
- [ReLATE: Resilient Learner Selection for Multivariate Time-Series Classification Against Adversarial Attacks](https://mlsanity.com/paper/2503.07882/) (2025)
- [Exposing Vulnerabilities in Explanation for Time Series Classifiers via Dual-Target Attacks](https://mlsanity.com/paper/2602.02763/) (2026)
- [Evasion of IoT Malware Detection via Dummy Code Injection](https://mlsanity.com/paper/2602.08170/) (2026)
- [GENADA: efficient generative time series adversarial attack framework](https://mlsanity.com/paper/2608.12535/) (2026)

## Abstract (arXiv)

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.