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

Many real-world Internet of Things (IoT) systems, which include a variety of Internet-connected sensory devices, produce substantial amounts of multivariate time-series data. Meanwhile, vital IoT infrastructures, such as smart power grids and water distribution networks are frequently targeted by cyberattacks, making anomaly detection an important study topic. Modeling such relatedness is, nevertheless, unavoidable for any efficient and effective anomaly detection system, given the intricate topological and nonlinear connections that are originally unknown among sensors. Furthermore, detecting anomalies in multivariate time series is difficult due to their temporal dependency and stochasticity. This article presented GTA, a new framework for multivariate time-series anomaly detection that involves automatically learning a graph structure, graph convolution, and modeling temporal dependency using a transformer-based architecture. The connection learning policy, which is based on the Gumbel-softmax sampling approach to learn bidirected links among sensors directly, is at the heart of the learning graph structure. To describe the anomaly information flow between network nodes, we introduced a new graph convolution called influence propagation convolution. In addition, to tackle the quadratic complexity barrier, we suggested a multibranch attention mechanism to replace the original multihead self-attention method. Extensive experiments on four publicly available anomaly detection benchmarks further demonstrate the superiority of our approach over alternative state of the arts.