Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoT

• URL: http://arxiv.org/abs/2104.03466v1
• Date: Thu, 8 Apr 2021 01:45:28 GMT
• Title: Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoT
• Authors: Zekai Chen, Dingshuo Chen, Zixuan Yuan, Xiuzhen Cheng, Xiao Zhang
• Abstract summary: This work proposed a novel framework, namely GTA, for multivariate time series anomaly detection by automatically learning a graph structure followed by the graph convolution. We also devised a novel graph convolution named Influence propagation convolution to model the anomaly information flow between graph nodes. The experiments on four public anomaly detection benchmarks further demonstrate our approach's superiority over other state-of-the-arts.
• Score: 11.480824844205864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many real-world IoT systems comprising various internet-connected sensory devices generate substantial amounts of multivariate time series data. Meanwhile, those critical IoT infrastructures, such as smart power grids and water distribution networks, are often targets of cyber-attacks, making anomaly detection of high research value. However, considering the complex topological and nonlinear dependencies that are initially unknown among sensors, modeling such relatedness is inevitable for any efficient and accurate anomaly detection system. Additionally, due to multivariate time series' temporal dependency and stochasticity, their anomaly detection remains a big challenge. This work proposed a novel framework, namely GTA, for multivariate time series anomaly detection by automatically learning a graph structure followed by the graph convolution and modeling the temporal dependency through a Transformer-based architecture. The core idea of learning graph structure is called the connection learning policy based on the Gumbel-softmax sampling strategy to learn bi-directed associations among sensors directly. We also devised a novel graph convolution named Influence Propagation convolution to model the anomaly information flow between graph nodes. Moreover, we proposed a multi-branch attention mechanism to substitute for original multi-head self-attention to overcome the quadratic complexity challenge. The extensive experiments on four public anomaly detection benchmarks further demonstrate our approach's superiority over other state-of-the-arts.

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