MGAD: Mutual Information and Graph Embedding Based Anomaly Detection in Multivariate Time Series

Electronics, Journal Year: 2024, Volume and Issue: 13(7), P. 1326 - 1326

Published: April 1, 2024

Along with the popularity of mobile Internet and smart applications, more high-dimensional sensor data have appeared, these hidden information about system performance degradation, failure, etc., how to mine them obtain such is a very difficult problem. This challenge can be solved by anomaly detection techniques, which an important field research in mining, especially domains network security, credit card fraud detection, industrial fault identification, etc. However, there are many difficulties multivariate time-series data, including poor accuracy, fast generation, lack labeled capture between sensors. To address issues, we present mutual graph embedding based algorithm time series, called MGAD (mutual detection). The consists four steps: (1) Embedding where heterogeneous become different vectors same vector space; (2) Constructing relationship sensors using their each other; (3) Learning attention mechanism, predict at next moment; (4) Compare predicted values real detect potential outliers. Our contributions as follows: propose unsupervised outlier high interpretability accuracy; massive experiments on benchmark datasets demonstrated superior algorithm, compared state-of-the-art baselines terms ROC, F1, AP.

Language: Английский

Cryptocurrency Transaction Anomaly Detection Based on Chebyshev Graph Neural Network

Communications in computer and information science, Journal Year: 2024, Volume and Issue: unknown, P. 375 - 386

Published: Nov. 2, 2024

Language: Английский