Federated Variational Learning for Anomaly Detection in Multivariate Time Series

• URL: http://arxiv.org/abs/2108.08404v1
• Date: Wed, 18 Aug 2021 22:23:15 GMT
• Title: Federated Variational Learning for Anomaly Detection in Multivariate Time Series
• Authors: Kai Zhang, Yushan Jiang, Lee Seversky, Chengtao Xu, Dahai Liu, Houbing Song
• Abstract summary: We propose an unsupervised time series anomaly detection framework in a federated fashion. We leave the training data distributed at the edge to learn a shared Variational Autoencoder (VAE) based on Convolutional Gated Recurrent Unit (ConvGRU) model. Experiments on three real-world networked sensor datasets illustrate the advantage of our approach over other state-of-the-art models.
• Score: 13.328883578980237
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Anomaly detection has been a challenging task given high-dimensional multivariate time series data generated by networked sensors and actuators in Cyber-Physical Systems (CPS). Besides the highly nonlinear, complex, and dynamic natures of such time series, the lack of labeled data impedes data exploitation in a supervised manner and thus prevents an accurate detection of abnormal phenomenons. On the other hand, the collected data at the edge of the network is often privacy sensitive and large in quantity, which may hinder the centralized training at the main server. To tackle these issues, we propose an unsupervised time series anomaly detection framework in a federated fashion to continuously monitor the behaviors of interconnected devices within a network and alerts for abnormal incidents so that countermeasures can be taken before undesired consequences occur. To be specific, we leave the training data distributed at the edge to learn a shared Variational Autoencoder (VAE) based on Convolutional Gated Recurrent Unit (ConvGRU) model, which jointly captures feature and temporal dependencies in the multivariate time series data for representation learning and downstream anomaly detection tasks. Experiments on three real-world networked sensor datasets illustrate the advantage of our approach over other state-of-the-art models. We also conduct extensive experiments to demonstrate the effectiveness of our detection framework under non-federated and federated settings in terms of overall performance and detection latency.

Related papers

• USD: Unsupervised Soft Contrastive Learning for Fault Detection in Multivariate Time Series [6.055410677780381]
  We introduce a combination of data augmentation and soft contrastive learning, specifically designed to capture the multifaceted nature of state behaviors more accurately. This dual strategy significantly boosts the model's ability to distinguish between normal and abnormal states, leading to a marked improvement in fault detection performance across multiple datasets and settings.
  arXiv  Detail & Related papers  (2024-05-25T14:48:04Z)
• Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values [67.76168547245237]
  We introduce a novel framework called GST-Pro, which utilizes a graphtemporal process and anomaly scorer to detect anomalies. Our experimental results show that the GST-Pro method can effectively detect anomalies in time series data and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-11T10:10:16Z)
• Causality-Based Multivariate Time Series Anomaly Detection [63.799474860969156]
  We formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data. We then propose a causality-based anomaly detection approach, which first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism. We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications.
  arXiv  Detail & Related papers  (2022-06-30T06:00:13Z)
• Deep Federated Anomaly Detection for Multivariate Time Series Data [93.08977495974978]
  We present a Federated Exemplar-based Deep Neural Network (Fed-ExDNN) to conduct anomaly detection for multivariate time series data on different edge devices. We show that ExDNN and Fed-ExDNN can outperform state-of-the-art anomaly detection algorithms and federated learning techniques.
  arXiv  Detail & Related papers  (2022-05-09T05:06:58Z)
• A Semi-Supervised Approach for Abnormal Event Prediction on Large Operational Network Time-Series Data [1.544681800932596]
  This paper presents a novel semi-supervised method that efficiently captures dependencies between network time series and across time points. The method can use the limited labeled data to explicitly learn separable embedding space for normal and abnormal samples. Experiments demonstrate that our approach significantly outperformed state-of-the-art approaches for event detection on a large real-world network log.
  arXiv  Detail & Related papers  (2021-10-14T18:33:57Z)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)
• Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals [10.866594993485226]
  We propose a novel deep learning-based anomaly detection algorithm called Deep Convolutional Autoencoding Memory network (CAE-M) We first build a Deep Convolutional Autoencoder to characterize spatial dependence of multi-sensor data with a Maximum Mean Discrepancy (MMD) Then, we construct a Memory Network consisting of linear (Autoregressive Model) and non-linear predictions (Bigressive LSTM with Attention) to capture temporal dependence from time-series data.
  arXiv  Detail & Related papers  (2021-07-27T06:48:20Z)
• HIFI: Anomaly Detection for Multivariate Time Series with High-order Feature Interactions [7.016615391171876]
  HIFI builds multivariate feature interaction graph automatically and uses the graph convolutional neural network to achieve high-order feature interactions. Experiments on three publicly available datasets demonstrate the superiority of our framework compared with state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-06-11T04:57:03Z)
• TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks [73.01104041298031]
  TadGAN is an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs) To capture the temporal correlations of time series, we use LSTM Recurrent Neural Networks as base models for Generators and Critics. To demonstrate the performance and generalizability of our approach, we test several anomaly scoring techniques and report the best-suited one.
  arXiv  Detail & Related papers  (2020-09-16T15:52:04Z)
• Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection [63.825781848587376]
  We propose OC4Seq, a one-class recurrent neural network for detecting anomalies in discrete event sequences. Specifically, OC4Seq embeds the discrete event sequences into latent spaces, where anomalies can be easily detected.
  arXiv  Detail & Related papers  (2020-08-31T04:48:22Z)
• RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks [37.16594704493679]
  We propose RobustTAD, a Robust Time series Anomaly Detection framework. It integrates robust seasonal-trend decomposition and convolutional neural network for time series data. It is deployed as a public online service and widely adopted in different business scenarios at Alibaba Group.
  arXiv  Detail & Related papers  (2020-02-21T20:43:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.