Table 1.
Comparison of intrusion detection systems.
Fig 1.
The architecture of LSTM cell.
Fig 2.
The architecture of ILSTM.
Table 2.
NSL-KDD dataset description.
Fig 3.
Shap analysis for NSL-KDD in binary classification.
Fig 4.
Shap analysis for NSL-KDD in multi-class classification.
Table 3.
Transformation of symbolic features in NSL-KDD.
Fig 5.
Performance test on KDDTest+ with increasing learning rate.
Table 4.
Parameter setting for LSTM.
Table 5.
Parameter setting for CBOA and PSO.
Table 6.
Summary of LITNET-2020 dataset.
Fig 6.
SHAP analysis for LITNET-2020 dataset.
Fig 7.
LITNET-2020 data splitting approach.
Fig 8.
Confusion matrices for KDDTest+ in binary classification.
Table 7.
Comparison between LSTM, LSTM-BOA, LSTM-CBOA and ILSTM using KDDTest+ and KDDTest-21 in binary classification with average 10 runs.
Fig 9.
Accuracy and number of iterations for LSTM and ILSTM using KDDTest+ in binary classifications.
A: LSTM. B: ILSTM.
Fig 10.
Confusion matrices for KDDTest-21 in binary classification.
Fig 11.
LSTM vs ILSTM for KDDTest-21 for binary classification.
A: LSTM. B: ILSTM.
Fig 12.
MSE for ILSTM algorithm in binary classification.
A: KDDTest+. B: KDDTest-21.
Table 8.
Statistic Test for KDDTest+ and KDDTest-21 in binary classification.
Fig 13.
Comparison between ILSTM and machine learning-based algorithms in binary classification.
Fig 14.
Comparison between ILSTM and deep learning-based algorithms in binary classification.
Table 9.
Comparison between other methods in literature using KDDTest+ for binary classification.
Fig 15.
Confusion matrices of ILSTM using KDDTest+ for multi-classification.
Table 10.
Comparison between LSTM, LSTM-BOA, LSTM-CBOA and ILSTM using KDDTest+ in Multi-class classification with average 10 runs.
Fig 16.
LSTM vs ILSTM for KDDTest+ in multi-class classification.
A: LSTM. B: ILSTM.
Fig 17.
Confusion matrices for KDDTest-21 in multi classification.
Table 11.
Comparison between LSTM, LSTM-BOA, LSTM-CBOA and ILSTM using KDDTes-21 in multi-class classification with average 10 runs.
Fig 18.
LSTM vs ILSTM for KDDTest-21 in multi-class classification.
A: LSTM. B: ILSTM.
Fig 19.
MSE evaluation for the ILSTM algorithm in multi-class classification.
A: KDDTest+. B: KDDTest-21.
Table 12.
Statistic test for KDDTest+ and KDDTest-21 in multi-class classificatio.
Fig 20.
Comparison with machine learning methods in multi-class classification.
Fig 21.
Comparison with deep learning methods in multi-class classification.
Table 13.
Comparison between other methods in literature using KDDTest+ for multi-class classification.
Fig 22.
Confusion matrices for LITNET-2020 dataset in binary classification.
A: LSTM. B: ILSTM.
Fig 23.
LSTM vs ILSTM for LITNET-2020 in binary classification.
A: LSTM. B: ILSTM.
Table 14.
Comparison between LSTM and ILSTM using LITNET-2020 in binary classification.
Fig 24.
Confusion matrix for LITNET-2020 dataset using LSTM algorithm.
Fig 25.
Confusion matrix for LITNET-2020 dataset using ILSTM algorithm.
Table 15.
Comparison between LSTM, ILSTM using LITNET-2020 in multi-class classification.
Fig 26.
LSTM vs ILSTM for LITNET-2020 in multi-class classification.
A: LSTM. B: ILSTM.