ad_tf_autoencoder.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neural Network Autoencoder for anomaly detection in pcap\n",
    "Unsupervised learning using autoencoder. The following approach is used:\n",
    "\n",
    "```\n",
    "pcap in --(1)--> tshark ek json --(2)--> neural network autoencoder --(3)--> top N errornous PDUs --(4)--> pcap out\n",
    "```\n",
    "\n",
    "* (1) The input json is generated by the following command from pcap: tshark -T ek -x -r trace.pcap > trace.json\n",
    "* (2) Only selected raw json fields are used for learning. This is defined in COLUMNS variable. All input bytes are serialized into floats as learning features and used as input to the neural network.\n",
    "* (3) Top N PDUs with highest mean square error are added into output pcap. This is controled by N variable\n",
    "* (4) test_ad.pcap is created\n",
    "\n",
    "Author: Martin Kacer\n",
    "\n",
    "Date: Dec 29, 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "non-resource variables are not supported in the long term\n"
     ]
    }
   ],
   "source": [
    "#!/usr/bin/env python\n",
    "\n",
    "#\n",
    "# Anomaly detection using tensorflow and tshark\n",
    "# Unsupervised learning using autoencoder\n",
    "#\n",
    "# Copyright 2017, H21 lab, Martin Kacer\n",
    "# All the content and resources have been provided in the hope that it will be useful.\n",
    "# Author do not take responsibility for any misapplication of it.\n",
    "#\n",
    "# Based on tensorflow classifier example wide_n_deep_tutorial.py\n",
    "# Copyright 2017, The TensorFlow Authors.\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n",
    "#\n",
    "\n",
    "import sys\n",
    "import json\n",
    "import argparse\n",
    "import tempfile\n",
    "import pandas as pd\n",
    "import operator\n",
    "import subprocess\n",
    "import os\n",
    "import hashlib\n",
    "from datetime import datetime\n",
    "import numpy as np\n",
    "import binascii\n",
    "import tensorflow.compat.v1 as tf\n",
    "tf.disable_v2_behavior() \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "COLUMNS = []\n",
    "LABEL_COLUMN = \"label\"\n",
    "CATEGORICAL_COLUMNS = []\n",
    "CONTINUOUS_COLUMNS = []\n",
    "\n",
    "FLAGS = None\n",
    "\n",
    "\n",
    "# Parameters\n",
    "learning_rate = 0.001\n",
    "training_epochs = 1000\n",
    "batch_size = 200\n",
    "display_step = 50\n",
    "\n",
    "# Network Parameters\n",
    "n_hidden_1 = 20 # 1st layer num features\n",
    "n_hidden_2 = 10 # 2nd layer num features\n",
    "n_input = None\n",
    "data_dir = '.'\n",
    "\n",
    "\n",
    "# Building the encoder\n",
    "def encoder(x):\n",
    "    # Encoder Hidden layer with sigmoid activation #1\n",
    "    layer_1 = tf.nn.tanh(tf.add(tf.matmul(x, weights['encoder_h1']),\n",
    "                                   biases['encoder_b1']))\n",
    "    # Decoder Hidden layer with sigmoid activation #2\n",
    "    layer_2 = tf.nn.tanh(tf.add(tf.matmul(layer_1, weights['encoder_h2']),\n",
    "                                   biases['encoder_b2']))\n",
    "    return layer_2\n",
    "\n",
    "\n",
    "# Building the decoder\n",
    "def decoder(x):\n",
    "    # Encoder Hidden layer with sigmoid activation #1\n",
    "    layer_1 = tf.nn.tanh(tf.add(tf.matmul(x, weights['decoder_h1']),\n",
    "                                   biases['decoder_b1']))\n",
    "    # Decoder Hidden layer with sigmoid activation #2\n",
    "    layer_2 = tf.nn.tanh(tf.add(tf.matmul(layer_1, weights['decoder_h2']),\n",
    "                                   biases['decoder_b2']))\n",
    "    return layer_2\n",
    "\n",
    "\n",
    "def df_to_pcap(j, df_predict, file):\n",
    "    linux_cooked_header = df_predict.at[j, 'linux_cooked_header'];\n",
    "    frame_raw = df_predict.at[j, 'frame_raw']\n",
    "    # for Linux cooked header replace dest MAC and remove two bytes to reconstruct normal frame using text2pcap\n",
    "    if (linux_cooked_header):\n",
    "        frame_raw = \"000000000000\" + frame_raw[6*2:] # replce dest MAC\n",
    "        frame_raw = frame_raw[:12*2] + \"\" + frame_raw[14*2:] # remove two bytes before Protocol\n",
    "    hex_to_txt(frame_raw, file)\n",
    "\n",
    "    \n",
    "def to_pcap_file(filename, output_pcap_file):\n",
    "    FNULL = open(os.devnull, 'w')\n",
    "    subprocess.call([\"text2pcap\", filename, output_pcap_file], stdout=FNULL, stderr=subprocess.STDOUT)\n",
    "\n",
    "    \n",
    "def hex_to_txt(hexstring, output_file):\n",
    "    h = hexstring.lower()\n",
    "    \n",
    "    file = open(output_file, 'a')\n",
    "    \n",
    "    for i in range(0, len(h), 2):\n",
    "        if(i%32 == 0):\n",
    "            file.write(format(int(i/2), '06x') + ' ')\n",
    "        \n",
    "        file.write(h[i:i+2] + ' ')\n",
    "        \n",
    "        if(i%32 == 30):\n",
    "            file.write('\\n')\n",
    "\n",
    "    file.write('\\n')\n",
    "    file.close()\n",
    "\n",
    "    \n",
    "def json_collector(dict, name):\n",
    "    r = []\n",
    "    if hasattr(dict, 'items'):\n",
    "        for k, v in dict.items():\n",
    "            if (k in name):\n",
    "                r.append(v)\n",
    "            else:\n",
    "                val = json_collector(v, name)\n",
    "                if (len(val) > 0):\n",
    "                    r = r + val\n",
    "\n",
    "    return r\n",
    "   \n",
    "    \n",
    "def readJsonEKLine(df, line, label):\n",
    "    # trim end of lines\n",
    "    line = line.rstrip('\\n')\n",
    "    # skip empty lines\n",
    "    if (line.rstrip() == \"\"):\n",
    "        return\n",
    "\n",
    "    j = json.loads(line)\n",
    "                \n",
    "    # frames\n",
    "    if ('layers' in j):\n",
    "        layers = j['layers']\n",
    "        \n",
    "        linux_cooked_header = False\n",
    "        if ('sll_raw' in layers):\n",
    "            linux_cooked_header = True\n",
    "        if ('frame_raw' in layers):\n",
    "            \n",
    "            i = len(df)\n",
    "            \n",
    "            df.loc[i, 'frame_raw'] = layers['frame_raw']\n",
    "            df.loc[i, 'linux_cooked_header'] = linux_cooked_header\n",
    "            \n",
    "            for c in COLUMNS:\n",
    "                v = json_collector(j, [c])\n",
    "                if (len(v) > 0):\n",
    "                    v = v[0]\n",
    "                else:\n",
    "                    v = ''\n",
    "                df.loc[i, c] = v\n",
    "                \n",
    "            df.loc[i, 'label'] = label\n",
    "            \n",
    "\n",
    "def readJsonEK(df, filename, label, limit = 0):\n",
    "    i = 0\n",
    "    while i <= limit:\n",
    "        with open(filename) as f:\n",
    "            for line in f:\n",
    "                if (limit != 0 and i > limit):\n",
    "                    return i\n",
    "                readJsonEKLine(df, line, label)\n",
    "                i = i + 1\n",
    "    return i"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selecting learning features\n",
    "Select the raw fields from json which will be used to learn neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ip_raw', 'tcp_tcp_srcport_raw', 'tcp_tcp_dstport_raw']\n",
      "['ip_raw', 'tcp_tcp_srcport_raw', 'tcp_tcp_dstport_raw']\n",
      "[]\n"
     ]
    }
   ],
   "source": [
    "global COLUMNS\n",
    "global CATEGORICAL_COLUMNS\n",
    "\n",
    "# only raw json fielads are accepted\n",
    "COLUMNS = [\"ip_raw\", \"tcp_tcp_srcport_raw\", \"tcp_tcp_dstport_raw\"]#[\"ip_ip_src_raw\", \"ip_ip_dst_raw\"]\n",
    "CATEGORICAL_COLUMNS = COLUMNS\n",
    "\n",
    "print(COLUMNS)\n",
    "print(CATEGORICAL_COLUMNS)\n",
    "print(CONTINUOUS_COLUMNS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load and parse input JSON\n",
    "Read input json which was generated from pcap by using tshark by the following command:\n",
    "```\n",
    "tshark -T ek -x -r trace.pcap > trace.json\n",
    "```\n",
    "\n",
    "trace.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             frame_raw linux_cooked_header  \\\n",
      "0    0008e23b5601003048624e4a080045000028b4e6400040...               False   \n",
      "1    0008e23b5601003048624e4a080045000028b4e4400040...               False   \n",
      "2    003048624e4a0008e23b56010800450005dc40b3400071...               False   \n",
      "3    003048624e4a0008e23b56010800450002743fe7400071...               False   \n",
      "4    003048624e4a0008e23b56010800450005dc405f400071...               False   \n",
      "..                                                 ...                 ...   \n",
      "343  003048624e4a0008e23b56010800450004284042400071...               False   \n",
      "344  003048624e4a0008e23b56010800450000283bad400071...               False   \n",
      "345  0008e23b5601003048624e4a08004500004e314d400040...               False   \n",
      "346  0008e23b5601003048624e4a080045000028b4ac400040...               False   \n",
      "347  003048624e4a0008e23b56010800450005dc4176400071...               False   \n",
      "\n",
      "                                       ip_raw tcp_tcp_srcport_raw  \\\n",
      "0    45000028b4e6400040068a0bc0960b6f6272cd66                0438   \n",
      "1    45000028b4e4400040068a0dc0960b6f6272cd66                0438   \n",
      "2    450005dc40b340007106c78a6272cd66c0960b6f                0868   \n",
      "3    450002743fe740007106cbbe6272cd66c0960b6f                0868   \n",
      "4    450005dc405f40007106c7de6272cd66c0960b6f                0868   \n",
      "..                                        ...                 ...   \n",
      "343  45000428404240007106c9af6272cd66c0960b6f                0868   \n",
      "344  450000283bad40007106d2446272cd66c0960b6f                071d   \n",
      "345  4500004e314d400040060d7fc0960b6f6272cd66                8dc8   \n",
      "346  45000028b4ac400040068a45c0960b6f6272cd66                0438   \n",
      "347  450005dc417640007106c6c76272cd66c0960b6f                0868   \n",
      "\n",
      "    tcp_tcp_dstport_raw  label  \n",
      "0                  0868    0.0  \n",
      "1                  0868    0.0  \n",
      "2                  0438    0.0  \n",
      "3                  0438    0.0  \n",
      "4                  0438    0.0  \n",
      "..                  ...    ...  \n",
      "343                0438    0.0  \n",
      "344                01bd    0.0  \n",
      "345                22b4    0.0  \n",
      "346                0868    0.0  \n",
      "347                0438    0.0  \n",
      "\n",
      "[348 rows x 6 columns]\n",
      "model directory = /tmp/tmp7nxx8k4r\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame()\n",
    "\n",
    "ln = readJsonEK(df, \"res/trace.json\", 0)\n",
    "#readJsonEK(df, FLAGS.anomaly_tshark_ek_x_json, 1, ln)\n",
    "\n",
    "df = df.sample(frac=1).reset_index(drop=True)\n",
    "\n",
    "print(df)\n",
    "\n",
    "#####################################\n",
    "# train neural network and evaluate #\n",
    "#####################################\n",
    "model_dir = tempfile.mkdtemp()\n",
    "print(\"model directory = %s\" % model_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare data for neural network\n",
    "Calculate bellow the maximum length of columns. This is latter used to create normalized table of floats.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['frame_raw', 'linux_cooked_header', 'ip_raw', 'tcp_tcp_srcport_raw',\n",
      "       'tcp_tcp_dstport_raw', 'label'],\n",
      "      dtype='object')\n",
      "[None, None, 20.0, 2.0, 2.0, None]\n",
      "24.0\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)\n",
    "\n",
    "v_len = 0\n",
    "col_max_len = []\n",
    "for i in range(len(df.columns)):\n",
    "    if (df.columns[i] in COLUMNS):\n",
    "        m = df[df.columns[i]].map(len).max() / 2\n",
    "        col_max_len.append(m)\n",
    "        if (df.columns[i] != 'timestamp'):\n",
    "            v_len = v_len + m\n",
    "        else:\n",
    "            min_t = int(df[df.columns[i]].min())\n",
    "            max_t = int(df[df.columns[i]].max())\n",
    "            print(min_t)\n",
    "            print(max_t)\n",
    "            v_len = v_len + 1\n",
    "        \n",
    "    else:\n",
    "        col_max_len.append(None)\n",
    "\n",
    "print(col_max_len)\n",
    "print(v_len)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create input for neural network\n",
    "np array including float values will be used as input for neural network.\n",
    "* Timestamp (if included in COLUMNS) is normalized into `<0.0; 1.0>` float interval\n",
    "* Bytes are normalized also into `<0.0; 1.0>` float intervals. Every byte is converted into float."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.27058824 0.         0.         ... 0.21960784 0.03137255 0.40784314]\n",
      " [0.27058824 0.         0.         ... 0.21960784 0.03137255 0.40784314]\n",
      " [0.27058824 0.         0.01960784 ... 0.40784314 0.01568627 0.21960784]\n",
      " ...\n",
      " [0.27058824 0.         0.         ... 0.78431373 0.13333333 0.70588235]\n",
      " [0.27058824 0.         0.         ... 0.21960784 0.03137255 0.40784314]\n",
      " [0.27058824 0.         0.01960784 ... 0.40784314 0.01568627 0.21960784]]\n"
     ]
    }
   ],
   "source": [
    "train_x = np.array([0]*int(v_len), np.float)\n",
    "#print(train_x)\n",
    "\n",
    "for index, row in df.iterrows():\n",
    "    #train_x = np.append(train_x, [1])\n",
    "    \n",
    "    f_array = []\n",
    "    #print(row)\n",
    "    c = 0\n",
    "    for column in df.columns:\n",
    "        if column in COLUMNS:\n",
    "            #print(i)\n",
    "            #print(s)\n",
    "            #print(s)\n",
    "            #print(column)\n",
    "            v = row.loc[column]\n",
    "            r = 0\n",
    "            #print(v)\n",
    "            if (column == 'timestamp'):\n",
    "                v = int(v)\n",
    "                #print(v)\n",
    "                r = float(v - min_t)/float(max_t - min_t)\n",
    "                #print(r)\n",
    "                f_array.append(r)\n",
    "            else:\n",
    "                #print(str(v))\n",
    "                s = binascii.unhexlify(str(v))\n",
    "                #print(s)\n",
    "                for i in range(int(col_max_len[c])):\n",
    "                    r = 0.0\n",
    "                    if (i < len(s)):\n",
    "                        #print(ord(s[i]))\n",
    "                        if (sys.version_info > (3, 0)):\n",
    "                            # Python 3 code in this block\n",
    "                            r = float(s[i])/255.0\n",
    "                        else:\n",
    "                            r = float(ord(s[i]))/255.0\n",
    "                    f_array.append(r)\n",
    "\n",
    "        c = c + 1\n",
    "    \n",
    "    #print(train_x)\n",
    "    #print(f_array)\n",
    "    train_x = np.vstack([train_x, f_array])\n",
    "\n",
    "# delete the first dummy row\n",
    "train_x = np.delete(train_x, 0, 0)\n",
    "\n",
    "print(train_x)\n",
    "#print(train_x.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize input data features\n",
    "Histograms of inputs features which will be forwarded to neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(348, 24)\n",
      "(348, 19)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1440 with 19 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mydf = pd.DataFrame(train_x)\n",
    "print(mydf.shape)\n",
    "\n",
    "# remove static columns\n",
    "mydf = mydf.loc[:, (mydf != mydf.iloc[0]).any()]\n",
    "\n",
    "#print(mydf)\n",
    "print(mydf.shape)\n",
    "\n",
    "plt.figure(figsize=(12,5*4))\n",
    "gs = gridspec.GridSpec(len(mydf.columns), 1)\n",
    "for i, cn in enumerate(mydf.columns):\n",
    "    ax = plt.subplot(gs[i])\n",
    "    sns.distplot(mydf[cn], bins=50)\n",
    "    #sns.distplot(mydf[cn][mydf.Class == 0], bins=50)\n",
    "    ax.set_xlabel('')\n",
    "    ax.set_title('histogram of feature: ' + str(cn))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create and train the neural network\n",
    "The autoencoder is created and trained below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_input = 24\n",
      "n_input_1 = 20\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/rmsprop.py:123: calling Ones.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "Total batches = 1\n",
      "Epoch: 0001 cost= 1.104477882\n",
      "Epoch: 0051 cost= 1.079803586\n",
      "Epoch: 0101 cost= 0.859455645\n",
      "Epoch: 0151 cost= 0.605037153\n",
      "Epoch: 0201 cost= 0.517840981\n",
      "Epoch: 0251 cost= 0.388763189\n",
      "Epoch: 0301 cost= 0.352327615\n",
      "Epoch: 0351 cost= 0.318870664\n",
      "Epoch: 0401 cost= 0.273631185\n",
      "Epoch: 0451 cost= 0.243915379\n",
      "Epoch: 0501 cost= 0.243163094\n",
      "Epoch: 0551 cost= 0.220409781\n",
      "Epoch: 0601 cost= 0.210709348\n",
      "Epoch: 0651 cost= 0.185023069\n",
      "Epoch: 0701 cost= 0.174985573\n",
      "Epoch: 0751 cost= 0.169261545\n",
      "Epoch: 0801 cost= 0.161907449\n",
      "Epoch: 0851 cost= 0.146588057\n",
      "Epoch: 0901 cost= 0.143820509\n",
      "Epoch: 0951 cost= 0.138220876\n",
      "Optimization Finished!\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model saved in file: ./model.ckpt\n"
     ]
    }
   ],
   "source": [
    "n_input = train_x.shape[1]\n",
    "print(\"n_input = \" + str(n_input))\n",
    "print(\"n_input_1 = \" + str(n_hidden_1))\n",
    "\n",
    "X = tf.placeholder(\"float\", [None, n_input])\n",
    "\n",
    "weights = {\n",
    "    'encoder_h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),\n",
    "    'encoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\n",
    "    #'decoder_h1': tf.Variable(tf.random_normal([n_hidden_1, n_input])),\n",
    "    'decoder_h1': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_1])),\n",
    "    'decoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_input])),\n",
    "}\n",
    "biases = {\n",
    "    'encoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),\n",
    "    'encoder_b2': tf.Variable(tf.random_normal([n_hidden_2])),\n",
    "    'decoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),\n",
    "    'decoder_b2': tf.Variable(tf.random_normal([n_input])),\n",
    "}\n",
    "\n",
    "encoder_op = encoder(X)\n",
    "decoder_op = decoder(encoder_op)\n",
    "\n",
    "# Prediction\n",
    "y_pred = decoder_op\n",
    "# Targets (Labels) are the input data.\n",
    "y_true = X\n",
    "\n",
    "# Define batch mse\n",
    "batch_mse = tf.reduce_mean(tf.pow(y_true - y_pred, 2), 1)\n",
    "\n",
    "# Define loss and optimizer, minimize the squared error\n",
    "cost = tf.reduce_mean(tf.pow(y_true - y_pred, 2))\n",
    "optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(cost)\n",
    "\n",
    "save_model = os.path.join(data_dir, 'model.ckpt')\n",
    "saver = tf.train.Saver()\n",
    "\n",
    "# Initializing the variables\n",
    "init = tf.global_variables_initializer()\n",
    "\n",
    "c_hist = []\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    now = datetime.now()\n",
    "    sess.run(init)\n",
    "    total_batch = int(train_x.shape[0]/batch_size)\n",
    "    print(\"Total batches = \" + str(total_batch))\n",
    "    \n",
    "    # Training cycle\n",
    "    for epoch in range(training_epochs):\n",
    "        # Loop over all batches\n",
    "        for i in range(total_batch):\n",
    "            batch_idx = np.random.choice(train_x.shape[0], batch_size)\n",
    "            batch_xs = train_x[batch_idx]\n",
    "            #print batch_xs.shape\n",
    "            #print train_x.shape\n",
    "            \n",
    "            # Run optimization op (backprop) and cost op (to get loss value)\n",
    "            _, c = sess.run([optimizer, cost], feed_dict={X: batch_xs})\n",
    "\n",
    "        # Display logs per epoch step\n",
    "        if epoch % display_step == 0:\n",
    "            train_batch_mse = sess.run(batch_mse, feed_dict={X: train_x})\n",
    "            print(\"Epoch:\", '%04d' % (epoch+1),\n",
    "                  \"cost=\", \"{:.9f}\".format(c))\n",
    "        c_hist.append(c)\n",
    "\n",
    "    print(\"Optimization Finished!\")\n",
    "    \n",
    "    #print c_hist\n",
    "    plt.plot(c_hist)\n",
    "    plt.title('model cost')\n",
    "    plt.ylabel('cost')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.legend(['train_x'], loc='upper right');\n",
    "    plt.show()\n",
    "    \n",
    "\n",
    "    save_path = saver.save(sess, save_model)\n",
    "    print(\"Model saved in file: %s\" % save_path)\n",
    "    \n",
    "    #print(train_batch_mse)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test the network\n",
    "Use the same input for testing. Get the mean square error and also the output data (reconstructed_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./model.ckpt\n",
      "Epoch: 1000 cost= 0.128818870\n",
      "[[ 0.20601809 -0.01514164 -0.03238092 ...  0.22005895 -0.99994946\n",
      "   0.4234564 ]\n",
      " [ 0.21207978 -0.01174885 -0.03104331 ...  0.21243478 -0.999949\n",
      "   0.42351437]\n",
      " [ 0.26479888 -0.00124848  0.03010795 ...  0.41555598  0.06080585\n",
      "   0.28818822]\n",
      " ...\n",
      " [ 0.4611879   0.13996233  0.22656552 ...  0.576597   -0.999999\n",
      "   0.4096785 ]\n",
      " [ 0.44018131  0.03743055  0.16396609 ...  0.32383314 -0.9999525\n",
      "   0.3914448 ]\n",
      " [ 0.27643663  0.01262085  0.03895006 ...  0.40704346  0.10349803\n",
      "   0.30583125]]\n",
      "[0.11387312 0.11314539 0.15382628 0.1552335  0.1566186  0.1605662\n",
      " 0.10361862 0.15983409 0.11731387 0.09735768 0.15298687 0.11547271\n",
      " 0.14817105 0.15777902 0.15345941 0.16135076 0.16255473 0.10405281\n",
      " 0.1620946  0.15729494 0.09904191 0.08611488 0.10582491 0.10434117\n",
      " 0.0881882  0.08085641 0.1548691  0.1482714  0.09826346 0.08568817\n",
      " 0.14796752 0.08068695 0.10892153 0.10641424 0.1759808  0.07961606\n",
      " 0.16427813 0.14923167 0.15821229 0.14871706 0.11635891 0.16431971\n",
      " 0.15067545 0.09127036 0.08882096 0.11148927 0.1041918  0.16477793\n",
      " 0.0914664  0.14881833 0.10744246 0.15062408 0.15493649 0.16279604\n",
      " 0.10693663 0.10074666 0.10814881 0.09845444 0.14924145 0.15360473\n",
      " 0.16219907 0.15655883 0.14863922 0.16739564 0.11682721 0.09846684\n",
      " 0.1037856  0.1515169  0.11212689 0.15596777 0.16172667 0.08121597\n",
      " 0.14932394 0.08916956 0.10741025 0.15603976 0.0816015  0.15514208\n",
      " 0.10458857 0.15717645 0.10690548 0.0896695  0.16069798 0.1533633\n",
      " 0.16101368 0.14862588 0.1548763  0.16406082 0.1624065  0.10380944\n",
      " 0.10973146 0.1626148  0.11087979 0.11029485 0.15625376 0.14896668\n",
      " 0.14756174 0.10603818 0.153287   0.16854633 0.11782012 0.10343017\n",
      " 0.1541826  0.17883635 0.11425378 0.08916444 0.15340984 0.15600567\n",
      " 0.09077942 0.16408008 0.16479348 0.10561675 0.16297549 0.15659444\n",
      " 0.1540821  0.1483003  0.0891667  0.15763076 0.14769599 0.15757251\n",
      " 0.07930592 0.14783266 0.15019853 0.15880223 0.15935038 0.11279682\n",
      " 0.14782836 0.10342995 0.10866004 0.16458072 0.08935326 0.07995362\n",
      " 0.08922083 0.1129692  0.1485553  0.15005453 0.15633339 0.14925839\n",
      " 0.11058446 0.14817573 0.07983189 0.1553699  0.14817911 0.1618943\n",
      " 0.1573167  0.19391768 0.10541415 0.08140558 0.10519918 0.10918713\n",
      " 0.10345205 0.08957424 0.15818284 0.15537328 0.1636191  0.14782481\n",
      " 0.14918107 0.09107163 0.09839223 0.15479143 0.16519357 0.15398194\n",
      " 0.1067057  0.08008408 0.16091324 0.15317433 0.09018149 0.10484373\n",
      " 0.08036995 0.08052468 0.10596713 0.08201122 0.10354555 0.14837635\n",
      " 0.08222471 0.1565688  0.14944445 0.11590779 0.1640331  0.09853772\n",
      " 0.15864287 0.15872021 0.11180431 0.14856191 0.10356659 0.1483445\n",
      " 0.15861574 0.15527989 0.15773548 0.14825557 0.1585803  0.10370693\n",
      " 0.07971927 0.10392505 0.08609486 0.15375315 0.09867021 0.16269499\n",
      " 0.16536142 0.11350393 0.15452671 0.08919456 0.15910624 0.16004218\n",
      " 0.15955779 0.16601314 0.10466785 0.15597391 0.15661322 0.16810083\n",
      " 0.15326379 0.09637645 0.09949271 0.16323353 0.10393826 0.1592943\n",
      " 0.0892115  0.11001062 0.07943965 0.085806   0.14911155 0.10412186\n",
      " 0.1640564  0.14758094 0.07936725 0.15777108 0.14758813 0.14868292\n",
      " 0.15684406 0.10945704 0.10450011 0.15817863 0.158807   0.08022297\n",
      " 0.15535603 0.14919241 0.15610674 0.15621208 0.14889759 0.15199853\n",
      " 0.11889682 0.15216735 0.16871154 0.10521743 0.15404312 0.10349701\n",
      " 0.15528329 0.152788   0.16009365 0.16478653 0.08566707 0.15116267\n",
      " 0.16249566 0.16154832 0.1549394  0.15337516 0.1511526  0.14844613\n",
      " 0.15357353 0.15513177 0.08103284 0.11464665 0.10502711 0.1514473\n",
      " 0.08886376 0.15445302 0.1076527  0.10344999 0.09719133 0.15888037\n",
      " 0.16717422 0.15865034 0.1506852  0.10348896 0.10840251 0.15670578\n",
      " 0.08848226 0.10433805 0.15547289 0.14831188 0.08941654 0.1129692\n",
      " 0.07952275 0.10366225 0.15298118 0.1642855  0.15456404 0.16267635\n",
      " 0.13413721 0.14902392 0.16128743 0.1673405  0.17425096 0.16681962\n",
      " 0.0896621  0.10647881 0.10717154 0.14931637 0.08918174 0.15634991\n",
      " 0.15959056 0.14958356 0.16071819 0.17419077 0.09843323 0.08180342\n",
      " 0.15490428 0.1530773  0.11118125 0.15445231 0.15576614 0.14897363\n",
      " 0.16663675 0.10556266 0.1547621  0.08925578 0.11834729 0.10625625\n",
      " 0.09831662 0.15490241 0.15305603 0.09111112 0.09840535 0.08917663\n",
      " 0.16412206 0.10789889 0.09602892 0.08949006 0.15468106 0.09824491\n",
      " 0.1569892  0.09844667 0.15904766 0.15239011 0.15577619 0.16155683\n",
      " 0.09874191 0.15715434 0.1031758  0.11505266 0.11086494 0.11245754\n",
      " 0.0892998  0.1513267  0.15767343 0.10084212 0.10487501 0.15430862]\n"
     ]
    }
   ],
   "source": [
    "save_model = os.path.join(data_dir, 'model.ckpt')\n",
    "saver = tf.train.Saver()\n",
    "\n",
    "# Initializing the variables\n",
    "init = tf.global_variables_initializer()\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    now = datetime.now()\n",
    "    \n",
    "    saver.restore(sess, save_model)\n",
    "    \n",
    "    test_batch_mse, reconstructed_x = sess.run([batch_mse, decoder_op], feed_dict={X: train_x})\n",
    "    \n",
    "    print(\"Epoch:\", '%04d' % (epoch+1),\n",
    "                  \"cost=\", \"{:.9f}\".format(c))\n",
    "    \n",
    "    print(reconstructed_x)\n",
    "    print(test_batch_mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Show the Error Distribution\n",
    "Display the Mean Square Error distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zC1ySGmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUqCZP5FkIJzhIrfMknfa5BS5JjTLAJalRBrgkNcoAl6RGjfQjZpLVwG3AXcD6qlo+jqIkSdMbx1Eov1tVPx3DciRJm8BdKJLUqFEDvIAzklyUZMU4CpIkDWfUXSj7V9W1SR4OnJnkyqo6Z3CGPthXACxdunTE5mZm1BN/NqcTh6bqy+bUR82tiScE+fmZOyNtgVfVtf2/64CTgX0nmef4qlpeVcuXLFkySnOSpAEzDvAk2yV5yIZh4EDgsnEVJknauFF2oewMnJxkw3I+VVVfHktVkqRpzTjAq+oHwBPHWIskaRN4GKEkNcoAl6RGGeCS1Kgmb+gwyOOXJzfMxfq9oH87XFeajFvgktQoA1ySGmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEY1fyLPbNmST5zYWN89WUpaONwCl6RGGeCS1CgDXJIaZYBLUqMMcElqlAEuSY0ywCWpUQa4JDXKE3lmwSh3w1noJ8oM07eF3geNbmOfg1HukuUdtjaNW+CS1CgDXJIaZYBLUqMMcElqlAEuSY0ywCWpUQa4JDVqiz4OfKHftGGh19cKjy2eP+N676dazkJetxP/fmejPrfAJalRBrgkNcoAl6RGGeCS1CgDXJIaNVKAJzkoyXeTfC/JMeMqSpI0vRkHeJKtgH8GXgDsDRyRZO9xFSZJ2rhRtsD3Bb5XVT+oql8DnwEOGU9ZkqTppKpm9sLkMOCgqnpN//xI4Ler6vUT5lsBrOif7gV8d+blArAY+OmIy1ho7FMb7FM7Nrd+PaqqlkwcOetnYlbV8cDx41pekguravm4lrcQ2Kc22Kd2bK79mmiUXSjXAo8ceP6IfpwkaQ6MEuAXAHskeXSSBwIvB04ZT1mSpOnMeBdKVa1P8nrgdGAr4CNVtWpslU1tbLtjFhD71Ab71I7NtV/3MeMfMSVJ88szMSWpUQa4JDVqQQX4dKfmJ9kmyUn99G8mWdaP3zrJiUkuTXJFkr+Y69qnMkSfnpnk4iTr+2PrB6cdleTq/nHU3FW9cTPtU5J9kpyXZFWS7yQ5fG4rn9oo66mf/tAka5K8f24qnt6In72lSc7o/54u3/C3Nt9G7NM/9J+9K5L8zySZu8pnSVUtiAfdD6HfB3YHHgh8G9h7wjx/AnygH345cFI//AfAZ/rhBwOrgWWN9GkZ8J+AjwGHDYzfCfhB/++O/fCOjfdpT2CPfnhXYC2wQ8t9Gph+HPAp4P3z3Z9x9Ak4G3heP7w98OCW+wQ8HTi3X8ZWwHnAAfPdp1EfC2kLfJhT8w8BTuyHPw88p/9ftIDtkiwCHgT8Grh1bsreqGn7VFWrq+o7wN0TXvt84MyquqmqbgbOBA6ai6KnMeM+VdVVVXV1P/wTYB1wv7PL5sEo64kkTwF2Bs6Yi2KHNOM+9dc0WlRVZ/bz3V5Vv5ijujdmlPVUwLZ0wb8NsDVw/eyXPLsWUoDvBlwz8HxNP27SeapqPfAz4GF0Yf5zui26HwPvqaqbZrvgIQzTp9l47WwaS11J9qX7Y/r+mOoaxYz7lOQBwHuBN89CXaMYZT3tCdyS5ItJvpXk3f3F6+bbjPtUVecBX6XLiLXA6VV1xdgrnGMLKcBHsS9wF93X8kcDb0qy+/yWpKkk2QX4OPCHVXW/LdrG/AlwWlWtme9CxmgR8Ay6/5SeSrfL4uj5LGhUSR4LPJ7ujPHdgGcnecb8VjW6hRTgw5yaf888/e6S3wBupNsH/uWqurOq1tHt61oI10EY5XIDC/VSBSPVleShwKnA26rq/DHXNlOj9OlpwOuTrAbeA7wqycrxljcjo/RpDXBJv6tiPfBvwJPHXN9MjNKnFwPn97uDbge+RLfumraQAnyYU/NPATYcjXEY8JXqfqH4MfBsgCTbAfsBV85J1Rs3yuUGTgcOTLJjkh2BA/tx823GfernPxn4WFV9fhZr3FQz7lNVvaKqllbVMrot1o9V1UK4uckon70LgB2SbPh94tnA5bNQ46YapU8/Bp6VZFGSrYFnAc3vQpn3X1EHH8DBwFV0+0Xf1o97B/D7/fC2wOeA7wH/Aexe9/5K/jlgFd0H7S3z3ZdN6NNT6bZ4fk73bWLVwGv/qO/r9+h2N8x7f0bpE/BK4E7gkoHHPvPdn1HX08AyjmaBHIUyhs/e84DvAJcCJwAPnO/+jPjZ2wr4IF1oXw68b777Mo6Hp9JLUqMW0i4USdImMMAlqVEGuCQ1ygCXpEYZ4JLUKANckhplgEtj1p8lLM06A1wLXpJlSa5MckKSq5J8Mslzk5zbXyt9336+ZyW5pH98K8lD+vFvSXJBfw3yv5lk+Vv1y74s3TXl/7wf/9gk/zfJt/trTD8mnXcPzHt4P+8BSb6e5BTg8n6Z7x5o97Vz+JZpC+GWglrxWOCldGenXkB3/Zv9gd8HjgUOpTuV/U+r6twk2wN3JDkQ2IPugmcBTknyzKo6Z2DZ+wC7VdUTAJLs0I//JLCyqk5Osi3dBs9L+vmfCCwGLkiyYVlPBp5QVT9MsgL4WVU9Nck2wLlJzqiqH87Ce6MtlFvgasUPq+rS6q5euAo4q7rTiC+lu4g/dBcxe1+SP6O7UcR6umvIHAh8C7gYeBxdoA/6AbB7kn9KchBwa7/1vltVnQxQVXdUd03s/YFPV9VdVXU98DW607cB/mMgoA+ku7DVJcA36S57PLFdaSRugasVvxoYvnvg+d30n+OqWpnkVLrrZZyb5Pl0W91/V1UfnGrBVXVzkifS3UTjdcDLgDfMoMafDwwH+C9VtRAuQKbNlFvg2mwkeUy/lf73dLtZHkd3Bcc/6nepkGS3JA+f8LrFwAOq6gvAXwJPrqrbgDVJDu3n2SbJg4GvA4f3+7iXAM+ku7DaRKcDf9xf+Y4ke/ZXypTGxi1wbU7emOR36bbKVwFfqqpfJXk8cF5/D9vb6a6KuG7gdbsBH+3vrgOw4abYRwIfTPIOuqsovpTucrhPo7sfYwFvrarrkjxuQi3/i27XzsX9bf9uoNtPL42NVyOUpEa5C0WSGmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEb9f+J0r+0LYlE2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(test_batch_mse, bins = 100)\n",
    "plt.title(\"test_batch_mse mse distribution\")\n",
    "plt.xlabel(\"mse score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Display also the input/output data histograms\n",
    "To see how the neural autoencoder has learned on the features. Compare input and output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(348, 24)\n",
      "(348, 24)\n",
      "(348, 24)\n",
      "(348, 19)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1440 with 19 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mydf = pd.DataFrame(reconstructed_x)\n",
    "mydf_orig = pd.DataFrame(train_x)\n",
    "print(mydf.shape)\n",
    "print(mydf_orig.shape)\n",
    "\n",
    "# remove static columns\n",
    "mydf = mydf.loc[:, (mydf != mydf.iloc[0]).any()]\n",
    "mydf_orig = mydf_orig.loc[:, (mydf_orig != mydf_orig.iloc[0]).any()]\n",
    "\n",
    "print(mydf.shape)\n",
    "print(mydf_orig.shape)\n",
    "\n",
    "plt.figure(figsize=(12,5*4))\n",
    "gs = gridspec.GridSpec(len(mydf.columns), 1)\n",
    "for i, cn in enumerate(mydf_orig.columns):\n",
    "    ax = plt.subplot(gs[i])\n",
    "    sns.distplot(mydf_orig[cn], bins=50)\n",
    "    sns.distplot(mydf[cn], bins=50)\n",
    "    #sns.distplot(mydf[cn][mydf.Class == 0], bins=50)\n",
    "    ax.set_xlabel('')\n",
    "    ax.set_title('histogram of feature: ' + str(cn))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate output anomaly pcap\n",
    "The top N packets with highest MSE are included in the output pcap. ad_test.pcap is created in the current directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "[145 103  34 292 303 242  99 209  63 291 270 293 312 205 198 160 110 249\n",
      "  47 129  41 285  36 324 109  87 222 178 154 213]\n",
      "MSE = 0.19391768\n",
      "                                             frame_raw linux_cooked_header  \\\n",
      "145  003048624e4a0008e23b560108004500005c3dbb400071...               False   \n",
      "\n",
      "                                       ip_raw tcp_tcp_srcport_raw  \\\n",
      "145  4500005c3dbb40007106d0026272cd66c0960b6f                22b4   \n",
      "\n",
      "    tcp_tcp_dstport_raw  label  \n",
      "145                8dc8    0.0  \n",
      "MSE = 0.17883635\n",
      "                                             frame_raw linux_cooked_header  \\\n",
      "103  003048624e4a0008e23b56010800450000403d68400071...               False   \n",
      "\n",
      "                                       ip_raw tcp_tcp_srcport_raw  \\\n",
      "103  450000403d6840007106d0716272cd66c0960b6f                22b4   \n",
      "\n",
      "    tcp_tcp_dstport_raw  label  \n",
      "103                8dc8    0.0  \n",
      "MSE = 0.1759808\n",
      "                                            frame_raw linux_cooked_header  \\\n",
      "34  003048624e4a0008e23b56010800450000323cfa400071...               False   \n",
      "\n",
      "                                      ip_raw tcp_tcp_srcport_raw  \\\n",
      "34  450000323cfa40007106d0ed6272cd66c0960b6f                0784   \n",
      "\n",
      "   tcp_tcp_dstport_raw  label  \n",
      "34                07a5    0.0  \n",
      "MSE = 0.17425096\n",
      "                                             frame_raw linux_cooked_header  \\\n",
      "292  003048624e4a0008e23b56010800450005dc3c3e400071...               False   \n",
      "\n",
      "                                       ip_raw tcp_tcp_srcport_raw  \\\n",
      "292  450005dc3c3e40007106cbff6272cd66c0960b6f                0724   \n",
      "\n",
      "    tcp_tcp_dstport_raw  label  \n",
      "292                01bd    0.0  \n",
      "MSE = 0.17419077\n",
      "                                             frame_raw linux_cooked_header  \\\n",
      "303  003048624e4a0008e23b56010800450005dc3c3d400071...               False   \n",
      "\n",
      "                                       ip_raw tcp_tcp_srcport_raw  \\\n",
      "303  450005dc3c3d40007106cc006272cd66c0960b6f                0724   \n",
      "\n",
      "    tcp_tcp_dstport_raw  label  \n",
      "303                01bd    0.0  \n",
      "MSE = 0.16871154\n",
      "                                             frame_raw linux_cooked_header  \\\n",
      "242  003048624e4a0008e23b56010800450000c83c2f400071...               False   \n",
      "\n",
      "                                       ip_raw tcp_tcp_srcport_raw  \\\n",
      "242  450000c83c2f40007106d1226272cd66c0960b6f                0724   \n",
      "\n",
      "    tcp_tcp_dstport_raw  label  \n",
      "242                01bd    0.0  \n",
      "MSE = 0.16854633\n",
      "                                            frame_raw linux_cooked_header  \\\n",
      "99  003048624e4a0008e23b56010800450001063bf0400071...               False   \n",
      "\n",
      "                                      ip_raw tcp_tcp_srcport_raw  \\\n",
      "99  450001063bf040007106d1236272cd66c0960b6f                0724   \n",
      "\n",
      "   tcp_tcp_dstport_raw  label  \n",
      "99                01bd    0.0  \n",
      "MSE = 0.16810083\n",
      "                                             frame_raw linux_cooked_header  \\\n",
      "209  003048624e4a0008e23b56010800450000513dac400071...               False   \n",
      "\n",
      "                                       ip_raw tcp_tcp_srcport_raw  \\\n",
      "209  450000513dac40007106d01c6272cd66c0960b6f                22b4   \n",
      "\n",
      "    tcp_tcp_dstport_raw  label  \n",
      "209                8dc8    0.0  \n",
      "MSE = 0.16739564\n",
      "                                            frame_raw linux_cooked_header  \\\n",
      "63  003048624e4a0008e23b56010800450004283dfb400071...               False   \n",
      "\n",
      "                                      ip_raw tcp_tcp_srcport_raw  \\\n",
      "63  450004283dfb40007106cbf66272cd66c0960b6f                0868   \n",
      "\n",
      "   tcp_tcp_dstport_raw  label  \n",
      "63                0438    0.0  \n",
      "MSE = 0.1673405\n",
      "                                             frame_raw linux_cooked_header  \\\n",
      "291  003048624e4a0008e23b56010800450004283ef9400071...               False   \n",
      "\n",
      "                                       ip_raw tcp_tcp_srcport_raw  \\\n",
      "291  450004283ef940007106caf86272cd66c0960b6f                0868   \n",
      "\n",
      "    tcp_tcp_dstport_raw  label  \n",
      "291                0438    0.0  \n",
      "MSE = 0.16717422\n",
      "                                             frame_raw linux_cooked_header  \\\n",
      "270  003048624e4a0008e23b560108004500042840fa400071...               False   \n",
      "\n",
      "                                       ip_raw tcp_tcp_srcport_raw  \\\n",
      "270  4500042840fa40007106c8f76272cd66c0960b6f                0868   \n",
      "\n",
      "    tcp_tcp_dstport_raw  label  \n",
      "270                0438    0.0  \n",
      "MSE = 0.16681962\n",
      "                                             frame_raw linux_cooked_header  \\\n",
      "293  003048624e4a0008e23b56010800450005dc4002400071...               False   \n",
      "\n",
      "                                       ip_raw tcp_tcp_srcport_raw  \\\n",
      "293  450005dc400240007106c83b6272cd66c0960b6f                0868   \n",
      "\n",
      "    tcp_tcp_dstport_raw  label  \n",
      "293                0438    0.0  \n",
      "MSE = 0.16663675\n",
      "                                             frame_raw linux_cooked_header  \\\n",
      "312  003048624e4a0008e23b56010800450005dc3e04400071...               False   \n",
      "\n",
      "                                       ip_raw tcp_tcp_srcport_raw  \\\n",
      "312  450005dc3e0440007106ca396272cd66c0960b6f                0868   \n",
      "\n",
      "    tcp_tcp_dstport_raw  label  \n",
      "312                0438    0.0  \n",
      "MSE = 0.16601314\n",
      "                                             frame_raw linux_cooked_header  \\\n",
      "205  003048624e4a0008e23b56010800450005dc3f0a400071...               False   \n",
      "\n",
      "                                       ip_raw tcp_tcp_srcport_raw  \\\n",
      "205  450005dc3f0a40007106c9336272cd66c0960b6f                0868   \n",
      "\n",
      "    tcp_tcp_dstport_raw  label  \n",
      "205                0438    0.0  \n",
      "MSE = 0.16536142\n",
      "                                             frame_raw linux_cooked_header  \\\n",
      "198  003048624e4a0008e23b56010800450000344199400071...               False   \n",
      "\n",
      "                                       ip_raw tcp_tcp_srcport_raw  \\\n",
      "198  45000034419940007106cc4c6272cd66c0960b6f                22b4   \n",
      "\n",
      "    tcp_tcp_dstport_raw  label  \n",
      "198                8dc8    0.0  \n",
      "MSE = 0.16519357\n",
      "                                             frame_raw linux_cooked_header  \\\n",
      "160  003048624e4a0008e23b56010800450001b83c3f400071...               False   \n",
      "\n",
      "                                       ip_raw tcp_tcp_srcport_raw  \\\n",
      "160  450001b83c3f40007106d0226272cd66c0960b6f                0724   \n",
      "\n",
      "    tcp_tcp_dstport_raw  label  \n",
      "160                01bd    0.0  \n",
      "MSE = 0.16479348\n",
      "                                             frame_raw linux_cooked_header  \\\n",
      "110  003048624e4a0008e23b56010800450005dc3f19400071...               False   \n",
      "\n",
      "                                       ip_raw tcp_tcp_srcport_raw  \\\n",
      "110  450005dc3f1940007106c9246272cd66c0960b6f                0868   \n",
      "\n",
      "    tcp_tcp_dstport_raw  label  \n",
      "110                0438    0.0  \n",
      "MSE = 0.16478653\n",
      "                                             frame_raw linux_cooked_header  \\\n",
      "249  003048624e4a0008e23b560108004500042840ef400071...               False   \n",
      "\n",
      "                                       ip_raw tcp_tcp_srcport_raw  \\\n",
      "249  4500042840ef40007106c9026272cd66c0960b6f                0868   \n",
      "\n",
      "    tcp_tcp_dstport_raw  label  \n",
      "249                0438    0.0  \n",
      "MSE = 0.16477793\n",
      "                                            frame_raw linux_cooked_header  \\\n",
      "47  003048624e4a0008e23b5601080045000034419b400071...               False   \n",
      "\n",
      "                                      ip_raw tcp_tcp_srcport_raw  \\\n",
      "47  45000034419b40007106cc4a6272cd66c0960b6f                22b4   \n",
      "\n",
      "   tcp_tcp_dstport_raw  label  \n",
      "47                8dc8    0.0  \n",
      "MSE = 0.16458072\n",
      "                                             frame_raw linux_cooked_header  \\\n",
      "129  003048624e4a0008e23b560108004500008a3c0b400071...               False   \n",
      "\n",
      "                                       ip_raw tcp_tcp_srcport_raw  \\\n",
      "129  4500008a3c0b40007106d1846272cd66c0960b6f                0724   \n",
      "\n",
      "    tcp_tcp_dstport_raw  label  \n",
      "129                01bd    0.0  \n",
      "MSE = 0.16431971\n",
      "                                            frame_raw linux_cooked_header  \\\n",
      "41  003048624e4a0008e23b56010800450005dc3e22400071...               False   \n",
      "\n",
      "                                      ip_raw tcp_tcp_srcport_raw  \\\n",
      "41  450005dc3e2240007106ca1b6272cd66c0960b6f                0868   \n",
      "\n",
      "   tcp_tcp_dstport_raw  label  \n",
      "41                0438    0.0  \n",
      "MSE = 0.1642855\n",
      "                                             frame_raw linux_cooked_header  \\\n",
      "285  003048624e4a0008e23b56010800450004283f00400071...               False   \n",
      "\n",
      "                                       ip_raw tcp_tcp_srcport_raw  \\\n",
      "285  450004283f0040007106caf16272cd66c0960b6f                0868   \n",
      "\n",
      "    tcp_tcp_dstport_raw  label  \n",
      "285                0438    0.0  \n",
      "MSE = 0.16427813\n",
      "                                            frame_raw linux_cooked_header  \\\n",
      "36  003048624e4a0008e23b56010800450005dc3e39400071...               False   \n",
      "\n",
      "                                      ip_raw tcp_tcp_srcport_raw  \\\n",
      "36  450005dc3e3940007106ca046272cd66c0960b6f                0868   \n",
      "\n",
      "   tcp_tcp_dstport_raw  label  \n",
      "36                0438    0.0  \n",
      "MSE = 0.16412206\n",
      "                                             frame_raw linux_cooked_header  \\\n",
      "324  003048624e4a0008e23b56010800450005dc4127400071...               False   \n",
      "\n",
      "                                       ip_raw tcp_tcp_srcport_raw  \\\n",
      "324  450005dc412740007106c7166272cd66c0960b6f                0868   \n",
      "\n",
      "    tcp_tcp_dstport_raw  label  \n",
      "324                0438    0.0  \n",
      "MSE = 0.16408008\n",
      "                                             frame_raw linux_cooked_header  \\\n",
      "109  003048624e4a0008e23b56010800450005dc3e2a400071...               False   \n",
      "\n",
      "                                       ip_raw tcp_tcp_srcport_raw  \\\n",
      "109  450005dc3e2a40007106ca136272cd66c0960b6f                0868   \n",
      "\n",
      "    tcp_tcp_dstport_raw  label  \n",
      "109                0438    0.0  \n",
      "MSE = 0.16406082\n",
      "                                            frame_raw linux_cooked_header  \\\n",
      "87  003048624e4a0008e23b56010800450005dc3f32400071...               False   \n",
      "\n",
      "                                      ip_raw tcp_tcp_srcport_raw  \\\n",
      "87  450005dc3f3240007106c90b6272cd66c0960b6f                0868   \n",
      "\n",
      "   tcp_tcp_dstport_raw  label  \n",
      "87                0438    0.0  \n",
      "MSE = 0.1640564\n",
      "                                             frame_raw linux_cooked_header  \\\n",
      "222  003048624e4a0008e23b56010800450005dc3f2b400071...               False   \n",
      "\n",
      "                                       ip_raw tcp_tcp_srcport_raw  \\\n",
      "222  450005dc3f2b40007106c9126272cd66c0960b6f                0868   \n",
      "\n",
      "    tcp_tcp_dstport_raw  label  \n",
      "222                0438    0.0  \n",
      "MSE = 0.1640331\n",
      "                                             frame_raw linux_cooked_header  \\\n",
      "178  003048624e4a0008e23b56010800450005dc402d400071...               False   \n",
      "\n",
      "                                       ip_raw tcp_tcp_srcport_raw  \\\n",
      "178  450005dc402d40007106c8106272cd66c0960b6f                0868   \n",
      "\n",
      "    tcp_tcp_dstport_raw  label  \n",
      "178                0438    0.0  \n",
      "MSE = 0.1636191\n",
      "                                             frame_raw linux_cooked_header  \\\n",
      "154  003048624e4a0008e23b560108004500003c3de4400071...               False   \n",
      "\n",
      "                                       ip_raw tcp_tcp_srcport_raw  \\\n",
      "154  4500003c3de440007106cff96272cd66c0960b6f                0868   \n",
      "\n",
      "    tcp_tcp_dstport_raw  label  \n",
      "154                0438    0.0  \n",
      "MSE = 0.16323353\n",
      "                                             frame_raw linux_cooked_header  \\\n",
      "213  003048624e4a0008e23b56010800450000493d6e400071...               False   \n",
      "\n",
      "                                       ip_raw tcp_tcp_srcport_raw  \\\n",
      "213  450000493d6e40007106d0626272cd66c0960b6f                22b4   \n",
      "\n",
      "    tcp_tcp_dstport_raw  label  \n",
      "213                8dc8    0.0  \n",
      "Generated ad_test.pcap\n"
     ]
    }
   ],
   "source": [
    "# Generate pcap\n",
    "# open TMP file used by text2pcap\n",
    "infile = 'ad_test'\n",
    "file = infile + '.tmp'\n",
    "f = open(file, 'w')\n",
    "\n",
    "\n",
    "print(type(test_batch_mse))\n",
    "\n",
    "# get top N of mse\n",
    "N = 30\n",
    "top_N = test_batch_mse.argsort()[-N:][::-1]\n",
    "print(top_N)\n",
    "\n",
    "for j in top_N:\n",
    "    #print mse\n",
    "    print(\"MSE = \" + str(test_batch_mse[j]))\n",
    "    print(str(df.iloc[[j]]))\n",
    "    # pcap '06x'\n",
    "    df_to_pcap(j, df, file)\n",
    "\n",
    "    \n",
    "# pcap\n",
    "f.close()\n",
    "to_pcap_file(infile + '.tmp', infile + '.pcap')\n",
    "os.remove(infile + '.tmp')\n",
    "print(\"Generated \" + infile + \".pcap\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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