Abstract
Anomaly detection is an important problem in many applications, ranging from medical informatics to network security. Various distribution-based techniques have been proposed to tackle this issue, which try to learn the probabilistic distribution of conventional behaviors and consider the observations with low densities as anomalies. For categorical observations, multinomial or dirichlet compound multinomial distributions were adopted as effective statistical models for conventional samples. However, when faced with small-scale data set containing multivariate categorical samples, these models will suffer from the curse of dimensionality and fail to capture the statistical properties of conventional behavior, since only a small proportion of possible categorical configurations will exist in the training data. As an effective bayesian non-parametric technique, categorical latent Gaussian process is able to model small-scale categorical data through learning a continuous latent space for multivariate categorical samples with Gaussian process. Therefore, on the basis of categorical latent Gaussian process, we propose an anomaly detection technique for multivariate categorical observations. In our method, categorical latent Gaussian process is adopted to capture the probabilistic distributions of conventional categorical samples. Experimental results on categorical data set show that our method can effectively detect anomalous categorical observations and achieve better detection performance compared with other anomaly detection techniques.
Original language | English (US) |
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Title of host publication | Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings |
Editors | Dongbin Zhao, Yuanqing Li, El-Sayed M. El-Alfy, Derong Liu, Shengli Xie |
Publisher | Springer Verlag |
Pages | 285-296 |
Number of pages | 12 |
ISBN (Print) | 9783319701387 |
DOIs | |
State | Published - 2017 |
Event | 24th International Conference on Neural Information Processing, ICONIP 2017 - Guangzhou, China Duration: Nov 14 2017 → Nov 18 2017 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 10638 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Other
Other | 24th International Conference on Neural Information Processing, ICONIP 2017 |
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Country/Territory | China |
City | Guangzhou |
Period | 11/14/17 → 11/18/17 |
Bibliographical note
Publisher Copyright:© Springer International Publishing AG 2017.
Keywords
- Anomaly detection
- Bayesian non-parametric model
- Categorical data
- Data-efficient learning
- Gaussian process