An Adversarial Examples Identification Method for Time Series in Internet-of-Things System

Abstract

Adversarial examples (AEs) make Internet-of-Things (IoT) systems to face a great challenge. The tiny fluctuations in AEs mislead the learning model, let the model accept, and make wrong decisions. So, accurately identifying AEs is significant for improving the robustness in IoT systems. In this article, a method to identify AEs for time-series data is proposed. We analyze the characteristics of adversarial time-series examples and normal time-series samples and propose a time-series representation method. The tiny fluctuations in AEs are easily discovered by this representation. A metric is also designed to support this representation method to identify AEs with supervised learning and unsupervised learning methods. Our representation method and the metric is applied with supervised learning and unsupervised learning methods to identify time series in real data set and data sets in the official UCR repository. The experimental results show that our method has a great ability to identify AEs, compared to previous approaches.