This project is the official implementation of the code for the paper titled "METC: A Hybrid Deep Learning Framework for Cross-Network Encrypted DNS over HTTPS Traffic Detection and Tunnel Identification". If you use this code or find it helpful in your research, please cite the paper: M. Zuo, C. Guo, H. Xu et al., METC: A Hybrid Deep Learning Framework for Cross-Network Encrypted DNS over HTTPS Traffic Detection and Tunnel Identification, Information Fusion (2025), doi: https://doi.org/10.1016/j.inffus.2025.103125.
@article{ZUO2025103125,
title = {METC: A Hybrid Deep Learning Framework for Cross-Network Encrypted DNS over HTTPS Traffic Detection and Tunnel Identification},
journal = {Information Fusion},
volume = {121},
pages = {103125},
year = {2025},
issn = {1566-2535},
doi = {https://doi.org/10.1016/j.inffus.2025.103125},
url = {https://www.sciencedirect.com/science/article/pii/S1566253525001988},
author = {Ming Zuo and Changyong Guo and Haiyan Xu and Zhaoxin Zhang and Yanan Cheng},
keywords = {DNS over HTTPS (DoH), Encrypted Traffic Detection, Tunnel Identification, Deep Learning, Machine Learning, Network Security},
}
....
This project provides a unified framework for training, fine-tuning, and testing machine learning models using various architectures like CNNs, LSTMs, Random Forests, and XGBoost. The setup supports multiple CUDA versions, enabling performance optimization on NVIDIA GPUs.
Choose the appropriate installation command based on your CUDA version. For the latest features, install PyTorch 2.4.1 and its dependencies.
conda install pytorch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 -c pytorchpip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1conda install pytorch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 pytorch-cuda=11.8 -c pytorch -c nvidiapip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cu118conda install pytorch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 pytorch-cuda=12.1 -c pytorch -c nvidiapip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cu121conda install pytorch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 pytorch-cuda=12.4 -c pytorch -c nvidiapip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cu124- CIRA-CIC-DoHBrw-2020
- doh_dataset
- Custom_dataset
- FiveWeek
- Generated
- RealWorld
- Tunnel
python main.py --model_type CNN_BiGRU_Attention --test --test_data_dir ../csv_output/doh_dataset --test_checkpoint_path CNN_BiGRU_Attention_best_model_checkpoint.pth
python main.py --model_type XGBoost --test --test_data_dir ../csv_output/RealWorld --test_checkpoint_path XGBoost_best_model_checkpoint.pkl
XGBoost: [CV 3/3] END colsample_bytree=0.8, gamma=0.1, learning_rate=0.001, max_depth=10, n_estimators=100, subsample=0.8;, score=0.995 total time= 3.1min [CV 1/3] END colsample_bytree=0.8, gamma=0.1, learning_rate=0.001, max_depth=10, n_estimators=100, subsample=0.8;, score=0.995 total time= 4.2min [CV 1/3] END colsample_bytree=0.8, gamma=0.1, learning_rate=0.001, max_depth=10, n_estimators=100, subsample=0.8;, score=0.995 total time= 4.2min [CV 1/3] END colsample_bytree=0.8, gamma=0.1, learning_rate=0.001, max_depth=10, n_estimators=100, subsample=1.0;, score=0.995 total time= 3.7min [CV 3/3] END colsample_bytree=0.8, gamma=0.1, learning_rate=0.001, max_depth=10, n_estimators=100, subsample=1.0;, score=0.995 total time= 3.4min [CV 2/3] END colsample_bytree=0.8, gamma=0.1, learning_rate=0.001, max_depth=10, n_estimators=100, subsample=1.0;, score=0.995 total time= 4.6min [CV 1/3] END colsample_bytree=0.8, gamma=0.1, learning_rate=0.001, max_depth=10, n_estimators=1100, subsample=1.0;, score=0.995 total time= 5.3min [CV 3/3] END colsample_bytree=0.8, gamma=0.1, learning_rate=0.001, max_depth=10, n_estimators=1100, subsample=0.8;, score=0.995 total time= 5.4min [CV 1/3] END colsample_bytree=0.8, gamma=0.1, learning_rate=0.001, max_depth=10, n_estimators=1100, subsample=0.8;, score=0.995 total time= 6.0min [CV 3/3] END colsample_bytree=0.8, gamma=0.1, learning_rate=0.001, max_depth=10, n_estimators=200, subsample=0.8;, score=0.995 total time= 7.3min [CV 1/3] END colsample_bytree=0.8, gamma=0.1, learning_rate=0.01, max_depth=5, n_estimators=1100, subsample=0.8;, score=0.986 total time= 2.9min [CV 2/3] END colsample_bytree=0.8, gamma=0.1, learning_rate=0.001, max_depth=10, n_estimators=200, subsample=1.0;, score=0.995 total time= 6.9min [CV 1/3] END colsample_bytree=0.8, gamma=0.1, learning_rate=0.001, max_depth=10, n_estimators=200, subsample=1.0;, score=0.995 total time= 7.6min
RandomForest XGBoost NOT RUNNING: 'CNN_Attention', 'BiGRU_Attention', 'BiLSTM_Attention'
jobs -p | xargs killnohup python main.py --model_type LogisticRegression --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 & nohup python main.py --model_type AdaBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 & nohup python main.py --model_type DecisionTree --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 & nohup python main.py --model_type NaiveBayes --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 & nohup python main.py --model_type LDA --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 & nohup python main.py --model_type ExtraTrees --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 & nohup python main.py --model_type CatBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 & nohup python main.py --model_type LightGBM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 & nohup python main.py --model_type RandomForest --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 & nohup python main.py --model_type XGBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 & nohup python main.py --model_type CNN --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 & nohup python main.py --model_type RNN --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 & nohup python main.py --model_type DNN --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 & nohup python main.py --model_type MLP --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 & nohup python main.py --model_type GRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 & nohup python main.py --model_type LSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 & nohup python main.py --model_type BiGRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 & nohup python main.py --model_type BiLSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 & nohup python main.py --model_type CNN_GRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 & nohup python main.py --model_type CNN_LSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 & nohup python main.py --model_type CNN_GRU_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 & nohup python main.py --model_type CNN_LSTM_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 & nohup python main.py --model_type CNN_BiGRU_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 & nohup python main.py --model_type CNN_BiLSTM_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 &
nohup python main.py --model_type LogisticRegression --train --epochs 100 --batch_size 256 --data_dir ../csv_output/doh_dataset & nohup python main.py --model_type AdaBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/doh_dataset & nohup python main.py --model_type DecisionTree --train --epochs 100 --batch_size 256 --data_dir ../csv_output/doh_dataset & nohup python main.py --model_type NaiveBayes --train --epochs 100 --batch_size 256 --data_dir ../csv_output/doh_dataset & nohup python main.py --model_type LDA --train --epochs 100 --batch_size 256 --data_dir ../csv_output/doh_dataset & nohup python main.py --model_type ExtraTrees --train --epochs 100 --batch_size 256 --data_dir ../csv_output/doh_dataset & nohup python main.py --model_type CatBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/doh_dataset & nohup python main.py --model_type LightGBM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/doh_dataset & nohup python main.py --model_type RandomForest --train --epochs 100 --batch_size 256 --data_dir ../csv_output/doh_dataset & nohup python main.py --model_type XGBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/doh_dataset & nohup python main.py --model_type CNN --train --epochs 100 --batch_size 256 --data_dir ../csv_output/doh_dataset & nohup python main.py --model_type RNN --train --epochs 100 --batch_size 256 --data_dir ../csv_output/doh_dataset & nohup python main.py --model_type DNN --train --epochs 100 --batch_size 256 --data_dir ../csv_output/doh_dataset & nohup python main.py --model_type MLP --train --epochs 100 --batch_size 256 --data_dir ../csv_output/doh_dataset & nohup python main.py --model_type GRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/doh_dataset & nohup python main.py --model_type LSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/doh_dataset & nohup python main.py --model_type BiGRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/doh_dataset & nohup python main.py --model_type BiLSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/doh_dataset & nohup python main.py --model_type CNN_GRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/doh_dataset & nohup python main.py --model_type CNN_LSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/doh_dataset & nohup python main.py --model_type CNN_GRU_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/doh_dataset & nohup python main.py --model_type CNN_LSTM_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/doh_dataset & nohup python main.py --model_type CNN_BiGRU_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/doh_dataset & nohup python main.py --model_type CNN_BiLSTM_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/doh_dataset &
nohup python main.py --model_type LogisticRegression --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Custom_dataset & nohup python main.py --model_type AdaBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Custom_dataset & nohup python main.py --model_type DecisionTree --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Custom_dataset & nohup python main.py --model_type NaiveBayes --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Custom_dataset & nohup python main.py --model_type LDA --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Custom_dataset & nohup python main.py --model_type ExtraTrees --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Custom_dataset & nohup python main.py --model_type CatBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Custom_dataset & nohup python main.py --model_type LightGBM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Custom_dataset & nohup python main.py --model_type RandomForest --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Custom_dataset & nohup python main.py --model_type XGBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Custom_dataset & nohup python main.py --model_type CNN --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Custom_dataset & nohup python main.py --model_type RNN --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Custom_dataset & nohup python main.py --model_type DNN --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Custom_dataset & nohup python main.py --model_type MLP --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Custom_dataset & nohup python main.py --model_type GRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Custom_dataset & nohup python main.py --model_type LSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Custom_dataset & nohup python main.py --model_type BiGRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Custom_dataset & nohup python main.py --model_type BiLSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Custom_dataset & nohup python main.py --model_type CNN_GRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Custom_dataset & nohup python main.py --model_type CNN_LSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Custom_dataset & nohup python main.py --model_type CNN_GRU_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Custom_dataset & nohup python main.py --model_type CNN_LSTM_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Custom_dataset & nohup python main.py --model_type CNN_BiGRU_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Custom_dataset & nohup python main.py --model_type CNN_BiLSTM_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Custom_dataset &
nohup python main.py --model_type LogisticRegression --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Generated & nohup python main.py --model_type AdaBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Generated & nohup python main.py --model_type DecisionTree --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Generated & nohup python main.py --model_type NaiveBayes --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Generated & nohup python main.py --model_type LDA --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Generated & nohup python main.py --model_type ExtraTrees --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Generated & nohup python main.py --model_type CatBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Generated & nohup python main.py --model_type LightGBM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Generated & nohup python main.py --model_type RandomForest --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Generated & nohup python main.py --model_type XGBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Generated & nohup python main.py --model_type CNN --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Generated & nohup python main.py --model_type RNN --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Generated & nohup python main.py --model_type DNN --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Generated & nohup python main.py --model_type MLP --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Generated & nohup python main.py --model_type GRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Generated & nohup python main.py --model_type LSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Generated & nohup python main.py --model_type BiGRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Generated & nohup python main.py --model_type BiLSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Generated & nohup python main.py --model_type CNN_GRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Generated & nohup python main.py --model_type CNN_LSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Generated & nohup python main.py --model_type CNN_GRU_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Generated & nohup python main.py --model_type CNN_LSTM_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Generated & nohup python main.py --model_type CNN_BiGRU_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Generated & nohup python main.py --model_type CNN_BiLSTM_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Generated &
nohup python main.py --model_type LogisticRegression --train --epochs 100 --batch_size 256 --data_dir ../csv_output/FiveWeek & nohup python main.py --model_type AdaBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/FiveWeek & nohup python main.py --model_type DecisionTree --train --epochs 100 --batch_size 256 --data_dir ../csv_output/FiveWeek & nohup python main.py --model_type NaiveBayes --train --epochs 100 --batch_size 256 --data_dir ../csv_output/FiveWeek & nohup python main.py --model_type LDA --train --epochs 100 --batch_size 256 --data_dir ../csv_output/FiveWeek & nohup python main.py --model_type ExtraTrees --train --epochs 100 --batch_size 256 --data_dir ../csv_output/FiveWeek & nohup python main.py --model_type CatBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/FiveWeek & nohup python main.py --model_type LightGBM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/FiveWeek & nohup python main.py --model_type RandomForest --train --epochs 100 --batch_size 256 --data_dir ../csv_output/FiveWeek & nohup python main.py --model_type XGBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/FiveWeek & nohup python main.py --model_type CNN --train --epochs 100 --batch_size 256 --data_dir ../csv_output/FiveWeek & nohup python main.py --model_type RNN --train --epochs 100 --batch_size 256 --data_dir ../csv_output/FiveWeek & nohup python main.py --model_type DNN --train --epochs 100 --batch_size 256 --data_dir ../csv_output/FiveWeek & nohup python main.py --model_type MLP --train --epochs 100 --batch_size 256 --data_dir ../csv_output/FiveWeek & nohup python main.py --model_type GRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/FiveWeek & nohup python main.py --model_type LSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/FiveWeek & nohup python main.py --model_type BiGRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/FiveWeek & nohup python main.py --model_type BiLSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/FiveWeek & nohup python main.py --model_type CNN_GRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/FiveWeek & nohup python main.py --model_type CNN_LSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/FiveWeek & nohup python main.py --model_type CNN_GRU_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/FiveWeek & nohup python main.py --model_type CNN_LSTM_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/FiveWeek & nohup python main.py --model_type CNN_BiGRU_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/FiveWeek & nohup python main.py --model_type CNN_BiLSTM_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/FiveWeek &
nohup python main.py --model_type LogisticRegression --train --epochs 100 --batch_size 256 --data_dir ../csv_output/RealWorld & nohup python main.py --model_type AdaBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/RealWorld & nohup python main.py --model_type DecisionTree --train --epochs 100 --batch_size 256 --data_dir ../csv_output/RealWorld & nohup python main.py --model_type NaiveBayes --train --epochs 100 --batch_size 256 --data_dir ../csv_output/RealWorld & nohup python main.py --model_type LDA --train --epochs 100 --batch_size 256 --data_dir ../csv_output/RealWorld & nohup python main.py --model_type ExtraTrees --train --epochs 100 --batch_size 256 --data_dir ../csv_output/RealWorld & nohup python main.py --model_type CatBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/RealWorld & nohup python main.py --model_type LightGBM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/RealWorld & nohup python main.py --model_type RandomForest --train --epochs 100 --batch_size 256 --data_dir ../csv_output/RealWorld & nohup python main.py --model_type XGBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/RealWorld & nohup python main.py --model_type CNN --train --epochs 100 --batch_size 256 --data_dir ../csv_output/RealWorld & nohup python main.py --model_type RNN --train --epochs 100 --batch_size 256 --data_dir ../csv_output/RealWorld & nohup python main.py --model_type DNN --train --epochs 100 --batch_size 256 --data_dir ../csv_output/RealWorld & nohup python main.py --model_type MLP --train --epochs 100 --batch_size 256 --data_dir ../csv_output/RealWorld & nohup python main.py --model_type GRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/RealWorld & nohup python main.py --model_type LSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/RealWorld & nohup python main.py --model_type BiGRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/RealWorld & nohup python main.py --model_type BiLSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/RealWorld & nohup python main.py --model_type CNN_GRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/RealWorld & nohup python main.py --model_type CNN_LSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/RealWorld & nohup python main.py --model_type CNN_GRU_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/RealWorld & nohup python main.py --model_type CNN_LSTM_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/RealWorld & nohup python main.py --model_type CNN_BiGRU_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/RealWorld & nohup python main.py --model_type CNN_BiLSTM_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/RealWorld &
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nohup python main.py --model_type LogisticRegression --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel & nohup python main.py --model_type AdaBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel & nohup python main.py --model_type DecisionTree --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel & nohup python main.py --model_type NaiveBayes --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel & nohup python main.py --model_type LDA --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel & nohup python main.py --model_type ExtraTrees --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel & nohup python main.py --model_type CatBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel & nohup python main.py --model_type LightGBM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel & nohup python main.py --model_type RandomForest --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel & nohup python main.py --model_type XGBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel & nohup python main.py --model_type CNN --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel & nohup python main.py --model_type RNN --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel & nohup python main.py --model_type DNN --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel & nohup python main.py --model_type MLP --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel & nohup python main.py --model_type GRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel & nohup python main.py --model_type LSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel & nohup python main.py --model_type BiGRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel & nohup python main.py --model_type BiLSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel & nohup python main.py --model_type CNN_GRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel & nohup python main.py --model_type CNN_LSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel & nohup python main.py --model_type CNN_GRU_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel & nohup python main.py --model_type CNN_LSTM_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel & nohup python main.py --model_type CNN_BiGRU_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel & nohup python main.py --model_type CNN_BiLSTM_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel &
nohup python main.py --model_type LogisticRegression --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/CIRA-CIC-DoHBrw-2020-Tunnel & nohup python main.py --model_type AdaBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/CIRA-CIC-DoHBrw-2020-Tunnel & nohup python main.py --model_type DecisionTree --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/CIRA-CIC-DoHBrw-2020-Tunnel & nohup python main.py --model_type NaiveBayes --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/CIRA-CIC-DoHBrw-2020-Tunnel & nohup python main.py --model_type LDA --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/CIRA-CIC-DoHBrw-2020-Tunnel & nohup python main.py --model_type ExtraTrees --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/CIRA-CIC-DoHBrw-2020-Tunnel & nohup python main.py --model_type CatBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/CIRA-CIC-DoHBrw-2020-Tunnel & nohup python main.py --model_type LightGBM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/CIRA-CIC-DoHBrw-2020-Tunnel & nohup python main.py --model_type RandomForest --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/CIRA-CIC-DoHBrw-2020-Tunnel & nohup python main.py --model_type XGBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/CIRA-CIC-DoHBrw-2020-Tunnel & nohup python main.py --model_type CNN --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/CIRA-CIC-DoHBrw-2020-Tunnel & nohup python main.py --model_type RNN --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/CIRA-CIC-DoHBrw-2020-Tunnel & nohup python main.py --model_type DNN --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/CIRA-CIC-DoHBrw-2020-Tunnel & nohup python main.py --model_type MLP --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/CIRA-CIC-DoHBrw-2020-Tunnel & nohup python main.py --model_type GRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/CIRA-CIC-DoHBrw-2020-Tunnel & nohup python main.py --model_type LSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/CIRA-CIC-DoHBrw-2020-Tunnel & nohup python main.py --model_type BiGRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/CIRA-CIC-DoHBrw-2020-Tunnel & nohup python main.py --model_type BiLSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/CIRA-CIC-DoHBrw-2020-Tunnel & nohup python main.py --model_type CNN_GRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/CIRA-CIC-DoHBrw-2020-Tunnel & nohup python main.py --model_type CNN_LSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/CIRA-CIC-DoHBrw-2020-Tunnel & nohup python main.py --model_type CNN_GRU_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/CIRA-CIC-DoHBrw-2020-Tunnel & nohup python main.py --model_type CNN_LSTM_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/CIRA-CIC-DoHBrw-2020-Tunnel & nohup python main.py --model_type CNN_BiGRU_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/CIRA-CIC-DoHBrw-2020-Tunnel & nohup python main.py --model_type CNN_BiLSTM_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/CIRA-CIC-DoHBrw-2020-Tunnel &
nohup python main.py --model_type LogisticRegression --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-DGA-Malware-Traffic-HKD & nohup python main.py --model_type AdaBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-DGA-Malware-Traffic-HKD & nohup python main.py --model_type DecisionTree --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-DGA-Malware-Traffic-HKD & nohup python main.py --model_type NaiveBayes --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-DGA-Malware-Traffic-HKD & nohup python main.py --model_type LDA --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-DGA-Malware-Traffic-HKD & nohup python main.py --model_type ExtraTrees --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-DGA-Malware-Traffic-HKD & nohup python main.py --model_type CatBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-DGA-Malware-Traffic-HKD & nohup python main.py --model_type LightGBM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-DGA-Malware-Traffic-HKD & nohup python main.py --model_type RandomForest --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-DGA-Malware-Traffic-HKD & nohup python main.py --model_type XGBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-DGA-Malware-Traffic-HKD & nohup python main.py --model_type CNN --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-DGA-Malware-Traffic-HKD & nohup python main.py --model_type RNN --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-DGA-Malware-Traffic-HKD & nohup python main.py --model_type DNN --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-DGA-Malware-Traffic-HKD & nohup python main.py --model_type MLP --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-DGA-Malware-Traffic-HKD & nohup python main.py --model_type GRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-DGA-Malware-Traffic-HKD & nohup python main.py --model_type LSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-DGA-Malware-Traffic-HKD & nohup python main.py --model_type BiGRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-DGA-Malware-Traffic-HKD & nohup python main.py --model_type BiLSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-DGA-Malware-Traffic-HKD & nohup python main.py --model_type CNN_GRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-DGA-Malware-Traffic-HKD & nohup python main.py --model_type CNN_LSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-DGA-Malware-Traffic-HKD & nohup python main.py --model_type CNN_GRU_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-DGA-Malware-Traffic-HKD & nohup python main.py --model_type CNN_LSTM_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-DGA-Malware-Traffic-HKD & nohup python main.py --model_type CNN_BiGRU_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-DGA-Malware-Traffic-HKD & nohup python main.py --model_type CNN_BiLSTM_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-DGA-Malware-Traffic-HKD &
nohup python main.py --model_type LogisticRegression --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-Tunnel-Traffic-HKD & nohup python main.py --model_type AdaBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-Tunnel-Traffic-HKD & nohup python main.py --model_type DecisionTree --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-Tunnel-Traffic-HKD & nohup python main.py --model_type NaiveBayes --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-Tunnel-Traffic-HKD & nohup python main.py --model_type LDA --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-Tunnel-Traffic-HKD & nohup python main.py --model_type ExtraTrees --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-Tunnel-Traffic-HKD & nohup python main.py --model_type CatBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-Tunnel-Traffic-HKD & nohup python main.py --model_type LightGBM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-Tunnel-Traffic-HKD & nohup python main.py --model_type RandomForest --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-Tunnel-Traffic-HKD & nohup python main.py --model_type XGBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-Tunnel-Traffic-HKD & nohup python main.py --model_type CNN --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-Tunnel-Traffic-HKD & nohup python main.py --model_type RNN --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-Tunnel-Traffic-HKD & nohup python main.py --model_type DNN --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-Tunnel-Traffic-HKD & nohup python main.py --model_type MLP --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-Tunnel-Traffic-HKD & nohup python main.py --model_type GRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-Tunnel-Traffic-HKD & nohup python main.py --model_type LSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-Tunnel-Traffic-HKD & nohup python main.py --model_type BiGRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-Tunnel-Traffic-HKD & nohup python main.py --model_type BiLSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-Tunnel-Traffic-HKD & nohup python main.py --model_type CNN_GRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-Tunnel-Traffic-HKD & nohup python main.py --model_type CNN_LSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-Tunnel-Traffic-HKD & nohup python main.py --model_type CNN_GRU_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-Tunnel-Traffic-HKD & nohup python main.py --model_type CNN_LSTM_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-Tunnel-Traffic-HKD & nohup python main.py --model_type CNN_BiGRU_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-Tunnel-Traffic-HKD & nohup python main.py --model_type CNN_BiLSTM_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnel/DoH-Tunnel-Traffic-HKD &
Train a model using the specified architecture and dataset.
python main.py --model_type XGBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/Tunnelpython main.py --model_type XGBoost --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020python main.py --model_type RandomForest --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020python main.py --model_type CNN --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020python main.py --model_type LSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020python main.py --model_type GRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020python main.py --model_type BiLSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020python main.py --model_type BiGRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020python main.py --model_type CNN_BiLSTM_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020python main.py --model_type CNN_BiGRU_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020python main.py --model_type CNN_BiLSTM --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020python main.py --model_type CNN_BiGRU --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020python main.py --model_type CNN_Attention --train --epochs 100 --batch_size 256 --data_dir ../csv_output/CIRA-CIC-DoHBrw-2020Fine-tune a pre-trained model on a new dataset.
python main.py --model_type CNN_BiGRU_Attention --fine_tune --fine_tune_data_dir ../csv_output/RealWorld --fine_tune_epochs 20 --best_checkpoint_path CNN_BiGRU_Attention_best_model_checkpoint.pth --sample_size 0.3 python main.py --model_type CNN_BiGRU_Attention --test --test_data_dir ../csv_output/RealWorld --test_checkpoint_path CNN_BiGRU_Attention_fine_tuned_best_model.pth
python main.py --model_type XGBoost --fine_tune --fine_tune_data_dir ../csv_output/RealWorld --fine_tune_epochs 20 --best_checkpoint_path XGBoost_best_model_checkpoint.pkl --sample_size 0.3 python main.py --model_type XGBoost --test --test_data_dir ../csv_output/RealWorld --test_checkpoint_path XGBoost_fine_tuned_model.pkl
python main.py --model_type CNN --fine_tune --fine_tune_data_dir ../csv_output/RealWorld --fine_tune_epochs 10 --best_checkpoint_path CNN_best_model_checkpoint.pth --sample_size 0.1 python main.py --model_type CNN --test --test_data_dir ../csv_output/RealWorld --test_checkpoint_path CNN_fine_tuned_best_model.pth
python main.py --model_type LSTM --fine_tune --fine_tune_data_dir ../csv_output/RealWorld --fine_tune_epochs 10 --best_checkpoint_path LSTM_best_model_checkpoint.pth --sample_size 0.1 python main.py --model_type LSTM --test --test_data_dir ../csv_output/RealWorld --test_checkpoint_path LSTM_fine_tuned_best_model.pth
python main.py --model_type GRU --fine_tune --fine_tune_data_dir ../csv_output/RealWorld --fine_tune_epochs 10 --best_checkpoint_path GRU_best_model_checkpoint.pth --sample_size 0.1 python main.py --model_type GRU --test --test_data_dir ../csv_output/RealWorld --test_checkpoint_path GRU_fine_tuned_best_model.pth
python main.py --model_type BiLSTM --fine_tune --fine_tune_data_dir ../csv_output/RealWorld --fine_tune_epochs 10 --best_checkpoint_path BiLSTM_best_model_checkpoint.pth --sample_size 0.1 python main.py --model_type BiLSTM --test --test_data_dir ../csv_output/RealWorld --test_checkpoint_path BiLSTM_fine_tuned_best_model.pth
python main.py --model_type BiGRU --fine_tune --fine_tune_data_dir ../csv_output/RealWorld --fine_tune_epochs 10 --best_checkpoint_path BiGRU_best_model_checkpoint.pth --sample_size 0.1 python main.py --model_type BiGRU --test --test_data_dir ../csv_output/RealWorld --test_checkpoint_path BiGRU_fine_tuned_best_model.pth
python main.py --model_type RNN --fine_tune --fine_tune_data_dir ../csv_output/RealWorld --fine_tune_epochs 10 --best_checkpoint_path RNN_best_model_checkpoint.pth --sample_size 0.1 python main.py --model_type RNN --test --test_data_dir ../csv_output/RealWorld --test_checkpoint_path RNN_fine_tuned_best_model.pth
python main.py --model_type DNN --fine_tune --fine_tune_data_dir ../csv_output/RealWorld --fine_tune_epochs 10 --best_checkpoint_path DNN_best_model_checkpoint.pth --sample_size 0.1 python main.py --model_type DNN --test --test_data_dir ../csv_output/RealWorld --test_checkpoint_path DNN_fine_tuned_best_model.pth
python main.py --model_type MLP --fine_tune --fine_tune_data_dir ../csv_output/RealWorld --fine_tune_epochs 10 --best_checkpoint_path MLP_best_model_checkpoint.pth --sample_size 0.1 python main.py --model_type MLP --test --test_data_dir ../csv_output/RealWorld --test_checkpoint_path MLP_fine_tuned_best_model.pth
python main.py --model_type CNN_GRU --fine_tune --fine_tune_data_dir ../csv_output/RealWorld --fine_tune_epochs 10 --best_checkpoint_path CNN_GRU_best_model_checkpoint.pth --sample_size 0.1 python main.py --model_type CNN_GRU --test --test_data_dir ../csv_output/RealWorld --test_checkpoint_path CNN_GRU_fine_tuned_best_model.pth
python main.py --model_type CNN_LSTM --fine_tune --fine_tune_data_dir ../csv_output/RealWorld --fine_tune_epochs 10 --best_checkpoint_path CNN_LSTM_best_model_checkpoint.pth --sample_size 0.1 python main.py --model_type CNN_LSTM --test --test_data_dir ../csv_output/RealWorld --test_checkpoint_path CNN_LSTM_fine_tuned_best_model.pth
python main.py --model_type CNN_GRU_Attention --fine_tune --fine_tune_data_dir ../csv_output/RealWorld --fine_tune_epochs 10 --best_checkpoint_path CNN_GRU_Attention_best_model_checkpoint.pth --sample_size 0.1 python main.py --model_type CNN_GRU_Attention --test --test_data_dir ../csv_output/RealWorld --test_checkpoint_path CNN_GRU_Attention_fine_tuned_best_model.pth
python main.py --model_type CNN_LSTM_Attention --fine_tune --fine_tune_data_dir ../csv_output/RealWorld --fine_tune_epochs 10 --best_checkpoint_path CNN_LSTM_Attention_best_model_checkpoint.pth --sample_size 0.1 python main.py --model_type CNN_LSTM_Attention --test --test_data_dir ../csv_output/RealWorld --test_checkpoint_path CNN_LSTM_Attention_fine_tuned_best_model.pth
python main.py --model_type CNN_BiLSTM_Attention --fine_tune --fine_tune_data_dir ../csv_output/RealWorld --fine_tune_epochs 10 --best_checkpoint_path CNN_BiLSTM_Attention_best_model_checkpoint.pth --sample_size 0.1 python main.py --model_type CNN_BiLSTM_Attention --test --test_data_dir ../csv_output/RealWorld --test_checkpoint_path CNN_BiLSTM_Attention_fine_tuned_best_model.pth
python main.py --model_type CNN_BiLSTM --fine_tune --fine_tune_data_dir ../csv_output/RealWorld --fine_tune_epochs 10 --best_checkpoint_path CNN_BiLSTM_best_model_checkpoint.pth --sample_size 0.1 python main.py --model_type CNN_BiLSTM --test --test_data_dir ../csv_output/RealWorld --test_checkpoint_path CNN_BiLSTM_fine_tuned_best_model.pth
python main.py --model_type CNN_Attention --fine_tune --fine_tune_data_dir ../csv_output/RealWorld --fine_tune_epochs 10 --best_checkpoint_path CNN_Attention_best_model_checkpoint.pth --sample_size 0.1 python main.py --model_type CNN_Attention --test --test_data_dir ../csv_output/RealWorld --test_checkpoint_path CNN_Attention_fine_tuned_best_model.pth
python main.py --model_type RandomForest --fine_tune --fine_tune_data_dir ../csv_output/RealWorld --fine_tune_epochs 10 --best_checkpoint_path RandomForest_best_model_checkpoint.pkl --sample_size 0.1 python main.py --model_type RandomForest --test --test_data_dir ../csv_output/RealWorld --test_checkpoint_path RandomForest_fine_tuned_model.pkl
python main.py --model_type LogisticRegression --fine_tune --fine_tune_data_dir ../csv_output/RealWorld --fine_tune_epochs 10 --best_checkpoint_path LogisticRegression_best_model_checkpoint.pkl --sample_size 0.1 python main.py --model_type LogisticRegression --test --test_data_dir ../csv_output/RealWorld --test_checkpoint_path LogisticRegression_fine_tuned_model.pkl
python main.py --model_type AdaBoost --fine_tune --fine_tune_data_dir ../csv_output/RealWorld --fine_tune_epochs 10 --best_checkpoint_path AdaBoost_best_model_checkpoint.pkl --sample_size 0.1 python main.py --model_type AdaBoost --test --test_data_dir ../csv_output/RealWorld --test_checkpoint_path AdaBoost_fine_tuned_model.pkl
python main.py --model_type DecisionTree --fine_tune --fine_tune_data_dir ../csv_output/RealWorld --fine_tune_epochs 10 --best_checkpoint_path DecisionTree_best_model_checkpoint.pkl --sample_size 0.1 python main.py --model_type DecisionTree --test --test_data_dir ../csv_output/RealWorld --test_checkpoint_path DecisionTree_fine_tuned_model.pkl
python main.py --model_type NaiveBayes --fine_tune --fine_tune_data_dir ../csv_output/RealWorld --fine_tune_epochs 10 --best_checkpoint_path NaiveBayes_best_model_checkpoint.pkl --sample_size 0.1 python main.py --model_type NaiveBayes --test --test_data_dir ../csv_output/RealWorld --test_checkpoint_path NaiveBayes_fine_tuned_model.pkl
python main.py --model_type LDA --fine_tune --fine_tune_data_dir ../csv_output/RealWorld --fine_tune_epochs 10 --best_checkpoint_path LDA_best_model_checkpoint.pkl --sample_size 0.1 python main.py --model_type LDA --test --test_data_dir ../csv_output/RealWorld --test_checkpoint_path LDA_fine_tuned_model.pkl
python main.py --model_type ExtraTrees --fine_tune --fine_tune_data_dir ../csv_output/RealWorld --fine_tune_epochs 10 --best_checkpoint_path ExtraTrees_best_model_checkpoint.pkl --sample_size 0.1 python main.py --model_type ExtraTrees --test --test_data_dir ../csv_output/RealWorld --test_checkpoint_path ExtraTrees_fine_tuned_model.pkl
python main.py --model_type CatBoost --fine_tune --fine_tune_data_dir ../csv_output/RealWorld --fine_tune_epochs 10 --best_checkpoint_path CatBoost_best_model_checkpoint.pkl --sample_size 0.1 python main.py --model_type CatBoost --test --test_data_dir ../csv_output/RealWorld --test_checkpoint_path CatBoost_fine_tuned_model.pkl
python main.py --model_type LightGBM --fine_tune --fine_tune_data_dir ../csv_output/RealWorld --fine_tune_epochs 10 --best_checkpoint_path LightGBM_best_model_checkpoint.pkl --sample_size 0.1 python main.py --model_type LightGBM --test --test_data_dir ../csv_output/RealWorld --test_checkpoint_path LightGBM_fine_tuned_model.pkl
python main.py --model_type CNN_BiLSTM_Attention --fine_tune --fine_tune_data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 --fine_tune_epochs 10 --best_checkpoint_path CNN_BiLSTM_Attention_best_model_checkpoint.pthpython main.py --model_type BiLSTM --fine_tune --fine_tune_data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 --fine_tune_epochs 10 --best_checkpoint_path BiLSTM_best_model_checkpoint.pthpython main.py --model_type RandomForest --fine_tune --fine_tune_data_dir ../csv_output/doh_dataset --fine_tune_epochs 10 --best_checkpoint_path RandomForest_model.pkl --sample_size 0.1python main.py --model_type XGBoost --fine_tune --fine_tune_data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 --fine_tune_epochs 10 --best_checkpoint_path XGBoost_model.pklCNN_BiLSTM_best_model_checkpoint.pth
Evaluate the performance of a trained or fine-tuned model on a test dataset(never used before).
python main.py --model_type CNN_BiLSTM --test --test_data_dir ../csv_output/Custom_dataset --test_checkpoint_path CNN_BiLSTM_best_model_checkpoint.pthpython main.py --model_type CNN_BiLSTM_Attention --test --test_data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 --test_checkpoint_path CNN_BiLSTM_Attention_fine_tuned_best_model.pthpython main.py --model_type BiLSTM --test --test_data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 --test_checkpoint_path BiLSTM_fine_tuned_model.pthpython main.py --model_type XGBoost --test --test_data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 --test_checkpoint_path XGBoost_fine_tuned_model.pklpython main.py --model_type RandomForest --test --test_data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 --test_checkpoint_path RandomForest_fine_tuned_model.pklpython main.py --model_type XGBoost --test --test_data_dir ../csv_output/RealWorld --test_checkpoint_path XGBoost_best_model_checkpoint.pklExplain
python main.py --explain --model_type CNN_BiLSTM_Attention --explain_checkpoint_path CNN_BiLSTM_Attention_best_model_checkpoint.pth --explain_data_dir ../csv_output/doh_dataset --data_dir ../csv_output/doh_datasetpython main.py --explain --model_type CNN_BiGRU_Attention --explain_checkpoint_path CNN_BiLSTM_best_model_checkpoint.pth --explain_data_dir ../csv_output/doh_dataset --data_dir ../csv_output/doh_datasetpython main.py --explain --model_type CNN_BiLSTM --explain_checkpoint_path CNN_BiLSTM_best_model_checkpoint.pth --explain_data_dir ../csv_output/doh_dataset --data_dir ../csv_output/doh_datasetpython main.py --explain --model_type CNN_BiGRU --explain_checkpoint_path CNN_BiGRU_best_model_checkpoint.pth --explain_data_dir ../csv_output/doh_dataset --data_dir ../csv_output/doh_datasetpython main.py --explain --model_type BiLSTM --explain_checkpoint_path BiLSTM_best_model_checkpoint.pth --explain_data_dir ../csv_output/doh_dataset --data_dir ../csv_output/doh_datasetpython main.py --explain --model_type BiGRU --explain_checkpoint_path BiGRU_best_model_checkpoint.pth --explain_data_dir ../csv_output/doh_dataset --data_dir ../csv_output/doh_datasetpython main.py --explain --model_type CNN --explain_checkpoint_path CNN_best_model_checkpoint.pth --explain_data_dir ../csv_output/doh_dataset --data_dir ../csv_output/doh_datasetpython main.py --explain --model_type RandomForest --explain_checkpoint_path RandomForest_best_model_checkpoint.pkl --explain_data_dir ../csv_output/doh_dataset --data_dir ../csv_output/doh_datasetpython main.py --explain --model_type XGBoost --explain_checkpoint_path XGBoost_best_model_checkpoint.pkl --explain_data_dir ../csv_output/doh_dataset --data_dir ../csv_output/doh_dataset- Train the model on the primary dataset.
- Save the best-performing checkpoint for future fine-tuning or testing.
python main.py --model_type CNN_BiLSTM_Attention --train --epochs 10 --batch_size 256 --data_dir ../csv_output/doh_dataset- Load the pre-trained checkpoint.
- Train the model on a new, related dataset to adapt it to specific tasks.
python main.py --model_type BiLSTM --fine_tune --fine_tune_data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 --fine_tune_epochs 10 --best_checkpoint_path BiLSTM_best_model_checkpoint.pth- Use the fine-tuned or pre-trained model to evaluate its performance on the test set.
python main.py --model_type XGBoost --test --test_data_dir ../csv_output/CIRA-CIC-DoHBrw-2020 --best_checkpoint_path XGBoost_fine_tuned_model.pkl- Ensure your environment is configured with the correct dependencies and library versions.
- Verify your CUDA installation to match the required configurations for maximum GPU utilization.
- Follow the PyTorch Installation Guide for step-by-step instructions to set up PyTorch with the appropriate CUDA version.
We greatly value contributions and feedback from the community. If you would like to contribute to this project or encounter any issues:
- Open an issue on this repository to report bugs or suggest new features.
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