paper_unmarked.pdf (2.51 MB)
Adversarial Attacks on Time Series
journal contributionposted on 2021-08-10, 20:40 authored by Fazle Karim, Somshubra Majumdar, Houshang DarabiHoushang Darabi
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 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. When compared to Fast Gradient Sign Method, the proposed attack generates a larger faction of successful adversarial black-box attacks. A simple defense mechanism is successfully devised to reduce the fraction of successful adversarial samples. 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.
Publisher Statement© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
CitationKarim, F., Majumdar, S.Darabi, H. (2020). Adversarial Attacks on Time Series. IEEE Transactions on Pattern Analysis and Machine Intelligence, PP(99), 1-1. https://doi.org/10.1109/tpami.2020.2986319
PublisherInstitute of Electrical and Electronics Engineers (IEEE)