AntiNex-Protecting-Django.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## AntiNex Django Analysis\n",
    "\n",
    "Example for showing how to build deep neural networks to defend software applications like the celery workers do automatically.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Components\n",
    "\n",
    "1. Using the AntiNex Datasets:\n",
    "   https://github.com/jay-johnson/antinex-datasets\n",
    "1. Recording repository\n",
    "   https://github.com/jay-johnson/network-pipeline\n",
    "1. Django REST Framework + Celery + JWT + Swagger API:\n",
    "   https://github.com/jay-johnson/train-ai-with-django-swagger-jwt\n",
    "1. Utilities\n",
    "   https://github.com/jay-johnson/antinex-utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "livereveal": {
     "theme": "serif",
     "transition": "zoom"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "import logging\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Name this Analysis and set up the logger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-03-20 06:31:02,215 standalone-scaler-django - INFO starting\n"
     ]
    }
   ],
   "source": [
    "name = \"standalone-scaler-django\"\n",
    "logging.basicConfig(\n",
    "    level=logging.DEBUG,\n",
    "    format=\"%(asctime)-15s %(name)s - %(levelname)s %(message)s\")\n",
    "log = logging.getLogger(name)\n",
    "log.info(\"starting\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Target Dataset and Model Artifacts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "dataset = \"/opt/antinex/antinex-datasets/v1/webapps/django/training-ready/v1_django_cleaned.csv\"\n",
    "output_dir = os.getenv(\n",
    "    \"SHARED_DIR\",\n",
    "    \"/tmp\")\n",
    "model_backup_file = \"{}/{}-full-model.h5\".format(output_dir, name)\n",
    "model_weights_file = \"{}/{}-weights.h5\".format(output_dir, name)\n",
    "model_json_file = \"{}/{}-model.json\".format(output_dir, name)\n",
    "model_image_file = \"{}/{}-predictions-vs-correct.png\".format(output_dir, name)\n",
    "image_title = \"{} - Predictions and Correct Predictions\".format(output_dir, name)\n",
    "footnote_text = \"AntiNex v1\"\n",
    "show_predictions = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Set the Random Seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "seed = 42\n",
    "np.random.seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "def build_model(\n",
    "        num_features,\n",
    "        loss,\n",
    "        optimizer,\n",
    "        metrics):\n",
    "    \"\"\"build_model\n",
    "\n",
    "    Build the Keras Deep Neural Network Model\n",
    "\n",
    "    :param num_features: number of features\n",
    "    :param loss: loss function to apply\n",
    "    :param optimizer: optimizer to use\n",
    "    :param metrics: list of metrics\n",
    "    \"\"\"\n",
    "    log.info(\"building model\")\n",
    "    model = Sequential()\n",
    "    model.add(\n",
    "        Dense(\n",
    "            250,\n",
    "            input_dim=num_features,\n",
    "            kernel_initializer=\"uniform\",\n",
    "            activation=\"relu\"))\n",
    "    model.add(\n",
    "        Dense(\n",
    "            1,\n",
    "            kernel_initializer=\"uniform\",\n",
    "            activation=\"sigmoid\"))\n",
    "\n",
    "    log.info((\"compiling loss={} optimizer={} metrics={}\")\n",
    "             .format(\n",
    "                loss,\n",
    "                optimizer,\n",
    "                metrics))\n",
    "\n",
    "    model.compile(\n",
    "        loss=loss,\n",
    "        optimizer=optimizer,\n",
    "        metrics=metrics)\n",
    "\n",
    "    return model\n",
    "# end of build_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Define Training Features to Process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-03-20 06:31:02,314 standalone-scaler-django - INFO downloading url=https://github.com/jay-johnson/antinex-datasets/raw/master/v1/webapps/django/training-ready/v1_django_cleaned.csv\n"
     ]
    }
   ],
   "source": [
    "features_to_process = [\n",
    "    \"idx\",\n",
    "    \"arp_hwlen\",\n",
    "    \"arp_hwtype\",\n",
    "    \"arp_id\",\n",
    "    \"arp_op\",\n",
    "    \"arp_plen\",\n",
    "    \"arp_ptype\",\n",
    "    \"dns_default_aa\",\n",
    "    \"dns_default_ad\",\n",
    "    \"dns_default_an\",\n",
    "    \"dns_default_ancount\",\n",
    "    \"dns_default_ar\",\n",
    "    \"dns_default_arcount\",\n",
    "    \"dns_default_cd\",\n",
    "    \"dns_default_id\",\n",
    "    \"dns_default_length\",\n",
    "    \"dns_default_ns\",\n",
    "    \"dns_default_nscount\",\n",
    "    \"dns_default_opcode\",\n",
    "    \"dns_default_qd\",\n",
    "    \"dns_default_qdcount\",\n",
    "    \"dns_default_qr\",\n",
    "    \"dns_default_ra\",\n",
    "    \"dns_default_rcode\",\n",
    "    \"dns_default_rd\",\n",
    "    \"dns_default_tc\",\n",
    "    \"dns_default_z\",\n",
    "    \"dns_id\",\n",
    "    \"eth_id\",\n",
    "    \"eth_type\",\n",
    "    \"icmp_addr_mask\",\n",
    "    \"icmp_code\",\n",
    "    \"icmp_gw\",\n",
    "    \"icmp_id\",\n",
    "    \"icmp_ptr\",\n",
    "    \"icmp_seq\",\n",
    "    \"icmp_ts_ori\",\n",
    "    \"icmp_ts_rx\",\n",
    "    \"icmp_ts_tx\",\n",
    "    \"icmp_type\",\n",
    "    \"icmp_unused\",\n",
    "    \"ip_id\",\n",
    "    \"ip_ihl\",\n",
    "    \"ip_len\",\n",
    "    \"ip_tos\",\n",
    "    \"ip_version\",\n",
    "    \"ipv6_fl\",\n",
    "    \"ipv6_hlim\",\n",
    "    \"ipv6_nh\",\n",
    "    \"ipv6_plen\",\n",
    "    \"ipv6_tc\",\n",
    "    \"ipv6_version\",\n",
    "    \"ipvsix_id\",\n",
    "    \"pad_id\",\n",
    "    \"tcp_dport\",\n",
    "    \"tcp_fields_options.MSS\",\n",
    "    \"tcp_fields_options.NOP\",\n",
    "    \"tcp_fields_options.SAckOK\",\n",
    "    \"tcp_fields_options.Timestamp\",\n",
    "    \"tcp_fields_options.WScale\",\n",
    "    \"tcp_id\",\n",
    "    \"tcp_seq\",\n",
    "    \"tcp_sport\",\n",
    "    \"udp_dport\",\n",
    "    \"udp_id\",\n",
    "    \"udp_len\",\n",
    "    \"udp_sport\"\n",
    "]\n",
    "\n",
    "predict_feature = \"label_value\"\n",
    "predict_rows_df = None\n",
    "\n",
    "if not os.path.exists(dataset):\n",
    "    dataset_url = \"https://github.com/jay-johnson/antinex-datasets/raw/master/v1/webapps/django/training-ready/v1_django_cleaned.csv\"\n",
    "    log.info((\"downloading url={}\")\n",
    "             .format(\n",
    "                 dataset_url))\n",
    "    predict_rows_df = pd.read_csv(dataset_url)\n",
    "else:\n",
    "    log.info((\"loading dataset={}\")\n",
    "         .format(\n",
    "            dataset))\n",
    "    predict_rows_df = pd.read_csv(dataset)\n",
    "# if available on disk use the file, otherwise download it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Setup Training Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-03-20 06:31:03,610 standalone-scaler-django - INFO converting to json\n"
     ]
    }
   ],
   "source": [
    "log.info(\"converting to json\")\n",
    "predict_rows = predict_rows_df.to_json()\n",
    "found_columns = list(predict_rows_df.columns.values)\n",
    "num_features = len(found_columns) - 1\n",
    "min_scaler_range = -1\n",
    "max_scaler_range = 1\n",
    "max_records = 100000\n",
    "verbose = 1\n",
    "test_size = 0.2\n",
    "batch_size = 32\n",
    "epochs = 15\n",
    "num_splits = 2\n",
    "loss = \"binary_crossentropy\"\n",
    "optimizer = \"adam\"\n",
    "metrics = [\n",
    "    \"accuracy\"\n",
    "]\n",
    "histories = [\n",
    "    \"val_loss\",\n",
    "    \"val_acc\",\n",
    "    \"loss\",\n",
    "    \"acc\"\n",
    "]\n",
    "label_rules = {\n",
    "    \"labels\": [\n",
    "        \"not_attack\",\n",
    "        \"not_attack\",\n",
    "        \"attack\"\n",
    "    ],\n",
    "    \"label_values\": [\n",
    "        -1,\n",
    "        0,\n",
    "        1\n",
    "    ]\n",
    "}\n",
    "\n",
    "should_set_labels = False\n",
    "labels_dict = {}\n",
    "if \"labels\" in label_rules and \"label_values\" in label_rules:\n",
    "    label_rows = label_rules[\"label_values\"]\n",
    "    for idx, lidx in enumerate(label_rows):\n",
    "        if len(label_rules[\"labels\"]) >= idx:\n",
    "            should_set_labels = True\n",
    "            labels_dict[str(lidx)] = label_rules[\"labels\"][idx]\n",
    "# end of compiling labels dictionary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Define a Keras Scikit-Learn Handler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# define a function with no arguments for Keras Scikit-Learn API to work\n",
    "# https://keras.io/scikit-learn-api/\n",
    "def set_model():\n",
    "    \"\"\"set_model\"\"\"\n",
    "    return build_model(\n",
    "        num_features=num_features,\n",
    "        loss=loss,\n",
    "        optimizer=optimizer,\n",
    "        metrics=metrics)\n",
    "# end of set_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Setup Training and Test Scalers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "cast_to_type = \"float32\"\n",
    "train_df = predict_rows_df[features_to_process]\n",
    "test_df = pd.DataFrame(\n",
    "    {predict_feature: predict_rows_df[predict_feature]})\n",
    "\n",
    "train_scaler = MinMaxScaler(\n",
    "    feature_range=(\n",
    "        min_scaler_range,\n",
    "        max_scaler_range))\n",
    "train_only_floats = train_df.values.astype(cast_to_type)\n",
    "scaled_train_dataset = train_scaler.fit_transform(\n",
    "    train_only_floats)\n",
    "\n",
    "test_scaler = MinMaxScaler(\n",
    "    feature_range=(\n",
    "        min_scaler_range,\n",
    "        max_scaler_range))\n",
    "test_only_floats = test_df.values.astype(cast_to_type)\n",
    "scaled_test_dataset = test_scaler.fit_transform(\n",
    "    test_only_floats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Build Train and Test records from Scaled Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-03-20 06:31:03,978 standalone-scaler-django - INFO splitting rows=30200 test_size=0.2\n"
     ]
    }
   ],
   "source": [
    "datanode = {\n",
    "    \"X_train\": None,\n",
    "    \"Y_train\": None,\n",
    "    \"X_test\": None,\n",
    "    \"Y_test\": None\n",
    "}\n",
    "\n",
    "log.info((\"splitting rows={} test_size={}\")\n",
    "         .format(\n",
    "            len(train_only_floats),\n",
    "            test_size))\n",
    "\n",
    "(datanode[\"X_train\"],\n",
    " datanode[\"X_test\"],\n",
    " datanode[\"Y_train\"],\n",
    " datanode[\"Y_test\"]) = train_test_split(\n",
    "            scaled_train_dataset,\n",
    "            scaled_test_dataset,\n",
    "            test_size=test_size,\n",
    "            random_state=seed)\n",
    "\n",
    "predict_rows_df[predict_feature] = scaled_test_dataset\n",
    "train_scaler_df = pd.DataFrame(\n",
    "    scaled_train_dataset,\n",
    "    columns=features_to_process)\n",
    "test_scaler_df = pd.DataFrame(\n",
    "    scaled_test_dataset,\n",
    "    columns=[predict_feature])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Create Train and Test Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-03-20 06:31:04,011 standalone-scaler-django - INFO scaled datasets ready x_train=24160 y_train=24160 x_test=6040 y_test=6040\n"
     ]
    }
   ],
   "source": [
    "sample_rows = train_scaler_df[features_to_process]\n",
    "target_rows = test_scaler_df\n",
    "num_samples = len(sample_rows.index)\n",
    "num_target_rows = len(target_rows.index)\n",
    "\n",
    "log.info((\"scaled datasets ready \"\n",
    "          \"x_train={} y_train={} \"\n",
    "          \"x_test={} y_test={}\")\n",
    "         .format(\n",
    "            len(datanode[\"X_train\"]),\n",
    "            len(datanode[\"Y_train\"]),\n",
    "            len(datanode[\"X_test\"]),\n",
    "            len(datanode[\"Y_test\"])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Build Keras Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-03-20 06:31:04,026 standalone-scaler-django - INFO building KerasClassifier\n"
     ]
    }
   ],
   "source": [
    "log.info(\"building KerasClassifier\")\n",
    "\n",
    "model = KerasClassifier(\n",
    "    build_fn=set_model,\n",
    "    epochs=epochs,\n",
    "    batch_size=batch_size,\n",
    "    verbose=verbose)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Fit Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-03-20 06:31:04,047 standalone-scaler-django - INFO fitting model epochs=15 batch=32 rows=24160 features=68\n",
      "2018-03-20 06:31:04,049 standalone-scaler-django - INFO building model\n",
      "2018-03-20 06:31:04,082 standalone-scaler-django - INFO compiling loss=binary_crossentropy optimizer=adam metrics=['accuracy']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 24160 samples, validate on 6040 samples\n",
      "Epoch 1/15\n",
      "24160/24160 [==============================] - 2s 65us/step - loss: 0.5867 - acc: 0.7017 - val_loss: -0.3003 - val_acc: 0.0000e+00\n",
      "Epoch 2/15\n",
      "24160/24160 [==============================] - 1s 59us/step - loss: 0.5585 - acc: 0.7009 - val_loss: -0.5013 - val_acc: 0.0000e+00\n",
      "Epoch 3/15\n",
      "24160/24160 [==============================] - 1s 52us/step - loss: 0.5001 - acc: 0.7020 - val_loss: -0.7091 - val_acc: 0.1601\n",
      "Epoch 4/15\n",
      "24160/24160 [==============================] - 1s 57us/step - loss: 0.3700 - acc: 0.8576 - val_loss: -1.4144 - val_acc: 0.2947\n",
      "Epoch 5/15\n",
      "24160/24160 [==============================] - 1s 58us/step - loss: 0.2391 - acc: 0.9529 - val_loss: -2.2905 - val_acc: 0.2947\n",
      "Epoch 6/15\n",
      "24160/24160 [==============================] - 1s 57us/step - loss: 0.1568 - acc: 0.9596 - val_loss: -2.8234 - val_acc: 0.2947\n",
      "Epoch 7/15\n",
      "24160/24160 [==============================] - 1s 58us/step - loss: 0.0906 - acc: 0.9940 - val_loss: -3.4526 - val_acc: 0.2947\n",
      "Epoch 8/15\n",
      "24160/24160 [==============================] - 1s 55us/step - loss: 0.0538 - acc: 0.9978 - val_loss: -3.9279 - val_acc: 0.2947\n",
      "Epoch 9/15\n",
      "24160/24160 [==============================] - 1s 57us/step - loss: 0.0367 - acc: 0.9978 - val_loss: -4.4362 - val_acc: 0.2947\n",
      "Epoch 10/15\n",
      "24160/24160 [==============================] - 1s 53us/step - loss: 0.0278 - acc: 0.9979 - val_loss: -4.7415 - val_acc: 0.2947\n",
      "Epoch 11/15\n",
      "24160/24160 [==============================] - 1s 52us/step - loss: 0.0220 - acc: 0.9981 - val_loss: -5.2251 - val_acc: 0.2949\n",
      "Epoch 12/15\n",
      "24160/24160 [==============================] - 1s 53us/step - loss: 0.0180 - acc: 0.9982 - val_loss: -5.4598 - val_acc: 0.2950\n",
      "Epoch 13/15\n",
      "24160/24160 [==============================] - 1s 54us/step - loss: 0.0170 - acc: 0.9982 - val_loss: -5.0771 - val_acc: 0.2950\n",
      "Epoch 14/15\n",
      "24160/24160 [==============================] - 1s 50us/step - loss: 0.0165 - acc: 0.9981 - val_loss: -5.4020 - val_acc: 0.2952\n",
      "Epoch 15/15\n",
      "24160/24160 [==============================] - 1s 55us/step - loss: 0.0159 - acc: 0.9978 - val_loss: -5.6785 - val_acc: 0.2952\n"
     ]
    }
   ],
   "source": [
    "log.info((\"fitting model epochs={} batch={} \"\n",
    "          \"rows={} features={}\")\n",
    "         .format(\n",
    "            epochs,\n",
    "            batch_size,\n",
    "            len(datanode[\"X_train\"]),\n",
    "            len(found_columns)))\n",
    "\n",
    "history = model.fit(\n",
    "    datanode[\"X_train\"],\n",
    "    datanode[\"Y_train\"],\n",
    "    validation_data=(\n",
    "        datanode[\"X_test\"],\n",
    "        datanode[\"Y_test\"]),\n",
    "    epochs=epochs,\n",
    "    batch_size=batch_size,\n",
    "    shuffle=False,\n",
    "    verbose=verbose)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Build Pipeline and Cross Validate using Stratified K-Folds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-03-20 06:31:24,604 standalone-scaler-django - INFO building estimators\n",
      "2018-03-20 06:31:24,606 standalone-scaler-django - INFO building pipeline\n",
      "2018-03-20 06:31:24,609 standalone-scaler-django - INFO starting classification StratifiedKFold splits=2 seed=42\n",
      "2018-03-20 06:31:24,610 standalone-scaler-django - INFO classification cross_val_score\n",
      "2018-03-20 06:31:24,628 standalone-scaler-django - INFO building model\n",
      "2018-03-20 06:31:24,658 standalone-scaler-django - INFO compiling loss=binary_crossentropy optimizer=adam metrics=['accuracy']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "12080/12080 [==============================] - 1s 62us/step - loss: 0.5750 - acc: 0.7003\n",
      "Epoch 2/15\n",
      "12080/12080 [==============================] - 1s 50us/step - loss: 0.5002 - acc: 0.7323\n",
      "Epoch 3/15\n",
      "12080/12080 [==============================] - 1s 50us/step - loss: 0.3661 - acc: 0.8589\n",
      "Epoch 4/15\n",
      "12080/12080 [==============================] - 1s 47us/step - loss: 0.2261 - acc: 0.9615\n",
      "Epoch 5/15\n",
      "12080/12080 [==============================] - 1s 50us/step - loss: 0.1334 - acc: 0.9947\n",
      "Epoch 6/15\n",
      "12080/12080 [==============================] - 1s 49us/step - loss: 0.0800 - acc: 0.9973\n",
      "Epoch 7/15\n",
      "12080/12080 [==============================] - 1s 47us/step - loss: 0.0515 - acc: 0.9976\n",
      "Epoch 8/15\n",
      "12080/12080 [==============================] - 1s 49us/step - loss: 0.0358 - acc: 0.9977\n",
      "Epoch 9/15\n",
      "12080/12080 [==============================] - 1s 47us/step - loss: 0.0265 - acc: 0.9980\n",
      "Epoch 10/15\n",
      "12080/12080 [==============================] - 1s 47us/step - loss: 0.0205 - acc: 0.9982\n",
      "Epoch 11/15\n",
      "12080/12080 [==============================] - 1s 47us/step - loss: 0.0167 - acc: 0.9984\n",
      "Epoch 12/15\n",
      "12080/12080 [==============================] - 1s 50us/step - loss: 0.0139 - acc: 0.9983\n",
      "Epoch 13/15\n",
      "12080/12080 [==============================] - 1s 52us/step - loss: 0.0121 - acc: 0.9984\n",
      "Epoch 14/15\n",
      "12080/12080 [==============================] - 1s 46us/step - loss: 0.0105 - acc: 0.9984\n",
      "Epoch 15/15\n",
      "12080/12080 [==============================] - 1s 48us/step - loss: 0.0092 - acc: 0.9986\n",
      "12080/12080 [==============================] - 0s 26us/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-03-20 06:31:34,307 standalone-scaler-django - INFO building model\n",
      "2018-03-20 06:31:34,331 standalone-scaler-django - INFO compiling loss=binary_crossentropy optimizer=adam metrics=['accuracy']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "12080/12080 [==============================] - 1s 61us/step - loss: 0.5691 - acc: 0.7028\n",
      "Epoch 2/15\n",
      "12080/12080 [==============================] - 1s 52us/step - loss: 0.4952 - acc: 0.7370\n",
      "Epoch 3/15\n",
      "12080/12080 [==============================] - 1s 48us/step - loss: 0.3566 - acc: 0.8714\n",
      "Epoch 4/15\n",
      "12080/12080 [==============================] - 1s 50us/step - loss: 0.2059 - acc: 0.9727\n",
      "Epoch 5/15\n",
      "12080/12080 [==============================] - 1s 49us/step - loss: 0.1091 - acc: 0.9967\n",
      "Epoch 6/15\n",
      "12080/12080 [==============================] - 1s 51us/step - loss: 0.0619 - acc: 0.9978\n",
      "Epoch 7/15\n",
      "12080/12080 [==============================] - 1s 56us/step - loss: 0.0390 - acc: 0.9982\n",
      "Epoch 8/15\n",
      "12080/12080 [==============================] - 1s 52us/step - loss: 0.0275 - acc: 0.9979\n",
      "Epoch 9/15\n",
      "12080/12080 [==============================] - 1s 49us/step - loss: 0.0206 - acc: 0.9982\n",
      "Epoch 10/15\n",
      "12080/12080 [==============================] - 1s 49us/step - loss: 0.0165 - acc: 0.9983\n",
      "Epoch 11/15\n",
      "12080/12080 [==============================] - 1s 48us/step - loss: 0.0136 - acc: 0.9983\n",
      "Epoch 12/15\n",
      "12080/12080 [==============================] - 1s 50us/step - loss: 0.0116 - acc: 0.9984\n",
      "Epoch 13/15\n",
      "12080/12080 [==============================] - 1s 54us/step - loss: 0.0105 - acc: 0.9987\n",
      "Epoch 14/15\n",
      "12080/12080 [==============================] - 1s 46us/step - loss: 0.0097 - acc: 0.9985\n",
      "Epoch 15/15\n",
      "12080/12080 [==============================] - 1s 49us/step - loss: 0.0084 - acc: 0.9987\n",
      "12080/12080 [==============================] - 0s 28us/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-03-20 06:31:44,199 standalone-scaler-django - INFO classification accuracy=99.82201986754967 samples=30200\n"
     ]
    }
   ],
   "source": [
    "log.info(\"building estimators\")\n",
    "\n",
    "estimators = []\n",
    "estimators.append(\n",
    "    (\"standardize\",\n",
    "        StandardScaler()))\n",
    "estimators.append(\n",
    "    (\"mlp\",\n",
    "        model))\n",
    "\n",
    "log.info(\"building pipeline\")\n",
    "pipeline = Pipeline(estimators)\n",
    "\n",
    "# noqa https://machinelearningmastery.com/multi-class-classification-tutorial-keras-deep-learning-library/\n",
    "log.info((\"starting classification \"\n",
    "          \"StratifiedKFold \"\n",
    "          \"splits={} seed={}\")\n",
    "         .format(\n",
    "            num_splits,\n",
    "            seed))\n",
    "\n",
    "kfold = StratifiedKFold(\n",
    "    n_splits=num_splits,\n",
    "    random_state=seed)\n",
    "\n",
    "log.info((\"classification cross_val_score\"))\n",
    "results = cross_val_score(\n",
    "    pipeline,\n",
    "    datanode[\"X_train\"],\n",
    "    datanode[\"Y_train\"],\n",
    "    cv=kfold)\n",
    "scores = [\n",
    "    results.std(),\n",
    "    results.mean()\n",
    "]\n",
    "accuracy = {\n",
    "    \"accuracy\": results.mean() * 100\n",
    "}\n",
    "\n",
    "log.info((\"classification accuracy={} samples={}\")\n",
    "         .format(\n",
    "            accuracy[\"accuracy\"],\n",
    "            num_samples))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Make Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30200/30200 [==============================] - 1s 21us/step\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(\n",
    "    sample_rows.values,\n",
    "    verbose=verbose)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Create Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-03-20 06:31:44,852 standalone-scaler-django - INFO classification confusion_matrix samples=30200 predictions=30200 target_rows=30200\n",
      "2018-03-20 06:31:44,880 standalone-scaler-django - INFO classification has confusion_matrix=[[21195     5]\n",
      " [   49  8951]] predictions=30200 target_rows=30200\n"
     ]
    }
   ],
   "source": [
    "log.info((\"classification confusion_matrix samples={} \"\n",
    "          \"predictions={} target_rows={}\")\n",
    "         .format(\n",
    "            num_samples,\n",
    "            len(predictions),\n",
    "            num_target_rows))\n",
    "\n",
    "cm = confusion_matrix(\n",
    "    target_rows.values,\n",
    "    predictions)\n",
    "\n",
    "log.info((\"classification has confusion_matrix={} \"\n",
    "          \"predictions={} target_rows={}\")\n",
    "         .format(\n",
    "            cm,\n",
    "            len(predictions),\n",
    "            num_target_rows))\n",
    "# end of confusion matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Round Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-03-20 06:31:44,952 standalone-scaler-django - INFO scores=[0.00012417218543048225, 0.9982201986754967] accuracy=99.82201986754967 merging samples=30200 with predictions=30200 labels={'-1': 'not_attack', '0': 'not_attack', '1': 'attack'}\n"
     ]
    }
   ],
   "source": [
    "rounded = [round(x[0]) for x in predictions]\n",
    "sample_predictions = []\n",
    "\n",
    "log.info((\"scores={} accuracy={} \"\n",
    "          \"merging samples={} with predictions={} \"\n",
    "          \"labels={}\")\n",
    "         .format(\n",
    "            scores,\n",
    "            accuracy.get(\"accuracy\", None),\n",
    "            len(sample_rows.index),\n",
    "            len(rounded),\n",
    "            labels_dict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Merge Correct Answers with Predicted Answers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "ridx = 0\n",
    "for idx, row in predict_rows_df.iterrows():\n",
    "    if len(sample_predictions) > max_records:\n",
    "        log.info((\"hit max={} predictions\")\n",
    "                 .format(\n",
    "                    max_records))\n",
    "        break\n",
    "    new_row = json.loads(row.to_json())\n",
    "    cur_value = rounded[ridx]\n",
    "    if predict_feature in row:\n",
    "        new_row[\"_original_{}\".format(\n",
    "                predict_feature)] = \\\n",
    "            int(row[predict_feature])\n",
    "    else:\n",
    "        new_row[\"_original_{}\".format(\n",
    "                predict_feature)] = \\\n",
    "            \"missing-from-dataset\"\n",
    "    if cur_value != int(row[predict_feature]):\n",
    "        new_row[\"prediction_status\"] = 1\n",
    "    else:\n",
    "        new_row[\"prediction_status\"] = 0\n",
    "\n",
    "    new_row[predict_feature] = int(cur_value)\n",
    "\n",
    "    if should_set_labels:\n",
    "        new_row[\"label_name\"] = \\\n",
    "            labels_dict[str(int(cur_value))]\n",
    "    new_row[\"_row_idx\"] = ridx\n",
    "    new_row[\"_count\"] = idx\n",
    "    sample_predictions.append(new_row)\n",
    "    ridx += 1\n",
    "# end of merging samples with predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### (Optional) Print Out Every Prediction and Original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-03-20 06:31:50,081 standalone-scaler-django - INFO creating merged_predictions_df from sample_predictions=30200\n"
     ]
    }
   ],
   "source": [
    "log.info((\"creating merged_predictions_df from sample_predictions={}\")\n",
    "         .format(\n",
    "            len(sample_predictions)))\n",
    "\n",
    "merged_predictions_df = pd.DataFrame(\n",
    "    sample_predictions)\n",
    "\n",
    "if show_predictions:\n",
    "    for row_num, row in merged_predictions_df.iterrows():\n",
    "        log.info((\"row={} original_{}={} predicted={}\")\n",
    "                 .format(\n",
    "                    row_num,\n",
    "                    predict_feature,\n",
    "                    row[\"_original_{}\".format(\n",
    "                        predict_feature)],\n",
    "                    row[predict_feature]))\n",
    "    # end seeing original vs predicted\n",
    "# if showing all predictions to log"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Save Weights + Model as JSON + full Model to files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-03-20 06:31:50,803 standalone-scaler-django - DEBUG saving weights\n",
      "2018-03-20 06:31:50,827 standalone-scaler-django - DEBUG saving full keras model to file\n",
      "2018-03-20 06:31:50,887 standalone-scaler-django - DEBUG saving keras model as json to file\n",
      "2018-03-20 06:31:50,890 standalone-scaler-django - INFO neural network created merged_predictions_df.index=30200 columns=['_count' '_original_label_value' '_row_idx' 'arp_hwlen' 'arp_hwtype'\n",
      " 'arp_id' 'arp_op' 'arp_plen' 'arp_ptype' 'dns_default_aa'\n",
      " 'dns_default_ad' 'dns_default_an' 'dns_default_ancount' 'dns_default_ar'\n",
      " 'dns_default_arcount' 'dns_default_cd' 'dns_default_id'\n",
      " 'dns_default_length' 'dns_default_ns' 'dns_default_nscount'\n",
      " 'dns_default_opcode' 'dns_default_qd' 'dns_default_qdcount'\n",
      " 'dns_default_qr' 'dns_default_ra' 'dns_default_rcode' 'dns_default_rd'\n",
      " 'dns_default_tc' 'dns_default_z' 'dns_id' 'eth_id' 'eth_type'\n",
      " 'icmp_addr_mask' 'icmp_code' 'icmp_gw' 'icmp_id' 'icmp_ptr' 'icmp_seq'\n",
      " 'icmp_ts_ori' 'icmp_ts_rx' 'icmp_ts_tx' 'icmp_type' 'icmp_unused' 'idx'\n",
      " 'ip_id' 'ip_ihl' 'ip_len' 'ip_tos' 'ip_version' 'ipv6_fl' 'ipv6_hlim'\n",
      " 'ipv6_nh' 'ipv6_plen' 'ipv6_tc' 'ipv6_version' 'ipvsix_id' 'label_name'\n",
      " 'label_value' 'pad_id' 'prediction_status' 'tcp_dport'\n",
      " 'tcp_fields_options.MSS' 'tcp_fields_options.NOP'\n",
      " 'tcp_fields_options.SAckOK' 'tcp_fields_options.Timestamp'\n",
      " 'tcp_fields_options.WScale' 'tcp_id' 'tcp_seq' 'tcp_sport' 'udp_dport'\n",
      " 'udp_id' 'udp_len' 'udp_sport'] with cross_val_score=[0.00012417218543048225, 0.9982201986754967] and accuracy=99.82201986754967\n",
      "2018-03-20 06:31:50,891 standalone-scaler-django - INFO saved model_backup_file=/data/standalone-scaler-django-full-model.h5 model_weights_file=/data/standalone-scaler-django-weights.h5 model_json_file=/data/standalone-scaler-django-model.json\n"
     ]
    }
   ],
   "source": [
    "log.debug(\"saving weights\")\n",
    "\n",
    "# there are some known issues saving weights:\n",
    "# https://github.com/keras-team/keras/issues/4875\n",
    "model.model.save_weights(model_weights_file)\n",
    "\n",
    "log.debug(\"saving full keras model to file\")\n",
    "\n",
    "model.model.save(model_backup_file)\n",
    "\n",
    "log.debug(\"saving keras model as json to file\")\n",
    "\n",
    "with open(\n",
    "        model_json_file,\n",
    "        \"w\") as json_file:\n",
    "    json_file.write(model.model.to_json())\n",
    "\n",
    "log.info((\"neural network created \"\n",
    "          \"merged_predictions_df.index={} columns={} \"\n",
    "          \"with cross_val_score={} and accuracy={}\")\n",
    "         .format(\n",
    "            len(merged_predictions_df.index),\n",
    "            merged_predictions_df.columns.values,\n",
    "            scores,\n",
    "            accuracy.get(\"accuracy\", None)))\n",
    "\n",
    "log.info((\"saved model_backup_file={} \"\n",
    "          \"model_weights_file={} model_json_file={}\")\n",
    "         .format(\n",
    "            model_backup_file,\n",
    "            model_weights_file,\n",
    "            model_json_file))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Show Prediction Distributions\n",
    "### 0.0 values are correct predictions\n",
    "### 1.0 values are not-correct predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "2018-03-20 06:31:51,091 matplotlib.font_manager - DEBUG findfont: Matching :family=sans-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000\n",
      "2018-03-20 06:31:51,128 matplotlib.font_manager - DEBUG findfont: Matching :family=sans-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=8.0 to DejaVu Sans ('/usr/local/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15.0, 10.0))\n",
    "ax = sns.distplot(merged_predictions_df[\"prediction_status\"])\n",
    "ax.get_figure().text(\n",
    "    0.90,\n",
    "    0.01,\n",
    "    footnote_text,\n",
    "    va=\"bottom\",\n",
    "    fontsize=8,\n",
    "    color=\"#888888\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Display Location of Analysis and Model Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2018-03-20 06:31:51,386 standalone-scaler-django - INFO saved model_backup_file=/data/standalone-scaler-django-full-model.h5 model_weights_file=/data/standalone-scaler-django-weights.h5 model_json_file=/data/standalone-scaler-django-model.json\n"
     ]
    }
   ],
   "source": [
    "log.info((\"saved model_backup_file={} model_weights_file={} \"\n",
    "          \"model_json_file={}\")\n",
    "         .format(\n",
    "            model_backup_file,\n",
    "            model_weights_file,\n",
    "            model_json_file))"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Edit Metadata",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}