Analyzing Deep Learning for Time-Series Data Through Adversarial Lens in Mobile and IoT Applications

Predictive analytics using the time-series data collected from various types of sensors is a fundamental task that enables diverse mobile and Internet of Things applications including smart health. Deep-learning-based solutions are increasingly employed to solve such tasks because of their ability to directly process raw sensor data to achieve high accuracy as opposed to using human-engineered features. However, there are no principled studies on analyzing deep models for multivariate time-series data in adversarial settings. In this article, we propose a novel framework referred as a multivariate time-series adversarial lens (MTS-AdLens) to analyze deep models for wearable and mobile sensing systems through the adversarial lens in a realistic setting. We make three main contributions toward this goal. First, we introduce highly effective black-box attacks that expose significant vulnerabilities of deep models for multivariate time-series input space. Specifically, we show that deep models are vulnerable to attacks on limited channels. Second, inspired by our vulnerability analysis, we propose a novel technique to improve the robustness of the model. Third, we perform comprehensive experiments on data collected from real tasks to validate all our claims. Our results show the effectiveness of MTS-AdLens in identifying the vulnerabilities of deep models and in improving their robustness to realistic attacks.