Electrical Grid Anomaly Detection via Tensor Decomposition

• URL: http://arxiv.org/abs/2310.08650v1
• Date: Thu, 12 Oct 2023 18:23:06 GMT
• Title: Electrical Grid Anomaly Detection via Tensor Decomposition
• Authors: Alexander Most, Maksim Eren, Nigel Lawrence, Boian Alexandrov
• Abstract summary: Previous work has shown that dimensionality reduction-based approaches can be used for accurate identification of anomalies in SCADA systems. In this work, we novelly apply the tensor decomposition method Canonical Polyadic Alternating Poisson Regression with a probabilistic framework, to identify anomalies in SCADA systems. In our experiments, we model real-world SCADA system data collected from the electrical grid operated by Los Alamos National Laboratory.
• Score: 41.94295877935867
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Supervisory Control and Data Acquisition (SCADA) systems often serve as the nervous system for substations within power grids. These systems facilitate real-time monitoring, data acquisition, control of equipment, and ensure smooth and efficient operation of the substation and its connected devices. Previous work has shown that dimensionality reduction-based approaches, such as Principal Component Analysis (PCA), can be used for accurate identification of anomalies in SCADA systems. While not specifically applied to SCADA, non-negative matrix factorization (NMF) has shown strong results at detecting anomalies in wireless sensor networks. These unsupervised approaches model the normal or expected behavior and detect the unseen types of attacks or anomalies by identifying the events that deviate from the expected behavior. These approaches; however, do not model the complex and multi-dimensional interactions that are naturally present in SCADA systems. Differently, non-negative tensor decomposition is a powerful unsupervised machine learning (ML) method that can model the complex and multi-faceted activity details of SCADA events. In this work, we novelly apply the tensor decomposition method Canonical Polyadic Alternating Poisson Regression (CP-APR) with a probabilistic framework, which has previously shown state-of-the-art anomaly detection results on cyber network data, to identify anomalies in SCADA systems. We showcase that the use of statistical behavior analysis of SCADA communication with tensor decomposition improves the specificity and accuracy of identifying anomalies in electrical grid systems. In our experiments, we model real-world SCADA system data collected from the electrical grid operated by Los Alamos National Laboratory (LANL) which provides transmission and distribution service through a partnership with Los Alamos County, and detect synthetically generated anomalies.

Related papers

• Are we certain it's anomalous? [57.729669157989235]
  Anomaly detection in time series is a complex task since anomalies are rare due to highly non-linear temporal correlations. Here we propose the novel use of Hyperbolic uncertainty for Anomaly Detection (HypAD) HypAD learns self-supervisedly to reconstruct the input signal.
  arXiv  Detail & Related papers  (2022-11-16T21:31:39Z)
• Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection [122.4894940892536]
  We present a novel self-supervised masked convolutional transformer block (SSMCTB) that comprises the reconstruction-based functionality at a core architectural level. In this work, we extend our previous self-supervised predictive convolutional attentive block (SSPCAB) with a 3D masked convolutional layer, a transformer for channel-wise attention, as well as a novel self-supervised objective based on Huber loss.
  arXiv  Detail & Related papers  (2022-09-25T04:56:10Z)
• 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)
• Feature anomaly detection system (FADS) for intelligent manufacturing [0.0]
  We present a new anomaly detection algorithm called FADS (feature-based anomaly detection system) FADS generates a statistical model of nominal inputs by observing the activation of the convolutional filters. During inference the system compares the convolutional filter activation of the new input to the statistical model and flags activations that are outside the expected range of values.
  arXiv  Detail & Related papers  (2022-04-21T17:54:37Z)
• 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)
• Federated Variational Learning for Anomaly Detection in Multivariate Time Series [13.328883578980237]
  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.
  arXiv  Detail & Related papers  (2021-08-18T22:23:15Z)
• CSCAD: Correlation Structure-based Collective Anomaly Detection in Complex System [11.739889613196619]
  We propose a correlation structure-based collective anomaly detection model for high-dimensional anomaly detection problem in large systems. Our framework utilize graph convolutional network combining a variational autoencoder to jointly exploit the feature space correlation and reconstruction deficiency of samples. An anomaly discriminating network can then be trained using low anomalous degree samples as positive samples, and high anomalous degree samples as negative samples.
  arXiv  Detail & Related papers  (2021-05-30T09:28:25Z)
• A Survey on Anomaly Detection for Technical Systems using LSTM Networks [0.0]
  Anomalies represent deviations from the intended system operation and can lead to decreased efficiency as well as partial or complete system failure. In this article, a survey on state-of-the-art anomaly detection using deep neural and especially long short-term memory networks is conducted. The investigated approaches are evaluated based on the application scenario, data and anomaly types as well as further metrics.
  arXiv  Detail & Related papers  (2021-05-28T13:24:40Z)
• TELESTO: A Graph Neural Network Model for Anomaly Classification in Cloud Services [77.454688257702]
  Machine learning (ML) and artificial intelligence (AI) are applied on IT system operation and maintenance. One direction aims at the recognition of re-occurring anomaly types to enable remediation automation. We propose a method that is invariant to dimensionality changes of given data.
  arXiv  Detail & Related papers  (2021-02-25T14:24:49Z)
• Data Anomaly Detection for Structural Health Monitoring of Bridges using Shapelet Transform [0.0]
  A number of Structural Health Monitoring (SHM) systems are deployed to monitor civil infrastructure. The data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. This paper proposes the use of a relatively new time series representation named Shapelet Transform to autonomously identify anomalies in SHM data.
  arXiv  Detail & Related papers  (2020-08-31T01:11:04Z)

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.