Fig 1.
The flow of the FIERL model.
Table 1.
Details of the experimental data.
Table 2.
Experimental results of detecting APT attack when the FIE model’s parameters are changed and parameters τ and α of RL model are fixed at 0.1 and 0.1 respectively.
Table 3.
Experimental results of detecting APT attack when the RL model’s parameters are changed and the FIE model’s parameters are fixed.
Fig 2.
Confusion matrix result of the FIERL model with the best parameters.
Table 4.
Experimental results when replacing the FIE model with some deep learning networks.
Fig 3.
Confusion matrix results of models using deep learning networks to replace the FIE model.
In which (a): CNN; (b): LSTM; (c): MLP-Inference.
Table 5.
Experimental results when replacing components in the RL model.
Fig 4.
Comparing the difference in data distribution of the Smote and Dropout methods.
Where: (a): The data distribution of the Smote algorithm; (b): The data distribution of the Dropout algorithm.
Fig 5.
(a): The data distribution when using a combination of Dropout and Triplet Loss; (b): The data distribution when using a combination of Dropout and Contrastive Learning.
Fig 6.
Comparing the data distribution of the method without using representation learning (a) and using representation learning (b).
Table 6.
Experimental results of scenario 3.