tshark_post_process.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('tshark.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_network_out = df[df['src'] == '172.17.20.237']\n",
    "df_network_in = df[df['src'] == '172.17.20.229']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "network_out = df_network_out['timestamp'].to_numpy(int)\n",
    "network_out_packet_sizes = df_network_out['val'].to_numpy(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "network_out_next = np.roll(network_out, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "diffs_micro = (network_out_next - network_out) / 1000\n",
    "diffs_micro_tmp = diffs_micro[1:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = list(range(0, 1000, 50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "plt.hist(diffs_micro_tmp, bins=bins)\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('Packet arriving time diffs')\n",
    "plt.xticks(bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "TIME_DELTA = 500\n",
    "MICRO_TO_NANO = 1000\n",
    "TIME_DELTA_NANO = TIME_DELTA * MICRO_TO_NANO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "flows = []\n",
    "diffs_high = np.argwhere(diffs_micro > TIME_DELTA).squeeze()\n",
    "diffs_high_rolled = np.roll(diffs_high, -1).squeeze()\n",
    "flows = list(zip(diffs_high, diffs_high_rolled))[:-1]\n",
    "flows_times = [(network_out[f[0]], network_out[f[1]]) for f in flows]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "flows_sizes = [np.sum(network_out_packet_sizes[slice(*f)]) for f in flows]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Flow Sizes')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.arange(0, 15000, 15)\n",
    "plt.figure(figsize=(50, 10))\n",
    "plt.hist(flows_sizes, bins=bins)\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('Flow Sizes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "SIZE_THRESHOLD = 75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "flows_times = [network_out[f[0]] for f in flows]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame({'time': np.array(flows_times), 'flow_size': np.array(flows_sizes)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train, test = train_test_split(data, test_size=0.1)\n",
    "test, validation = train_test_split(test, test_size=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "prefix = 'tshark/data/node-1/train'\n",
    "train.to_csv(f'nsdi19/{prefix}/flow.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "prefix = 'tshark/data/node-1/test'\n",
    "test.to_csv(f'nsdi19/{prefix}/flow.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "prefix = 'tshark/data/node-1/validation'\n",
    "validation.to_csv(f'nsdi19/{prefix}/flow.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "2e27e19ca6a05df96a2b941e6f80a39c60cf0d4b0a98b67408d6945db8a91ff3"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}