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        "author": "Van-Kha Nguyen",
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        "description": "# Detect Attacks: \n\nA python package which detects network attacks includes: \n\n* Collecting data from attacks \n* Classifying data to predict the risks of the network attacks \n* Warning users risks which could be a network attack.\n\n# Getting Started\n\n## Prerequisites\n\n* These packages should be installed before using detect_attacks:\n\n```\ntensorflow\t1.5.0\nsklearn\t0.19.1\nkeras\t2.1.3\nnumpy\t1.14.0\nmatplotlib\t2.1.2\ndeepmg 0.5.9\n```\n\n* Please install if you do not have them\n\n```\npip install matplotlib\npip install numpy\n\nconda install scikit-learn\nconda install -c conda-forge tensorflow \nconda install -c conda-forge keras\n\npip install Keras-Applications\npip install Keras-Preprocessing\npip install keras_sequential_ascii\n\npip install deepmg\n```\n\n## Install or Download the package detect_attacks\n```\npip install detect_attacks\n```\n\n# Running Experiments\n## How to use detect_attacks\n\n* **Input**: \n  - mandatory: csv files containing data (\\*_x.csv) and labels (\\*_y.csv) \n  - optional: if use external validation set: data (\\*_zx.csv) and labels (\\*_zy.csv)) put in [data](data/) changable with parameters *--orginal_data_folder*). \n\n  For examples, data1_x.csv and data1_y.csv for.\n\n* **Output**:\n    - *results*: performance/training/testing information of each fold and summary results put in    [results/*name_dataset_parameters_to_generate_image*/] (results/) (changable with parameters **--parent_folder_results**), includes more than 5 files: \n      - \\*file_sum.txt: parameters used to run, performance at each fold. The last rows show training/testing performance in ACC, AUC, execution time, and other metrics of the experiment. When the experiment finishes, a suffix \"_ok\" (changable with parameters **--suff_fini**) appended to the name of file marking that the experiment finishes.\n\n      - \\*file_eachfold.txt (if **--save_folds=y**): results of each fold with accuracy, auc, mcc, loss of training and testing.\n\n      - \\*file_mean_acc.txt (if **--save_avg_run=y**): if the experiment includes *n* runs repeated independently, so the file includes average performance on *k*-folds of each run measured by **accuracy** and time execution at training/testing of beginning, training/testing when finished.\n\n      - \\*file_mean_auc.txt (if **--save_avg_run=y**): if the experiment includes *n* runs repeated independently, so the file includes average performance on *k*-folds of each run measured by **AUC**  at training/testing of beginning, training/testing when finished.\n\n      - If **--save_para=y**: configuration file to repeat the experiment\n\n      - If use **--save_w=y** (save weights of trained networks) and/or **--save_entire_w=y**, **--save_d=y**, then 2 folders will be created:\n\n          - results/*name_dataset_parameters_to_generate_image*/models/: includes \\*weightmodel\\*.json contains structure of the model \\*weightmodel\\*.h5 stores weights.\n\n          - results/*name_dataset_parameters_to_generate_image*/details/\\*weight_\\*.txt: contains accuracy and loss of training and testing every epochs **--save_d=y**. If **--save_rf=y**, then we will have important scores generated from RFs for each run.\n\n## Some examples\n\n```\ndb_name='data1';\nfolder_data='/Users/hainguyen//test/data/';\nfolder_res='/Users/hainguyen//test/results/';\n\npython -m detect_attacks -i $db_name -r $folder_data --parent_folder_results $folder_res --model rf_model\npython -m detect_attacks -i $db_name -r $folder_data --parent_folder_results $folder_res --model svm_model\npython -m detect_attacks -i $db_name -r $folder_data --parent_folder_results $folder_res --model model_cnn1d\npython -m detect_attacks -i $db_name -r $folder_data --parent_folder_results $folder_res --model model_mlp\npython -m detect_attacks -i $db_name -r $folder_data --parent_folder_results $folder_res --model fc_model\n\n```\n\n## Download data\n\nPlease use the link below to download data for the experiments\n\n[darpa29f](https://drive.google.com/file/d/1xv6NhPHOQD3YxjTeQ0EgeW0px3QljXIw/view?usp=sharing)\n\n\n# References:\n\n\n\n",
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