文件名称:UNSW_NB15_RNN
介绍说明－－下载内容均来自于网络，请自行研究使用
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)
相关搜索： 基于SGM-CNN入侵检测
(系统自动生成,下载前可以参看下载内容)
下载文件列表
| 文件名 | 大小 | 更新时间 |
|---|---|---|
| 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 |