UNSW_NB15_RNN

说明：  用UNSW数据集进行入侵检测，运用各种组合模型，精确度能达到90%以上，运用比较流行的神经网络模型分别进行了测试(Intrusion detection using UNSW dataset)

UNSW_NB15_RNN\build_model.py, 3977 , 2020-03-26
UNSW_NB15_RNN\classifier.py, 4486 , 2020-03-26
UNSW_NB15_RNN\data\encoded_test.npy, 10538624 , 2020-06-22
UNSW_NB15_RNN\data\encoded_train.npy, 22443776 , 2020-06-22
UNSW_NB15_RNN\data\readme.md, 180 , 2020-03-26
UNSW_NB15_RNN\data\test_label.npy, 658784 , 2020-06-22
UNSW_NB15_RNN\data\train_label.npy, 1402856 , 2020-06-22
UNSW_NB15_RNN\data\UNSW_NB15_testing-set.csv, 15298467 , 2018-04-29
UNSW_NB15_RNN\data\UNSW_NB15_testing-set.rar, 3503081 , 2020-03-26
UNSW_NB15_RNN\data\UNSW_NB15_training-set.csv, 32117676 , 2018-04-29
UNSW_NB15_RNN\data\UNSW_NB15_training-set.rar, 6926992 , 2020-03-26
UNSW_NB15_RNN\data_generator.py, 5836 , 2020-03-26
UNSW_NB15_RNN\data_processing.py, 1169 , 2020-03-26
UNSW_NB15_RNN\figure\framework.png, 6162 , 2020-03-26
UNSW_NB15_RNN\figure\GRU.png, 36441 , 2020-03-26
UNSW_NB15_RNN\figure\LSTM.png, 42000 , 2020-03-26
UNSW_NB15_RNN\figure\readme.md, 77 , 2020-03-26
UNSW_NB15_RNN\figure\Sparse AE.png, 33601 , 2020-03-26
UNSW_NB15_RNN\figure\wave_1.png, 778023 , 2020-03-26
UNSW_NB15_RNN\logs\events.out.tfevents.1592802476.MM-202005312146, 3104792 , 2020-06-22
UNSW_NB15_RNN\models\readme.md, 54 , 2020-03-26
UNSW_NB15_RNN\plot_wave_testing.py, 2369 , 2020-03-26
UNSW_NB15_RNN\README.md, 2695 , 2020-03-26
UNSW_NB15_RNN\saved_ae_1\best_ae_1.hdf5, 637216 , 2020-06-22
UNSW_NB15_RNN\saved_ae_1\readme.md, 45 , 2020-03-26
UNSW_NB15_RNN\saved_ae_2\best_ae_2.hdf5, 128272 , 2020-06-22
UNSW_NB15_RNN\saved_ae_2\readme.md, 45 , 2020-03-26
UNSW_NB15_RNN\saved_ae_3\best_ae_3.hdf5, 53520 , 2020-06-22
UNSW_NB15_RNN\saved_ae_3\readme.md, 45 , 2020-03-26
UNSW_NB15_RNN\saved_models_temp\best_model.hdf5, 490520 , 2020-06-22
UNSW_NB15_RNN\saved_models_temp\readme.md, 40 , 2020-03-26
UNSW_NB15_RNN\__pycache__\build_model.cpython-35.pyc, 3347 , 2020-06-22
UNSW_NB15_RNN\__pycache__\data_processing.cpython-35.pyc, 1152 , 2020-06-22
UNSW_NB15_RNN\data, 0 , 2020-06-22
UNSW_NB15_RNN\figure, 0 , 2020-03-26
UNSW_NB15_RNN\logs, 0 , 2020-06-22
UNSW_NB15_RNN\models, 0 , 2020-03-26
UNSW_NB15_RNN\saved_ae_1, 0 , 2020-06-22
UNSW_NB15_RNN\saved_ae_2, 0 , 2020-06-22
UNSW_NB15_RNN\saved_ae_3, 0 , 2020-06-22
UNSW_NB15_RNN\saved_models_temp, 0 , 2020-06-22
UNSW_NB15_RNN\__pycache__, 0 , 2020-06-22
UNSW_NB15_RNN, 0 , 2020-06-22