Unsupervised Anomaly Detection in IoT Systems for Smart Cities

Anomaly detection is critical in the Internet of Things (IoT) systems due to its wide applications for building smart cities, such as quality control in manufacturing, intrusion detection in system security, fault detection in system monitoring. Many existing schemes are problem specific and supervi...

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Bibliographic Details
Published in:IEEE transactions on network science and engineering Vol. 7; no. 4; pp. 2231 - 2242
Main Authors: Guo, Yifan, Ji, Tianxi, Wang, Qianlong, Yu, Lixing, Min, Geyong, Li, Pan
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.10.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Anomalies
Anomaly detection
Bayesian analysis
Decoding
Fault detection
Gated Recurrent Unit (GRU)
Gaussian distribution
Gaussian Mixture Model (GMM)
Image reconstruction
Internet of Things
iot
Quality control
Smart cities
Statistical inference
Time series
Time series analysis
Training
unsupervised anomaly detection
Variational Autoencoder(VAE)
ISSN:2327-4697, 2334-329X
Online Access:Get full text
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Summary:Anomaly detection is critical in the Internet of Things (IoT) systems due to its wide applications for building smart cities, such as quality control in manufacturing, intrusion detection in system security, fault detection in system monitoring. Many existing schemes are problem specific and supervised approaches, which require domain knowledge and tremendous data labeling efforts. In this paper, we investigate unsupervised anomaly detection on multidimensional time series data in IoT systems, and develops a GRU-based Gaussian Mixture VAE scheme, called GGM-VAE. In particular, we employ Gated Recurrent Unit (GRU) cells to discover the correlations among time series data, and use Gaussian Mixture priors in the latent space to characterize the multimodal data. Several previous works assume simple distributions for Gaussian Mixture priors, resulting in insufficient ability to fully capture the data patterns. To overcome this issue, we design a model selection mechanism during the training process under the guidance of Bayesian Inference Criterion (BIC) to find the model which can well estimate the distribution in the Gaussian Mixture latent space. We conduct extensive simulations on four datasets and observe that our proposed scheme outperforms the state-of-the-art anomaly detection schemes and achieves up to 47.88% improvement in F1 scores on average.
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ISSN:2327-4697
2334-329X
DOI:10.1109/TNSE.2020.3027543