MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks

Dan Li; Dacheng Chen; Baihong Jin; Lei Shi; Jonathan Goh; See-Kiong Ng

doi:10.1007/978-3-030-30490-4_56

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MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks

Dan Li Dacheng Chen Baihong Jin Lei Shi Jonathan Goh See-Kiong Ng

Year: 2019 Lecture notes in computer science Pages: 703-716 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-030-30490-4_56

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Keywords:

Anomaly detection Computer science Discriminator Exploit Anomaly (physics) Data mining Recurrent neural network Artificial intelligence Intrusion detection system Latent variable Time series Multivariate statistics Unsupervised learning Pattern recognition (psychology) Artificial neural network Machine learning

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Topics

Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Network Security and Intrusion Detection

Physical Sciences → Computer Science → Computer Networks and Communications

Time Series Analysis and Forecasting

Physical Sciences → Computer Science → Signal Processing

MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks

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Citation History

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