├── .gitignore ├── Deep-Drop ├── core │ ├── __init__.py │ ├── config.py │ ├── deepdrop.py │ ├── logging.py │ ├── models │ │ ├── DecisionTree.pkl │ │ ├── minmax_scaler.bin │ │ └── neuralnetwork │ │ │ ├── saved_model.pb │ │ │ └── variables │ │ │ ├── variables.data-00000-of-00001 │ │ │ └── variables.index │ ├── routing.py │ └── utils.py ├── deepdrop.py └── training │ ├── data │ └── data_process_count.csv │ ├── training_dt.ipynb │ └── training_nn.ipynb ├── LICENSE ├── README.md └── requirements.txt /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | __pycache__ 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | .pytest_cache/ 49 | 50 | # Translations 51 | *.mo 52 | *.pot 53 | 54 | # Django stuff: 55 | *.log 56 | local_settings.py 57 | db.sqlite3 58 | 59 | # Flask stuff: 60 | instance/ 61 | .webassets-cache 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # Jupyter Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # SageMath parsed files 82 | *.sage.py 83 | 84 | # Environments 85 | .env 86 | .venv 87 | env/ 88 | venv/ 89 | ENV/ 90 | env.bak/ 91 | venv.bak/ 92 | 93 | # Spyder project settings 94 | .spyderproject 95 | .spyproject 96 | 97 | # Rope project settings 98 | .ropeproject 99 | 100 | # mkdocs documentation 101 | /site 102 | 103 | # mypy 104 | .mypy_cache/ 105 | 106 | # vscode 107 | .vscode/* 108 | .vscode/settings.json 109 | .vscode/tasks.json 110 | .vscode/launch.json 111 | .vscode/extensions.json 112 | 113 | 114 | /Deep-Drop/.vscode/* 115 | 116 | # test dlls 117 | *.dll -------------------------------------------------------------------------------- /Deep-Drop/core/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/moohax/Deep-Drop/36518ee560e3b92a4a2da5ba7ae6efab123abbea/Deep-Drop/core/__init__.py -------------------------------------------------------------------------------- /Deep-Drop/core/config.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | # domain 4 | domain = 'localhost' 5 | 6 | # root dir 7 | basedir = os.path.dirname(os.path.abspath(__file__)) 8 | 9 | # trained models 10 | trained_models = { 11 | 'decisiontree': os.path.join(basedir, 'models', 'decisiontree.pkl'), 12 | 'neuralnetwork': os.path.join(basedir, 'models', 'neuralnetwork'), 13 | 'nn_scaler': os.path.join(basedir, 'models', 'minmax_scaler.bin') 14 | } -------------------------------------------------------------------------------- /Deep-Drop/core/deepdrop.py: -------------------------------------------------------------------------------- 1 | import re 2 | import base64 3 | import numpy 4 | import uuid 5 | from collections import namedtuple 6 | 7 | from core import config 8 | from core import logging 9 | from core import utils 10 | 11 | Models = None 12 | 13 | def load_models(config): 14 | global Models 15 | Models = utils.Models() 16 | Models.NeuralNetworkClassifier(config.trained_models['neuralnetwork'], config.trained_models['nn_scaler']) 17 | Models.DecisionTreeClassifier(config.trained_models['decisiontree']) 18 | 19 | def predict(features): 20 | if len(features) < 3: 21 | return 'Not enough features' 22 | 23 | ProcessList = utils.ProcessList(features) 24 | Models.predict(ProcessList) 25 | 26 | return ProcessList.predictions -------------------------------------------------------------------------------- /Deep-Drop/core/logging.py: -------------------------------------------------------------------------------- 1 | import sys 2 | from termcolor import colored 3 | 4 | def warn(msg, sameline=False): 5 | log(msg, 2, sameline) 6 | 7 | def error(msg, sameline=False): 8 | log(msg, 3, sameline) 9 | 10 | def debug(msg, sameline=False): 11 | log(msg, 4, sameline) 12 | 13 | def success(msg, sameline=False): 14 | log(msg, 1, sameline) 15 | 16 | def print(msg, sameline=False): 17 | log(msg, 0, sameline) 18 | 19 | def log(msg, msgType, sameline=False, color=None): 20 | 21 | if (isinstance(msg, int)): 22 | msg = str(msg) 23 | 24 | if msgType == 1: 25 | msg = colored("[+] ", 'green') + msg 26 | elif msgType == 2: 27 | msg = colored("[-] ", 'yellow') + msg 28 | elif msgType == 3: 29 | msg = colored("[!] ", 'red') + msg 30 | elif msgType == 4: 31 | msg = colored("[DBG] ", 'blue') + msg 32 | 33 | if color: 34 | msg = colored(msg, color) 35 | 36 | try: 37 | if not sameline: 38 | sys.stdout.write('\r' + msg + '\r\n') 39 | else: 40 | sys.stdout.write(msg) 41 | except: 42 | if not sameline: 43 | sys.stdout.write(b'\r' + msg.encode() + b'\r\n') 44 | else: 45 | sys.stdout.write(msg.encode()) 46 | try: 47 | sys.stdout.flush() 48 | except: 49 | # traceback.print_exc() 50 | pass 51 | -------------------------------------------------------------------------------- /Deep-Drop/core/models/DecisionTree.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/moohax/Deep-Drop/36518ee560e3b92a4a2da5ba7ae6efab123abbea/Deep-Drop/core/models/DecisionTree.pkl -------------------------------------------------------------------------------- /Deep-Drop/core/models/minmax_scaler.bin: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/moohax/Deep-Drop/36518ee560e3b92a4a2da5ba7ae6efab123abbea/Deep-Drop/core/models/minmax_scaler.bin -------------------------------------------------------------------------------- /Deep-Drop/core/models/neuralnetwork/saved_model.pb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/moohax/Deep-Drop/36518ee560e3b92a4a2da5ba7ae6efab123abbea/Deep-Drop/core/models/neuralnetwork/saved_model.pb -------------------------------------------------------------------------------- /Deep-Drop/core/models/neuralnetwork/variables/variables.data-00000-of-00001: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/moohax/Deep-Drop/36518ee560e3b92a4a2da5ba7ae6efab123abbea/Deep-Drop/core/models/neuralnetwork/variables/variables.data-00000-of-00001 -------------------------------------------------------------------------------- /Deep-Drop/core/models/neuralnetwork/variables/variables.index: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/moohax/Deep-Drop/36518ee560e3b92a4a2da5ba7ae6efab123abbea/Deep-Drop/core/models/neuralnetwork/variables/variables.index -------------------------------------------------------------------------------- /Deep-Drop/core/routing.py: -------------------------------------------------------------------------------- 1 | from flask import request, jsonify 2 | from core import logging 3 | from core import utils 4 | from core import deepdrop 5 | 6 | def setup_routes(app): 7 | 8 | @app.route('/api/v1/predict', methods=['GET']) 9 | def predict(): 10 | 11 | process_count = request.args.get('process_count', type=float) 12 | user_count = request.args.get('user_count', type=float) 13 | process_user_ratio = request.args.get('process_user_ratio', type=float) 14 | 15 | predictions = deepdrop.predict([process_count, user_count, process_user_ratio]) 16 | 17 | return str(predictions) 18 | -------------------------------------------------------------------------------- /Deep-Drop/core/utils.py: -------------------------------------------------------------------------------- 1 | import base64 2 | import re 3 | from uuid import uuid4 4 | from datetime import date 5 | from collections import namedtuple 6 | 7 | from core import logging 8 | import numpy as np 9 | import pickle 10 | 11 | from tensorflow.keras.models import load_model 12 | # from sklearn.preprocessing import MinMaxScaler 13 | 14 | # Decision Tree Imports 15 | from sklearn.tree import DecisionTreeClassifier 16 | from sklearn.externals.joblib import dump 17 | from sklearn.externals.joblib import load 18 | 19 | class Models(object): 20 | def __init__(self): 21 | self.nn_clf = None 22 | self.dt_clf = None 23 | self.minmax_scaler = None 24 | 25 | def DecisionTreeClassifier(self, model_file): 26 | self.dt_clf = pickle.load(open(model_file, 'rb')) 27 | 28 | def NeuralNetworkClassifier(self, model_file, scaler_file): 29 | self.nn_clf = load_model(model_file) 30 | self.minmax_scaler = load(scaler_file) 31 | 32 | def predict(self, ProcessList): 33 | scaled_features = self.minmax_scaler.transform(ProcessList.features['raw']) 34 | ProcessList.features['min_max_scaled'].append(scaled_features) 35 | 36 | #ProcessList.predictions['neural_network'] = self.nn_clf.predict_classes(ProcessList.features['min_max_scaled'])[0][0] 37 | ProcessList.predictions['decision_tree'] = self.dt_clf.predict(ProcessList.features['raw'])[0] 38 | 39 | class ProcessList(object): 40 | def __init__(self, features): 41 | self.uid = uuid4() 42 | self.date = date.today() 43 | self.parsed_data = [] 44 | self.features = {'raw': [features], 'min_max_scaled': []} 45 | self.predictions = {} -------------------------------------------------------------------------------- /Deep-Drop/deepdrop.py: -------------------------------------------------------------------------------- 1 | import os 2 | import uuid 3 | import argparse 4 | 5 | from flask import Flask 6 | from core import config 7 | from core import routing 8 | from core import logging 9 | from core import deepdrop 10 | 11 | 12 | def main(args): 13 | logging.print(''' 14 | ____ ____ 15 | | \ ___ ___ ___| \ ___ ___ ___ 16 | | | | -_| -_| . | | | _| . | . | 17 | |____/|___|___| _|____/|_| |___| _| 18 | |_| |_| 19 | ''') 20 | 21 | app = Flask(__name__) 22 | 23 | try: 24 | # Load the models 25 | deepdrop.load_models(config) 26 | logging.success('All models loaded') 27 | 28 | # Setup our routes 29 | routing.setup_routes(app) 30 | 31 | except Exception as e: 32 | logging.error(str(e)) 33 | 34 | app.run('0.0.0.0', 80, threaded=False, use_reloader=False) # No threading because https://github.com/keras-team/keras/issues/2397 35 | 36 | if __name__ == '__main__': 37 | parser = argparse.ArgumentParser(description='Math enabled dropper for fun and safety') 38 | parser.add_argument('-d', '--debug', default=False, required=False, action='store_true', help='Will return the entire process list object for examination') 39 | args = parser.parse_args() 40 | main(args) -------------------------------------------------------------------------------- /Deep-Drop/training/data/data_process_count.csv: -------------------------------------------------------------------------------- 1 | 79, 39.5, 2, 0 2 | 57, 28.5, 2, 0 3 | 111, 55.5, 2, 0 4 | 81, 11.571428571428571, 7, 0 5 | 118, 59.0, 2, 0 6 | 32, 10.666666666666666, 3, 1 7 | 131, 65.5, 2, 0 8 | 43, 10.75, 4, 0 9 | 42, 8.4, 5, 0 10 | 50, 50.0, 1, 0 11 | 47, 15.666666666666666, 3, 0 12 | 46, 23.0, 2, 0 13 | 49, 49.0, 1, 0 14 | 52, 26.0, 2, 0 15 | 35, 8.75, 4, 1 16 | 32, 10.666666666666666, 3, 0 17 | 45, 22.5, 2, 0 18 | 72, 36.0, 2, 0 19 | 88, 44.0, 2, 0 20 | 66, 16.5, 4, 0 21 | 92, 46.0, 2, 0 22 | 61, 30.5, 2, 0 23 | 71, 35.5, 2, 0 24 | 117, 58.5, 2, 0 25 | 77, 38.5, 2, 0 26 | 83, 41.5, 2, 0 27 | 34, 17.0, 2, 0 28 | 116, 58.0, 2, 0 29 | 45, 22.5, 2, 0 30 | 66, 33.0, 2, 0 31 | 66, 33.0, 2, 0 32 | 56, 14.0, 4, 0 33 | 48, 12.0, 4, 0 34 | 51, 12.75, 4, 0 35 | 61, 15.25, 4, 0 36 | 47, 23.5, 2, 0 37 | 45, 11.25, 4, 0 38 | 49, 12.25, 4, 0 39 | 58, 29.0, 2, 0 40 | 82, 41.0, 2, 0 41 | 68, 17.0, 4, 0 42 | 68, 17.0, 4, 0 43 | 48, 12.0, 4, 0 44 | 60, 15.0, 4, 0 45 | 65, 32.5, 2, 0 46 | 50, 25.0, 2, 0 47 | 39, 19.5, 2, 0 48 | 76, 76.0, 1, 0 49 | 30, 15.0, 2, 1 50 | 35, 17.5, 2, 1 51 | 18, 4.5, 4, 1 52 | 99, 49.5, 2, 0 53 | 36, 18.0, 2, 1 54 | 11, 2.75, 4, 1 55 | 123, 61.5, 2, 0 56 | 120, 120.0, 1, 0 57 | 96, 19.2, 5, 0 58 | 13, 3.25, 4, 1 59 | 114, 57.0, 2, 0 60 | 18, 4.5, 4, 1 61 | 16, 4.0, 4, 1 62 | 62, 15.5, 4, 0 63 | 22, 11.0, 2, 1 64 | 37, 18.5, 2, 0 65 | 56, 14.0, 4, 0 66 | 40, 10.0, 4, 0 67 | 39, 9.75, 4, 1 68 | 132, 132.0, 1, 0 69 | 56, 28.0, 2, 0 70 | 129, 64.5, 2, 0 71 | 89, 44.5, 2, 0 72 | 60, 15.0, 4, 0 73 | 37, 2.176470588235294, 17, 0 74 | 45, 9.0, 5, 0 75 | 53, 8.833333333333334, 6, 0 76 | 116, 58.0, 2, 0 77 | 62, 31.0, 2, 0 78 | 78, 39.0, 2, 0 79 | 37, 9.25, 4, 0 80 | 41, 8.2, 5, 0 81 | 57, 14.25, 4, 0 82 | 16, 4.0, 4, 1 83 | 51, 25.5, 2, 0 84 | 46, 11.5, 4, 0 85 | 35, 8.75, 4, 1 86 | 152, 152.0, 1, 0 87 | 61, 61.0, 1, 0 88 | 33, 4.714285714285714, 7, 0 89 | 54, 54.0, 1, 0 90 | 155, 155.0, 1, 0 91 | 127, 127.0, 1, 0 92 | 148, 148.0, 1, 0 93 | 135, 135.0, 1, 0 94 | 38, 19.0, 2, 1 95 | 128, 64.0, 2, 0 96 | 93, 46.5, 2, 0 97 | 40, 20.0, 2, 0 98 | 134, 67.0, 2, 0 99 | 72, 36.0, 2, 0 100 | 63, 31.5, 2, 0 101 | 30, 15.0, 2, 1 102 | 89, 44.5, 2, 0 103 | 55, 27.5, 2, 0 104 | 89, 44.5, 2, 0 105 | 44, 7.333333333333333, 6, 0 106 | 33, 11.0, 3, 1 107 | 62, 15.5, 4, 0 108 | 43, 14.333333333333334, 3, 0 109 | 48, 12.0, 4, 0 110 | 47, 15.666666666666666, 3, 0 111 | 73, 36.5, 2, 0 112 | 106, 53.0, 2, 0 113 | 134, 67.0, 2, 0 114 | 49, 24.5, 2, 0 115 | 81, 40.5, 2, 0 116 | 50, 25.0, 2, 0 117 | 76, 38.0, 2, 0 118 | 75, 37.5, 2, 0 119 | 39, 19.5, 2, 1 120 | 51, 25.5, 2, 0 121 | 29, 14.5, 2, 0 122 | 9, 4.5, 2, 0 123 | 22, 11.0, 2, 0 124 | 72, 36.0, 2, 0 125 | 289, 289.0, 1, 0 126 | 304, 304.0, 1, 0 127 | 63, 31.5, 2, 0 128 | 271, 271.0, 1, 0 129 | 289, 289.0, 1, 0 130 | 242, 242.0, 1, 0 131 | 305, 305.0, 1, 0 132 | 271, 271.0, 1, 0 133 | 267, 267.0, 1, 0 134 | 275, 275.0, 1, 0 135 | 243, 243.0, 1, 0 136 | 102, 51.0, 2, 0 137 | 205, 205.0, 1, 0 138 | 170, 170.0, 1, 0 139 | 175, 175.0, 1, 0 140 | 206, 206.0, 1, 0 141 | 172, 86.0, 2, 0 142 | 124, 62.0, 2, 0 143 | 175, 87.5, 2, 0 144 | 171, 171.0, 1, 0 145 | 24, 4.0, 6, 0 146 | 30, 3.0, 10, 0 147 | 60, 30.0, 2, 0 148 | 33, 3.6666666666666665, 9, 0 149 | 56, 28.0, 2, 1 150 | 68, 34.0, 2, 0 151 | 12, 3.0, 4, 1 152 | 72, 36.0, 2, 0 153 | 62, 31.0, 2, 0 154 | 66, 33.0, 2, 0 155 | 63, 31.5, 2, 0 156 | 69, 34.5, 2, 0 157 | 76, 38.0, 2, 0 158 | 76, 38.0, 2, 0 159 | 66, 33.0, 2, 0 160 | 77, 38.5, 2, 0 161 | 67, 33.5, 2, 0 162 | 37, 18.5, 2, 1 163 | 134, 67.0, 2, 0 164 | 75, 37.5, 2, 0 165 | 64, 32.0, 2, 0 166 | 61, 30.5, 2, 0 167 | 38, 19.0, 2, 1 168 | 103, 51.5, 2, 0 169 | 81, 40.5, 2, 0 170 | 41, 20.5, 2, 1 171 | 40, 20.0, 2, 1 172 | 71, 35.5, 2, 0 173 | 30, 7.5, 4, 1 174 | 29, 14.5, 2, 1 175 | 138, 69.0, 2, 0 176 | 35, 11.666666666666666, 3, 1 177 | 88, 88.0, 1, 0 178 | 71, 71.0, 1, 0 179 | 42, 14.0, 3, 0 180 | 74, 37.0, 2, 0 181 | 71, 35.5, 2, 0 182 | 12, 3.0, 4, 1 183 | 47, 47.0, 1, 0 184 | 68, 34.0, 2, 0 185 | 131, 65.5, 2, 0 186 | 53, 26.5, 2, 0 187 | 59, 14.75, 4, 0 188 | 64, 32.0, 2, 0 189 | 46, 23.0, 2, 0 190 | 142, 71.0, 2, 0 191 | 44, 44, 1, 1 192 | 50, 12.5, 4, 1 193 | 79, 39.5, 2, 0 194 | 57, 28.5, 2, 0 195 | 111, 55.5, 2, 0 196 | 81, 11.571428571428571, 7, 0 197 | 118, 59.0, 2, 0 198 | 32, 10.666666666666666, 3, 1 199 | 131, 65.5, 2, 0 200 | 43, 10.75, 4, 0 201 | 42, 8.4, 5, 0 202 | 50, 50.0, 1, 0 203 | 47, 15.666666666666666, 3, 0 204 | 46, 23.0, 2, 0 205 | 49, 49.0, 1, 0 206 | 52, 26.0, 2, 0 207 | 35, 8.75, 4, 1 208 | 32, 10.666666666666666, 3, 0 209 | 45, 22.5, 2, 0 210 | 72, 36.0, 2, 0 211 | 88, 44.0, 2, 0 212 | 66, 16.5, 4, 0 213 | 92, 46.0, 2, 0 214 | 61, 30.5, 2, 0 215 | 71, 35.5, 2, 0 216 | 117, 58.5, 2, 0 217 | 77, 38.5, 2, 0 218 | 83, 41.5, 2, 0 219 | 34, 17.0, 2, 0 220 | 116, 58.0, 2, 0 221 | 45, 22.5, 2, 0 222 | 66, 33.0, 2, 0 223 | 66, 33.0, 2, 0 224 | 56, 14.0, 4, 0 225 | 48, 12.0, 4, 0 226 | 51, 12.75, 4, 0 227 | 61, 15.25, 4, 0 228 | 47, 23.5, 2, 0 229 | 45, 11.25, 4, 0 230 | 49, 12.25, 4, 0 231 | 58, 29.0, 2, 0 232 | 82, 41.0, 2, 0 233 | 68, 17.0, 4, 0 234 | 68, 17.0, 4, 0 235 | 48, 12.0, 4, 0 236 | 60, 15.0, 4, 0 237 | 65, 32.5, 2, 0 238 | 50, 25.0, 2, 0 239 | 39, 19.5, 2, 0 240 | 76, 76.0, 1, 0 241 | 30, 15.0, 2, 1 242 | 35, 17.5, 2, 1 243 | 18, 4.5, 4, 1 244 | 99, 49.5, 2, 0 245 | 36, 18.0, 2, 1 246 | 11, 2.75, 4, 1 247 | 123, 61.5, 2, 0 248 | 120, 120.0, 1, 0 249 | 96, 19.2, 5, 0 250 | 13, 3.25, 4, 1 251 | 114, 57.0, 2, 0 252 | 18, 4.5, 4, 1 253 | 16, 4.0, 4, 1 254 | 62, 15.5, 4, 0 255 | 22, 11.0, 2, 1 256 | 37, 18.5, 2, 0 257 | 56, 14.0, 4, 0 258 | 40, 10.0, 4, 0 259 | 39, 9.75, 4, 1 260 | 132, 132.0, 1, 0 261 | 56, 28.0, 2, 0 262 | 129, 64.5, 2, 0 263 | 89, 44.5, 2, 0 264 | 60, 15.0, 4, 0 265 | 37, 2.176470588235294, 17, 0 266 | 45, 9.0, 5, 0 267 | 53, 8.833333333333334, 6, 0 268 | 116, 58.0, 2, 0 269 | 62, 31.0, 2, 0 270 | 78, 39.0, 2, 0 271 | 37, 9.25, 4, 0 272 | 41, 8.2, 5, 0 273 | 57, 14.25, 4, 0 274 | 16, 4.0, 4, 1 275 | 51, 25.5, 2, 0 276 | 46, 11.5, 4, 0 277 | 35, 8.75, 4, 1 278 | 152, 152.0, 1, 0 279 | 61, 61.0, 1, 0 280 | 33, 4.714285714285714, 7, 0 281 | 54, 54.0, 1, 0 282 | 155, 155.0, 1, 0 283 | 127, 127.0, 1, 0 284 | 148, 148.0, 1, 0 285 | 135, 135.0, 1, 0 286 | 38, 19.0, 2, 1 287 | 128, 64.0, 2, 0 288 | 93, 46.5, 2, 0 289 | 40, 20.0, 2, 0 290 | 134, 67.0, 2, 0 291 | 72, 36.0, 2, 0 292 | 63, 31.5, 2, 0 293 | 30, 15.0, 2, 1 294 | 89, 44.5, 2, 0 295 | 55, 27.5, 2, 0 296 | 89, 44.5, 2, 0 297 | 44, 7.333333333333333, 6, 0 298 | 33, 11.0, 3, 1 299 | 62, 15.5, 4, 0 300 | 43, 14.333333333333334, 3, 0 301 | 48, 12.0, 4, 0 302 | 47, 15.666666666666666, 3, 0 303 | 73, 36.5, 2, 0 304 | 106, 53.0, 2, 0 305 | 134, 67.0, 2, 0 306 | 49, 24.5, 2, 0 307 | 81, 40.5, 2, 0 308 | 50, 25.0, 2, 0 309 | 76, 38.0, 2, 0 310 | 75, 37.5, 2, 0 311 | 39, 19.5, 2, 1 312 | 51, 25.5, 2, 0 313 | 29, 14.5, 2, 0 314 | 9, 4.5, 2, 0 315 | 22, 11.0, 2, 0 316 | 72, 36.0, 2, 0 317 | 289, 289.0, 1, 0 318 | 304, 304.0, 1, 0 319 | 63, 31.5, 2, 0 320 | 271, 271.0, 1, 0 321 | 289, 289.0, 1, 0 322 | 242, 242.0, 1, 0 323 | 305, 305.0, 1, 0 324 | 271, 271.0, 1, 0 325 | 267, 267.0, 1, 0 326 | 275, 275.0, 1, 0 327 | 243, 243.0, 1, 0 328 | 102, 51.0, 2, 0 329 | 205, 205.0, 1, 0 330 | 170, 170.0, 1, 0 331 | 175, 175.0, 1, 0 332 | 206, 206.0, 1, 0 333 | 172, 86.0, 2, 0 334 | 124, 62.0, 2, 0 335 | 175, 87.5, 2, 0 336 | 171, 171.0, 1, 0 337 | 24, 4.0, 6, 0 338 | 30, 3.0, 10, 0 339 | 60, 30.0, 2, 0 340 | 33, 3.6666666666666665, 9, 0 341 | 56, 28.0, 2, 1 342 | 68, 34.0, 2, 0 343 | 12, 3.0, 4, 1 344 | 72, 36.0, 2, 0 345 | 62, 31.0, 2, 0 346 | 66, 33.0, 2, 0 347 | 63, 31.5, 2, 0 348 | 69, 34.5, 2, 0 349 | 76, 38.0, 2, 0 350 | 76, 38.0, 2, 0 351 | 66, 33.0, 2, 0 352 | 77, 38.5, 2, 0 353 | 67, 33.5, 2, 0 354 | 37, 18.5, 2, 1 355 | 134, 67.0, 2, 0 356 | 75, 37.5, 2, 0 357 | 64, 32.0, 2, 0 358 | 61, 30.5, 2, 0 359 | 38, 19.0, 2, 1 360 | 103, 51.5, 2, 0 361 | 81, 40.5, 2, 0 362 | 41, 20.5, 2, 1 363 | 40, 20.0, 2, 1 364 | 71, 35.5, 2, 0 365 | 30, 7.5, 4, 1 366 | 29, 14.5, 2, 1 367 | 138, 69.0, 2, 0 368 | 35, 11.666666666666666, 3, 1 369 | 88, 88.0, 1, 0 370 | 71, 71.0, 1, 0 371 | 42, 14.0, 3, 0 372 | 74, 37.0, 2, 0 373 | 71, 35.5, 2, 0 374 | 12, 3.0, 4, 1 375 | 47, 47.0, 1, 0 376 | 68, 34.0, 2, 0 377 | 131, 65.5, 2, 0 378 | 53, 26.5, 2, 0 379 | 59, 14.75, 4, 0 380 | 64, 32.0, 2, 0 381 | 46, 23.0, 2, 0 382 | 142, 71.0, 2, 0 383 | 44, 44, 1, 1 384 | 50, 12.5, 4, 1 385 | -------------------------------------------------------------------------------- /Deep-Drop/training/training_nn.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 2, 4 | "metadata": { 5 | "language_info": { 6 | "name": "python", 7 | "codemirror_mode": { 8 | "name": "ipython", 9 | "version": 3 10 | }, 11 | "version": "3.6.6" 12 | }, 13 | "orig_nbformat": 2, 14 | "file_extension": ".py", 15 | "mimetype": "text/x-python", 16 | "name": "python", 17 | "npconvert_exporter": "python", 18 | "pygments_lexer": "ipython3", 19 | "version": 3 20 | }, 21 | "cells": [ 22 | { 23 | "cell_type": "code", 24 | "execution_count": 28, 25 | "metadata": {}, 26 | "outputs": [], 27 | "source": [ 28 | "import os\n", 29 | "import numpy\n", 30 | "\n", 31 | "import tensorflow\n", 32 | "from tensorflow.keras.models import Sequential\n", 33 | "from tensorflow.keras.layers import Dense\n", 34 | "from tensorflow.keras.models import load_model\n", 35 | "\n", 36 | "from sklearn.externals.joblib import dump\n", 37 | "from sklearn.externals.joblib import load\n", 38 | "from sklearn.preprocessing import MinMaxScaler\n", 39 | "\n", 40 | "class NeuralNetwork(object):\n", 41 | " def __init__(self, data_file=None, epochs=500, batch=32, activation='sigmoid', loss='binary_crossentropy', optimizer='adam', split=0.20):\n", 42 | " self.data_file = data_file\n", 43 | " self.features = None\n", 44 | " self.labels = None\n", 45 | " self.epochs = epochs\n", 46 | " self.batch = batch\n", 47 | " self.scaler = MinMaxScaler(feature_range=(0, 1))\n", 48 | "\n", 49 | " self.clf = Sequential(name='Sandbox') \n", 50 | " self.clf.add(Dense(3, input_dim=3, name='Input'))\n", 51 | " self.clf.add(Dense(3, activation=activation, name='Hidden_layer_1'))\n", 52 | " self.clf.add(Dense(3, activation=activation, name='Hidden_layer_2'))\n", 53 | " self.clf.add(Dense(1, activation=activation, name='Output'))\n", 54 | " self.clf.compile(loss=loss, optimizer=optimizer, metrics=['acc', 'mae', 'mse'])\n", 55 | " self.split = split\n", 56 | "\n", 57 | " def load_data(self):\n", 58 | " dataset = numpy.loadtxt(self.data_file, delimiter=\",\", dtype='float')\n", 59 | " self.features = dataset[:,0:3]\n", 60 | " self.labels = dataset[:,3]\n", 61 | "\n", 62 | " def scale_data(self, save_scaler=False):\n", 63 | " self.features = self.features.astype(numpy.float)\n", 64 | " self.labels = self.labels.astype(numpy.float)\n", 65 | " self.labels.reshape(-1, 1)\n", 66 | "\n", 67 | " self.scaler.fit(self.features)\n", 68 | " self.min_max_data = self.scaler.transform(self.features)\n", 69 | "\n", 70 | " if save_scaler:\n", 71 | " dump(self.scaler, 'minmax_scaler.bin', compress=True)\n", 72 | "\n", 73 | " def train(self, metrics=True):\n", 74 | " self.clf.fit(self.min_max_data, self.labels, epochs=self.epochs, batch_size=self.batch, validation_split=self.split, verbose=False)\n", 75 | "\n", 76 | " if metrics:\n", 77 | " scores = self.clf.evaluate(self.min_max_data, self.labels)\n", 78 | " print(\"\\n%s: %.2f%%\" % (self.clf.metrics_names[1], scores[1]*100))\n", 79 | "\n", 80 | " def predict(self, features, verbose=True):\n", 81 | " # test = numpy.array([[131, 65.5, 2]])\n", 82 | " # test1 = numpy.array([[36, 18.0, 2]])\n", 83 | "\n", 84 | " features = features.astype(numpy.float64) \n", 85 | " min_max_data = self.scaler.transform(features)\n", 86 | "\n", 87 | " if verbose:\n", 88 | " print(f'Predicted Class: {self.clf.predict_classes(min_max_data)[0][0]}')\n", 89 | " print(f'Class Probability: {self.clf.predict(min_max_data)[0][0]}')\n", 90 | "\n", 91 | " def save(self, outfile):\n", 92 | " self.model.save(outfile, save_format='tf')\n", 93 | "\n", 94 | " def load(self, model_path, scaler_file):\n", 95 | " self.model = load_model(model_path)\n", 96 | " self.scaler_file = load(scaler_file)\n", 97 | "\n", 98 | "\n" 99 | ] 100 | }, 101 | { 102 | "cell_type": "code", 103 | "execution_count": 29, 104 | "metadata": {}, 105 | "outputs": [], 106 | "source": [ 107 | "nn = NeuralNetwork(data_file=\".\\\\data\\\\data_process_count.csv\")" 108 | ] 109 | }, 110 | { 111 | "cell_type": "code", 112 | "execution_count": 30, 113 | "metadata": {}, 114 | "outputs": [], 115 | "source": [ 116 | "nn.load_data()" 117 | ] 118 | }, 119 | { 120 | "cell_type": "code", 121 | "execution_count": 31, 122 | "metadata": {}, 123 | "outputs": [], 124 | "source": [ 125 | "nn.scale_data()" 126 | ] 127 | }, 128 | { 129 | "cell_type": "code", 130 | "execution_count": 32, 131 | "metadata": {}, 132 | "outputs": [ 133 | { 134 | "name": "stdout", 135 | "output_type": "stream", 136 | "text": "384/1 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- 0s 290us/sample - loss: 0.2869 - acc: 0.9271 - mae: 0.1188 - mse: 0.0573\n\nacc: 92.71%\n" 137 | } 138 | ], 139 | "source": [ 140 | "nn.train()" 141 | ] 142 | }, 143 | { 144 | "cell_type": "code", 145 | "execution_count": 33, 146 | "metadata": {}, 147 | "outputs": [ 148 | { 149 | "name": "stdout", 150 | "output_type": "stream", 151 | "text": "Predicted Class: 0\nClass Probability: 0.01737615279853344\n" 152 | } 153 | ], 154 | "source": [ 155 | "safe_test = numpy.array([[131, 65.5, 2]])\n", 156 | "nn.predict(safe_test)" 157 | ] 158 | }, 159 | { 160 | "cell_type": "code", 161 | "execution_count": 34, 162 | "metadata": {}, 163 | "outputs": [ 164 | { 165 | "name": "stdout", 166 | "output_type": "stream", 167 | "text": "Predicted Class: 1\nClass Probability: 0.6497369408607483\n" 168 | } 169 | ], 170 | "source": [ 171 | "unsafe_test = numpy.array([[36, 18.0, 2]])\n", 172 | "nn.predict(unsafe_test)" 173 | ] 174 | }, 175 | { 176 | "cell_type": "code", 177 | "execution_count": null, 178 | "metadata": {}, 179 | "outputs": [], 180 | "source": [] 181 | } 182 | ] 183 | } -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Deep-Drop 2 | The dropper implementation was proof-of-concept, and a lot of the inital code came from training code during research. This branch aims to simplify the project and expose model predictions through a REST API. (The old branch could get a makeover at some point using this API). 3 | 4 | ## Process List Data 5 | The process list parsing was built around a specific inital access payload, as a result the parsing wasn't flexible. The API no longer makes an assumption about the process list structure and only cares about the features - process_count, user_count, and process_user_ratio. 6 | 7 | **Example cURL request** 8 | 9 | ```curl 10 | curl http://localhost/api/v1/predict?process_count=345&user_count=2&process_user_ratio=172.5 11 | {'decision_tree': 0.0} 12 | ``` 13 | 14 | ## Predictions 15 | Returning scores for both models was somewhat arbitrary. During training and evaluation you would deploy the model that is the most accurate, and re-evaluate on a periodic basis as more data was collected. 16 | 17 | ## Training 18 | Training code has not changed. 19 | 20 | ## TODO -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | termcolor==1.1.0 2 | Keras==2.2.4 3 | numpy==1.16.3 4 | Flask==1.0.2 5 | scikit_learn==0.21.3 6 | --------------------------------------------------------------------------------