Analyzing Accessed Content Sequences with HDP-based Models
Pages 142 - 149
Abstract
The need for auditing network users' behaviors based on their accessed content urges for a new method for modeling and analysis. Topic model is a probabilistic generative model for data mixture of varying length that can extract features from individual instances of content. The hidden Markov model can be used for analyzing sequences of content. By introducing Hierarchical Dirichlet Processes on top of topic mixtures and HMMs, we can tackle down the challenges of unknown numbers of mixtures in both models by resorting to nonparametric approach. We employ variational inference for model calculation, and cluster the extracted user patterns in the form of HMM parameters to detect potential anomalies in access behavior. The proposed scheme is then verified by carrying out experiments on the LAN users' log data in an institute.
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- Analyzing Accessed Content Sequences with HDP-based Models
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Published In
April 2018
168 pages
ISBN:9781450364577
DOI:10.1145/3220162
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Published: 28 April 2018
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- Research
- Refereed limited
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- Science and Technology Planning Project of Guangdong Province
- Shantou science and technology project
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ICMSSP '18
ICMSSP '18: 2018 3rd International Conference on Multimedia Systems and Signal Processing
April 28 - 30, 2018
Shenzhen, China
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