Selection of Deep Neural Network Models for IoT Anomaly Detection Experiments

This research is about selection of deep neural network models for anomaly detection in Internet of Things network traffic. We are experimentally evaluating deep neural network models using the same software, hardware and the same subsets of the UNSW-NB 15 dataset for training and testing. The asses...

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Bibliographic Details
Main Authors: Gaifulina, Diana, Kotenko, Igor
Format: Conference Proceeding
Language:English
Subjects:
Analytical models
anomaly detection
Deep learning
deep neural networks
Hardware
information security
Internet of Things
Recurrent neural networks
Software
Telecommunication traffic
Training
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Description
Summary:This research is about selection of deep neural network models for anomaly detection in Internet of Things network traffic. We are experimentally evaluating deep neural network models using the same software, hardware and the same subsets of the UNSW-NB 15 dataset for training and testing. The assessment results are quality metrics of anomaly detection and the time spent on training models.
ISSN:2377-5750
DOI:10.1109/PDP52278.2021.00049