Selection of Deep Neural Network Models for IoT Anomaly Detection Experiments
Diana Gaifulina, Igor Vitalievich Kotenko · 2021
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.