Fig 1.
The proposed work generic workflow.
Fig 2.
A detailed overview of the proposed anomaly detection methodology.
Fig 3.
Different data preprocessing phases/steps.
Table 1.
Comparison of results with and without optimal statistical method.
Fig 4.
LSTM network architecture for the proposed work.
Fig 5.
Deep autoencoding gaussian mixture model architecture for the proposed work.
Fig 6.
Deep learning ensemble model architecture for the proposed work.
Fig 7.
Deep CNN network architecture for the proposed work.
Fig 8.
Transformer network architecture for the proposed work.
Fig 9.
GNN network architecture for the proposed work.
Fig 10.
The proposed work’s detailed system block diagram.
Fig 11.
CNN-LSTM and their ensembled comparison of classification results on the proposed solution.
Fig 12.
Transformer-GNN and their ensembled comparison of classification results on proposed.
Fig 13.
DAGMM comparison of classification results on the proposed solution.
Table 2.
Comparison of classification results.