05_1_machine_learning_implementation_for_attack_files .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File              ML algorithm       accuracy        Precision       Recall          F1-score        Time           \n",
      "Bot               Naive Bayes        0.55            0.82            0.55            0.54            0.0031         \n",
      "Bot               QDA                0.67            0.85            0.67            0.68            0.0225         \n",
      "Bot               Random Forest      0.96            0.96            0.96            0.96            0.0281         \n",
      "Bot               ID3                0.96            0.96            0.96            0.96            0.0078         \n",
      "Bot               AdaBoost           0.97            0.97            0.97            0.97            0.1813         \n",
      "Bot               MLP                0.68            0.65            0.68            0.64            0.1453         \n",
      "Bot               Nearest Neighbors  0.95            0.96            0.95            0.95            0.0176         \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "File              ML algorithm       accuracy        Precision       Recall          F1-score        Time           \n",
      "DDoS              Naive Bayes        0.78            0.77            0.78            0.77            0.0491         \n",
      "DDoS              QDA                0.41            0.8             0.41            0.34            0.0541         \n",
      "DDoS              Random Forest      0.96            0.96            0.96            0.96            0.4187         \n",
      "DDoS              ID3                0.96            0.97            0.96            0.96            0.1891         \n",
      "DDoS              AdaBoost           0.96            0.96            0.96            0.96            2.6724         \n",
      "DDoS              MLP                0.78            0.8             0.78            0.76            3.4024         \n",
      "DDoS              Nearest Neighbors  0.92            0.93            0.92            0.92            1.3234         \n"
     ]
    },
    {
     "data": {
      "image/png": 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jXI1kM0CSqqoXtA9JWvycF+oj81Z9Ze6qz+bcs53kcUm2Atu65UcleUvzyKRFzHmhPjJv1VfmrvpslMNI3gQ8DbgJoKquBZ7QMiipB5wX6iPzVn1l7qq3Rim2qaodM5ruaBCL1CvOC/WReau+MnfVV6MU2zuSnARUkkOTvILua5y5JDktyXVJtic5Zw99fi7J1iRbkrxvH2KXJmne80KaIPNWfWXuqrdGKbZfDLwEOBLYCfxUt7xXSZYAFwNPZ3DHp3VJjpvRZxVwLvD4qvq/GZxtLPXBvOaFNGHmrfrK3FVvjXI1ku8Az53H2CcC26vqeoAkVwCnA1uH+rwIuLiqbum29e15bEcau/2YF9LEmLfqK3NXfTZnsZ1kJXAWsGK4f1WtmWPVI4Hh46t2Ao+Z0efHu218BlgCnF9Vfz1n1NKE7ce8kCbGvFVfmbvqs1HuIPlB4FLgQ8Cd+zB2ZmmrWba/CjgFWA78bZLjq+rf7zJQciZwJsDRRx+9DyFIzYw0L8xdLTLmrfrK3FVvjXS79qq6aB5j7wSOGlpeDtw4S5/PVdUPgK8luY5B8b15uFNVXQJcArB69eqZBbs0CSPNC3NXi4x5q74yd9VboxTbFyZ5FfAR4Lbpxqr64hzrbQZWdV/9fANYC/zCjD4fBNYBlyVZyuCwkutHjF2apPnOC2mSzFv1lbmr3hql2P5J4BeBJ/FfX91Ut7xHVbU7yXrgagbHY7+rqrYk2QBMVdXG7rmndneFugP4zaq6aX4vRRqrec0LacLMW/WVuaveGqXYfibwsKq6fV8Hr6pNwKYZbecNPS7g5d2P1CfznhfSBJm36itzV701ynW2rwUe0DoQqWecF+oj81Z9Ze6qt0bZs/0Q4J+SbOaux0l5uR0dzJwX6iPzVn1l7qq3Rim2X9U8Cql/nBfqI/NWfWXuqrdGuYPkp8YRiNQnzgv1kXmrvjJ31WejHLMtSZIkaR4stiVJkqRG9lhsJ/E+p9IMzgv1kXmrvjJ3dSDY257tD04/SPKBMcQi9YHzQn1k3qqvzF313t6K7Qw9fljrQKSecF6oj8xb9ZW5q97bW7Fde3gsHcycF+oj81Z9Ze6q9/Z26b9HJfkug78q7909pluuqjq8eXTS4uO8UB+Zt+orc1e9t8diu6qWjDMQqQ+cF+oj81Z9Ze7qQOCl/yRJkqRGmhbbSU5Lcl2S7UnOmeX5M5LsSvIP3c+vtIxHkiRJGqc5b9c+X0mWABcDTwF2ApuTbKyqrTO6vr+q1reKQ5IkSZqUlnu2TwS2V9X1VXU7cAVwesPtSZIkSYtKy2L7SGDH0PLOrm2mZyf5cpKrkhw120BJzkwylWRq165dLWKVmjB31UfmrfrK3NVi1LLYzixtM6+R+SFgRVU9Evgo8O7ZBqqqS6pqdVWtXrZs2QKHKbVj7qqPzFv1lbmrxahlsb0TGN5TvRy4cbhDVd1UVbd1i+8AHt0wHkmSJGmsWhbbm4FVSVYmORRYC2wc7pDkoUOLa4BtDeORJEmSxqrZ1UiqaneS9cDVwBLgXVW1JckGYKqqNgJnJ1kD7AZuBs5oFY8kSZI0bs2KbYCq2gRsmtF23tDjc4FzW8YgSZIkTYp3kJQkSZIasdiWJEmSGrHYliRJkhqx2JYkSZIasdiWJEmSGrHYliRJkhqx2JYkSZIasdiWJEmSGrHYliRJkhqx2JYkSZIasdiWJEmSGrHYliRJkhqx2JYkSZIaaVpsJzktyXVJtic5Zy/9npOkkqxuGY8kSZI0Ts2K7SRLgIuBpwPHAeuSHDdLv/sBZwN/3yoWSZIkaRJa7tk+EdheVddX1e3AFcDps/T7A+CPge83jEWSJEkau5bF9pHAjqHlnV3bDyU5ATiqqj68t4GSnJlkKsnUrl27Fj5SqRFzV31k3qqvzF0tRi2L7czSVj98MrkH8EbgN+YaqKouqarVVbV62bJlCxii1Ja5qz4yb9VX5q4Wo5bF9k7gqKHl5cCNQ8v3A44HPpnkX4HHAhs9SVKSJEkHipbF9mZgVZKVSQ4F1gIbp5+sqluramlVraiqFcDngDVVNdUwJkmSJGlsmhXbVbUbWA9cDWwDrqyqLUk2JFnTaruSJEnSYnFIy8GrahOwaUbbeXvoe0rLWCRJkqRx8w6SkiRJUiMW25IkSVIjFtuSJElSIxbbkiRJUiMW25IkSVIjFtuSJElSIxbbkiRJUiNNr7MtSZx//0bj3tpmXEmSFpDFtqS2LIolSQcxDyORJEmSGrHYliRJkhqx2JYkSZIasdiWJEmSGrHYliRJkhpJVU06hn2SZBdww6TjWOSWAt+ZdBCL3DFVtWycGzR3R2Lu7p15uziZt3Mzdxcnc3du+527vSu2NbckU1W1etJxSPvK3FUfmbfqK3N3PDyMRJIkSWrEYluSJElqxGL7wHTJpAOQ5sncVR+Zt+orc3cMPGZbkiRJasQ925IkSVIjFtsHkCTvSvLtJP846VikUZm36itzV31l7o6XxfaB5TLgtEkHIe2jyzBv1U+XYe6qny7D3B0bi+0DSFV9Grh50nFI+8K8VV+Zu+orc3e8LLYlSZKkRiy2JUmSpEYstiVJkqRGLLYlSZKkRiy2DyBJLgc+Czwiyc4kL5x0TNJczFv1lbmrvjJ3x8s7SEqSJEmNuGdbkiRJasRiW5IkSWrEYluSJElqxGK7oSRnJPlwkguTvCvJw5KsSPKlru3PkjxlD+uuTfK+JG9OcvoI23pwkm8lWT7Lc1ftYZ3Pd+N/IMmj9/0V7nlsHfjGld9JTknyzSSHdct/l2R9175+lnjeneS3FvbValzGnFc7ktw3yWFJLtvHOOf8/Tnb52OSjyV5Wff4tUlWzNJndZKfH2XM7v36mYWKWdLCO2TSARwE3lZVH07yIOCNwHnAx6rqFUkCbEzy0a79COD+wK8CzwF+uaq+O+J2ng+8DDgDeHWSJwBnAv8MkOQ+wO8ADwCurap3AF+vqvVJngSclOQrwNuBW4Gbq2pDklfNiOv3gcOAHcDngeOS/G5VvXo/3iP117jy+1PAs5JsBb4+QjwfmO8L0qIwrrz6OIPPzddPNyR5JbAMuB/wUuB04KeAw4GXAK8EHgh8KclO4H8A9wY+ACwFTgH+A7iIwefj+cA7q2pnt4lbgBOSPGBomycw+Ow+hMEVInYCy5I8GPhT4KvAU6rqscDSJK/uYjqzG2JdklOBb1fVHyW5CNgN3LOqzkryYeDvgC8kOaMb/5qq+ssR3ydJ+8G/csekqm4C7jmjrYDrgaOAFVX1UuCTwFMZfKBfkOSyJP99hE08pqrex+BDPMCvAb8CvG16cww+yG8Gfq5rOyrJmxgU4R/rtvvpLo5jkhw+S1zLGHxov7OqrgG2WmhrDPn9t8DJwHOBy/fS70VJ3gG8e59fhBadMeTVZmAF8BCAJMcCTwD+HbgdOLbrtxs4EjihW35/Vf0ZcHbX99+AE4HlwJeBN1XVDQw+H88fKrSn/THw20PLL2dQhO8a2gbAzwNvqarfA27r2n5QVb8LvBN4Ytf2kar6DeC/JXkkcEtVvRz4TpLjGfyu/2Pg0wx2lvwV8P+O8P5IWgDu2R6Tbg/N7TPaAqxksCd5WgFU1XXAWUnuyWCPyd926/wE8GLgU1X1F13b4xkUzm8DfhT4aeBOBr8gpj+gn8Hgg/89ST7Rte2oqpcmOQS4DHj/9PZnMd3+a8DjgSu6Mb12pJrm95AbgGO68Y7aQyjvqKoP79+r0WIxprx6DYMdDjAoSrdU1flD2/vdqlrTfct3n6751qH+r66q3UP9HwW8LsnvsYfPx6r6x25nxhFd06HAhVV1SzfGKdPDDY0x/e/0tr/P4JtKZvSpWdb5Xhfj7m7P9lOBN3fviaTGLLbbe3F3fOHhwPld25OTXNi1/WlV3ZrkhiRvYPD15K8m+W0GBcVhDH5pAFBV/8Tgq81hZwDPrqqdSZYy+Nr17Qz2ZHyz6/Ml4LVJHgos6dqOTvJm4L7AR7qftyX5SQaF+HdnxgVcwOAXzPXdGD9I8gfdnhcdfMaR39Nex6D4GN5j+fPdnrtbgW0L97I0YWPLq6r6lyTfA+5VVVuS3JnkTxgcGvKHwL9lcA7AiQwOZxp2EfDOJDcDU8CPAKsY7Oy4CdiS5PUMCukdM9b9Q+CfGBT6fwT8aZJvAf8KfKXrcwVwUZKfZpD7e3Jakp8CpqrqK0l+NcnrgHt3hT0A3ef/7wB3AFv2Mp6kBeRNbSRJWoSSLGFwXPrhwFer6q0TDknSPFhsS5IkSY14gqQkSZLUiMW2JEmS1IjFtiRJktSIxbYkSZLUSO8u/bd06dJasWLFpMNQz33hC1/4TlUtG+c2zV3tL/NWfWXuqq8WInd7V2yvWLGCqampSYehnktyw7i3ae5qf5m36itzV321ELnrYSSSJElSIxbbkiRJUiMW25IkSVIjFtuSJElSI707QfKgdf79G459a7uxJUmSDmIW231hQSxJktQ7HkYiSZIkNWKxLUmSJDVisS1JkiQ1YrEtSZIkNWKxLUmSJDVisS1JkiQ1YrEtSZIkNeJ1tiVJmskbiUlaIE2L7SSnARcCS4B3VtVrZzx/DPAuYBlwM/C8qtrZMiZJkuZkQSxpgTQ7jCTJEuBi4OnAccC6JMfN6PZ64D1V9UhgA/CaVvFIkiRJ49bymO0Tge1VdX1V3Q5cAZw+o89xwMe6x5+Y5XlJkiSpt1oW20cCO4aWd3Ztw64Fnt09fiZwvyQPmjlQkjOTTCWZ2rVrV5NgpRbMXfWReau+Mne1GLUstjNLW81YfgXwxCRfAp4IfAPYfbeVqi6pqtVVtXrZsmULH6nUiLmrPjJv1VfmrhajlidI7gSOGlpeDtw43KGqbgSeBZDkR4BnV5VnpUiSJOmA0HLP9mZgVZKVSQ4F1gIbhzskWZpkOoZzGVyZRJIkSTogNCu2q2o3sB64GtgGXFlVW5JsSLKm63YKcF2SrwIPAS5oFY8kSZI0bk2vs11Vm4BNM9rOG3p8FXBVyxgkSZKkSfF27ZIkSVIjFtuSJElSIxbbkiRJUiMW25IkSVIjFtuSJElSIxbbkiRJUiMW25IkSVIjFtuSJElSIxbbkiRJUiMW25IkSVIjFtuSJElSIxbbkiRJUiOHTDoASQefJPvUv6oaRSJJUltN92wnOS3JdUm2JzlnluePTvKJJF9K8uUkz2gZj6TFoapm/dnTc5Ik9VWzYjvJEuBi4OnAccC6JMfN6Pa7wJVVdQKwFnhLq3gkSZKkcWu5Z/tEYHtVXV9VtwNXAKfP6FPA4d3j+wM3NoxHkiRJGquWxfaRwI6h5Z1d27Dzgecl2QlsAs6abaAkZyaZSjK1a9euFrFKTZi76iPzVn1l7moxallsz3YG1MyDL9cBl1XVcuAZwHuT3C2mqrqkqlZX1eply5Y1CFVqw9yFBz7wgSQZ6QcYqd8DH/jACb+qA5t5q74yd7UYtbwayU7gqKHl5dz9MJEXAqcBVNVnkxwGLAW+3TAuSWN0yy23LPhJjvt6NRNJkial5Z7tzcCqJCuTHMrgBMiNM/p8HXgyQJJjgcMAv/eRJEnSAaFZsV1Vu4H1wNXANgZXHdmSZEOSNV233wBelORa4HLgjPI6X5IkSTpANL2pTVVtYnDi43DbeUOPtwKPbxmDpMmqVx0O599/4ceUJKkHvIOkpKby+99tcsx2nb+gQ0qS1ITFtqTmFvqExiOOOGJBx5MkqZWRi+0k962q/2wZjNQ3zou57cte7STenn0MzFtJGp85T5BMclKSrQxOciTJo5J4W3Ud1JwX6iPzVpLGb5SrkbwReBpwE0BVXQs8oWVQUg84L9RH5q0kjdlIh5FU1Y4Zx1ze0SYcqT+cF/O3t2O4Z3vOQ0sWjnkrSeM1SrG9I8lJQHU3pzmb7itI6SDmvNgPFs8TY95K0piNchjJi4GXAEcyuAX7T3XL0sHMeaE+Mm8lacz2umc7yRLgF6vquWOKR1r0nBfqI/NWkiZjr3u2q+oO4PQxxSL1gvNCfWTeStJkjHLM9meSvBl4P/DD67JW1RebRSUtfs4L9ZF5K0ljNkqxfVL374ahtgKetPDhSL3hvFAfmbeSNGZzFttVdep8B09yGnAhsAQ5MmA4AAAOsklEQVR4Z1W9dsbzbwSmx78P8OCqesB8tyeNy/7MC2lSzFtJGr85i+0k583WXlUbZmsfWm8JcDHwFAZnvW9OsrGqtg6N8bKh/mcBJ4wYtzRR850X0iSZt5I0fqNc+u8/h37uAJ4OrBhhvROB7VV1fVXdDlzB3k/OWQdcPsK40mIw33khTZJ5K0ljNsphJG8YXk7yemDjCGMfCewYWt4JPGa2jkmOAVYCHx9hXGni9mNeSBNj3krS+I2yZ3um+wAPG6HfbPdj3tNt49YCV3WXprr7QMmZSaaSTO3atWvEMKWxmnVemLta5MxbHVDMXS1Goxyz/RX+q0heAizjrmey78lO4Kih5eXAjXvou5a93MWsqi4BLgFYvXq193nWxI06L8xdLSbmrQ505q4Wo1Eu/fczQ493A9+qqt0jrLcZWJVkJfANBgX1L8zslOQRwBHAZ0cYU1os5jsvpEkybyVpzEY5jOQQ4JtVdQOwCvj1JHNenq/7AF8PXA1sA66sqi1JNiRZM9R1HXBFVfkXqPpkXvNCmjDzVpLGbJRi+wPAHUkeDlzK4ETG940yeFVtqqofr6ofq6oLurbzqmrjUJ/zq+qcecQuTdK854U0QeatJI3ZKMX2nd1e6mcBb+qujf3QtmFJi57zQn1k3krSmI1SbP8gyTrgl4APd233bBeS1AvOC/WReStJYzZKsf0C4HHABVX1te6Exz9vG5a06Dkv1EfmrSSN2Sg3tdkKnD20/DXgtS2DkhY754X6yLyVpPEb5Trbq4DXAMcBh023V9UoN7aRDkjOC/WReStJ4zfKYSR/BryVwTVZTwXeA7y3ZVBSDzgv1EfmrSSN2SjF9r2r6mNAquqGqjofeFLbsKRFz3mhPjJvJWnMRrmD5PeT3AP45yTrGdwN8sFtw5IWPeeF+si8laQxG2XP9kuB+zA4qebRwPOA57cMSuoB54X6yLyVpDEb5WokmwGSVFW9oH1I0uLnvFAfmbeSNH5z7tlO8rgkW4Ft3fKjkryleWTSIua8UB+Zt5I0fqMcRvIm4GnATQBVdS3whJZBST3gvFAfmbeSNGajFNtU1Y4ZTXc0iEXqFeeF+si8laTxGqXY3pHkJKCSHJrkFXRfQc4lyWlJrkuyPck5e+jzc0m2JtmS5H37ELs0SfOeF9IEmbeSNGajXPrvxcCFwJHATuAjwEvmWinJEuBi4CndepuTbOxuFzzdZxVwLvD4qroliZegUl/Ma15IE2beStKYjXI1ku8Az53H2CcC26vqeoAkVwCnA1uH+rwIuLiqbum29e15bEcau/2YF9LEmLeSNH5zFttJVgJnASuG+1fVmjlWPRIYPjZwJ/CYGX1+vNvGZ4AlwPlV9ddzRi1N2H7MC2lizFtJGr9RDiP5IHAp8CHgzn0YO7O01SzbXwWcAiwH/jbJ8VX173cZKDkTOBPg6KOP3ocQpGZGmhfmrhYZ81YHNHNXi9EoJ0h+v6ouqqpPVNWnpn9GWG8ncNTQ8nLgxln6/GVV/aCqvgZcx6D4vouquqSqVlfV6mXLlo2waam5keaFuTuayy+/nOOPP54lS5Zw/PHHc/nll086pAOVeasDmrmrxWiUPdsXJnkVgxNpbpturKovzrHeZmBV97XlN4C1wC/M6PNBYB1wWZKlDA4ruX7E2KVJmu+80AyXX345r3zlK7n00ks5+eSTueaaa3jhC18IwLp16yYc3QHHvJWkMRul2P5J4BeBJ/FfXztWt7xHVbU7yXrgagbHY7+rqrYk2QBMVdXG7rmndnc0uwP4zaq6aX4vRRqrec0L3d0FF1zApZdeyqmnngrAqaeeyqWXXspZZ51lsb3wzFtJGrNRiu1nAg+rqtv3dfCq2gRsmtF23tDjAl7e/Uh9Mu95obvatm0bJ5988l3aTj75ZLZt8/LPDZi3kjRmoxyzfS3wgNaBSD3jvFggxx57LNdcc81d2q655hqOPfbYCUV0QDNvJWnMRtmz/RDgn5Js5q7H+HmpKB3MnBcL5JWvfCUvfOEL73bM9gUXXDDp0A5E5q0kjdkoxfarmkch9Y/zYoFMH5d91llnsW3bNo499lguuOACj9duw7yVpDEb5Q6So1zmTzqoOC8W1rp16yyux8C8laTxG+WYbUmSJEnzYLEtSZIkNbLHYjuJ9zmVZnBeqI/MW0manL3t2f7g9IMkHxhDLFIfOC/UR+atJE3I3ortDD1+WOtApJ5wXqiPzFtJmpC9Fdu1h8fSwcx5oT4ybyVpQvZ26b9HJfkugz0i9+4e0y1XVR3ePDpp8XFeqI/MW0makD0W21W1ZJyBSH3gvFAfmbeSNDle+k+SJElqpGmxneS0JNcl2Z7knFmePyPJriT/0P38Sst4JEmSpHGa83bt85VkCXAx8BRgJ7A5ycaq2jqj6/uran2rOCRJkqRJabln+0Rge1VdX1W3A1cApzfcniRJkrSotCy2jwR2DC3v7NpmenaSLye5KslRsw2U5MwkU0mmdu3a1SJWqQlzV31k3qqvzF0tRi2L7czSNvP6rh8CVlTVI4GPAu+ebaCquqSqVlfV6mXLli1wmFI75q76yLxVX5m7WoxaFts7geE91cuBG4c7VNVNVXVbt/gO4NEN45EkSZLGqmWxvRlYlWRlkkOBtcDG4Q5JHjq0uAbY1jAeSZIkaayaXY2kqnYnWQ9cDSwB3lVVW5JsAKaqaiNwdpI1wG7gZuCMVvFIkiRJ49as2Aaoqk3Aphlt5w09Phc4t2UMkiRJ0qR4B0lJkiSpEYttSZIkqRGLbUmSJKkRi21JkiSpEYttSZIkqRGLbUmSJKkRi21JkiSpEYttSZIkqRGLbUmSJKkRi21JkiSpEYttSZIkqRGLbUmSJKkRi21JkiSpkabFdpLTklyXZHuSc/bS7zlJKsnqlvFIkiRJ49Ss2E6yBLgYeDpwHLAuyXGz9LsfcDbw961ikSRJkiah5Z7tE4HtVXV9Vd0OXAGcPku/PwD+GPh+w1gkSZKksWtZbB8J7Bha3tm1/VCSE4CjqurDexsoyZlJppJM7dq1a+EjlRoxd9VH5q36ytzVYtSy2M4sbfXDJ5N7AG8EfmOugarqkqpaXVWrly1btoAhSm2Zu+oj81Z9Ze5qMWpZbO8EjhpaXg7cOLR8P+B44JNJ/hV4LLDRkyQlSZJ0oGhZbG8GViVZmeRQYC2wcfrJqrq1qpZW1YqqWgF8DlhTVVMNY5IkSZLGplmxXVW7gfXA1cA24Mqq2pJkQ5I1rbYrSZIkLRaHtBy8qjYBm2a0nbeHvqe0jEWSJEkaN+8gKUmSJDVisS1JkiQ1YrEtSZIkNWKxLUmSJDVisS1JkiQ1YrEtSZIkNWKxLUmSJDVisS1JkiQ1YrEtSZIkNWKxLUmSJDVisS1JkiQ1YrEtSZIkNWKxLUmSJDVisS1JkiQ1kqqadAz7JMku4IZJx7HILQW+M+kgFrljqmrZODdo7o7E3N0783ZxMm/nNonc/Q/gunFucwSLLVeMZ26PqKr77c8AhyxUJOMy7snaR0mmqmr1pOPQXZm7czN3Fx/zdm7m7aJ13WL7f1lsuWI8c0sytb9jeBiJJEmS1IjFtiRJktSIxfaB6ZJJByDNk7mrPjJvF6fF+P+y2GIynrntd0y9O0FSkiRJ6gv3bEuSJEmNWGwfQJK8K8m3k/zjpGORRmXeqq/M3clJclqS65JsT3LOLM/fK8n7u+f/PsmKoefO7dqvS/K0McXz8iRbk3w5yceSHDP03B1J/qH72TimeM5Ismtou78y9Nzzk/xz9/P8hYhnxJjeOBTPV5P8+9BzLd6jvc7fDFzUxfvlJP/P0HP79B55GMkBJMkTgP8DvKeqjp90PNIozFv1lbk7GUmWAF8FngLsBDYD66pq61CfXwceWVUvTrIWeGZV/XyS44DLgROB/wv4KPDjVXVH43hOBf6+qr6X5NeAU6rq57vn/k9V/ch8tz/PeM4AVlfV+hnrPhCYAlYDBXwBeHRV3dI6phn9zwJOqKpf7pYX9D3qxtzr/E3yDOAs4BnAY4ALq+ox83mP3LN9AKmqTwM3TzoOaV+Yt+orc3diTgS2V9X1VXU7cAVw+ow+pwPv7h5fBTw5Sbr2K6rqtqr6GrC9G69pPFX1iar6Xrf4OWD5fm5zv+LZi6cBf1NVN3fF498Ap00gpnUM/ihqZoT5ezqDQryq6nPAA5I8lHm8RxbbkiSpT44Edgwt7+zaZu1TVbuBW4EHjbhui3iGvRD4q6Hlw5JMJflckv+5n7HsSzzP7g6PuCrJUfu4bquY6A6xWQl8fKh5od+jUewp5n1+j3p3B0lJknRQyyxtM4+J3VOfUdZtEc+gY/I8BocfPHGo+eiqujHJw4CPJ/lKVf1L43g+BFxeVbcleTGDbwGeNOK6rWKatha4asahPQv9Ho1iwXLIPduSJKlPdgJHDS0vB27cU58khwD3Z3DIwCjrtoiHJD8NvBJYU1W3TbdX1Y3dv9cDnwROaB1PVd00FMM7gEePum6rmIasZcYhJA3eo1HsKeZ9fo8stiVJUp9sBlYlWZnkUAbF2cwrVGwEpq8S8Rzg4zW4IsRGYG13tZKVwCrg863jSXIC8HYGhfa3h9qPSHKv7vFS4PHArCcNLnA8Dx1aXANs6x5fDTy1i+sI4Kld2/4a5f+MJI8AjgA+O9TW4j0axUbgl7qrkjwWuLWq/o15vEceRnIASXI5cAqwNMlO4FVVdelko5L2zrxVX5m7k1FVu5OsZ1DgLAHeVVVbkmwApqpqI3Ap8N4k2xns0V7brbslyZUMirXdwEv250ok+xDP64AfAf734DxNvl5Va4BjgbcnuZPBDtDX7ukKHQscz9lJ1jB4D24GzujWvTnJHzAojgE2VNV+nwQ8YkwwODHyirrrpfIW/D2C2ecvcM8u3rcBmxhciWQ78D3gBd1z+/weeek/SZIkqREPI5EkSZIasdiWJEmSGrHYliRJkhqx2JYkSZIasdiWJEmSGrHYliRJkhqx2JYkSZIasdiWJEmSGvn/AZpc4YtTrjfpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "File              ML algorithm       accuracy        Precision       Recall          F1-score        Time           \n",
      "DoS GoldenEye     Naive Bayes        0.83            0.82            0.83            0.81            0.0094         \n",
      "DoS GoldenEye     QDA                0.7             0.84            0.7             0.71            0.0141         \n",
      "DoS GoldenEye     Random Forest      0.99            0.99            0.99            0.99            0.0884         \n",
      "DoS GoldenEye     ID3                0.99            0.99            0.99            0.99            0.0345         \n",
      "DoS GoldenEye     AdaBoost           0.99            0.99            0.99            0.99            0.5689         \n",
      "DoS GoldenEye     MLP                0.65            0.75            0.65            0.64            0.6825         \n",
      "DoS GoldenEye     Nearest Neighbors  0.98            0.98            0.98            0.98            0.1172         \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "File              ML algorithm       accuracy        Precision       Recall          F1-score        Time           \n",
      "DoS Hulk          Naive Bayes        0.34            0.8             0.34            0.23            0.3127         \n",
      "DoS Hulk          QDA                0.41            0.81            0.41            0.36            0.353          \n",
      "DoS Hulk          Random Forest      0.93            0.94            0.93            0.93            3.4215         \n",
      "DoS Hulk          ID3                0.96            0.96            0.96            0.96            0.865          \n",
      "DoS Hulk          AdaBoost           0.96            0.96            0.96            0.96            19.8474        \n",
      "DoS Hulk          MLP                0.95            0.95            0.95            0.95            14.8005        \n",
      "DoS Hulk          Nearest Neighbors  0.96            0.96            0.96            0.96            219.6141       \n"
     ]
    },
    {
     "data": {
      "image/png": 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xm9pH2tE5LtRH5q36ytxVn42yZvsKYAXwUeCetuFIveG4UB+Zt+orc1e9NdLl2qvq3OaRSP3iuFAfmbfqK3NXvTVKsf32JK8FPgH8bGNjVf1Ts6ik6ee4UB+Zt+orc1e9NUqx/SjgBcBT+MVXN9VtSzsrx4X6yLxVX5m76q1Riu1nAAdU1R2tg5F6xHGhPjJv1VfmrnprlPNs3wA8uHUgUs84LtRH5q36ytxVb40ys/2rwD8nuY57r5PydDvamTku1EfmrfrK3FVvjVJsv7Z5FFL/OC7UR+at+srcVW+NcgXJvx9HIFKfOC7UR+at+srcVZ+NsmZbkiRJ0jaw2JYkSZIa2WSxneTh4wxE6gPHhfrIvFVfmbvaEWxuZvuKjTeSXD6GWKQ+cFyoj8xb9ZW5q97bXLGdodsHtA5E6gnHhfrIvFVfmbvqvc0V27WJ29LOzHGhPjJv1Vfmrnpvc6f+e3SSHzD4VHnf7jbddlXVHs2jk6aP40J9ZN6qr8xd9d4mi+2qmjfOQKQ+cFyoj8xb9ZW5qx2Bp/6TJEmSGrHYliRJkhqx2JYkSZIasdiWJEmSGmlabCc5JslNSdYmOX0z/Z6dpJIsbhmPJEmSNE7Niu0k84DzgWOBRcCSJItm6fdA4FTg861ikSRJkiah5cz2ocDaqrq5qu4ALgFOmKXf64E/A37aMBZJkiRp7FoW23sD64a213dtP5fkEGDfqrqqYRySJEnSRLQstjNL288vtZpkF+CtwKu2eKDkpCSrkqzasGHDHIYotWXuqo/MW/WVuatp1LLYXg/sO7S9D3DL0PYDgYOBv0vyr8DjgStn+5FkVV1YVYuravGCBQsahizNLXNXfWTeqq/MXU2jlsX2dcCBSfZPshtwInDlxjur6vaq2quqFlbVQuBzwPFVtaphTJIkSdLYNCu2q+ouYBlwNbAGuLSqbkxyVpLjWz2uJEmSNC3mtzx4Va0EVs5oO3MTfY9oGYskSZI0bl5BUpIkSWrEYluSJElqpOkyEkli+YMaHff2NseVJGkOWWxLasuiWJK0E3MZiSRJktSIxbYkSZLUiMW2JEmS1IjFtiRJktSIxbYkSZLUiMW2JEmS1IjFtiRJktSIxbYkSZLUiMW2JEmS1IjFtiRJktSIxbYkSZLUiMW2JEmS1IjFtiRJktSIxbYkSZLUiMW2JEmS1IjFtiRJktSIxbYkSZLUiMW2JEmS1IjFtiRJktSIxbYkSZLUiMW2JEmS1IjFtiRJktTI/EkHoBEtf1DDY9/e7tiSJEk7MYvtvrAgliRJ6p2my0iSHJPkpiRrk5w+y/2nJVmd5MtJPplkv5bxSJIkSePUrNhOMg84HzgWWAQsSbJoRrcvAour6reAy4A/axWPJEmSNG4tZ7YPBdZW1c1VdQdwCXDCcIequqaqftxtfg7Yp2E8kiRJ0li1LLb3BtYNba/v2jZlKfCxhvFIkiRJY9Wy2M4sbTVrx+T5wGLgnE3cf1KSVUlWbdiwYQ5DlNoyd9VH5q36ytzVNErVrPXv9h84eQKwvKqO7rZfA1BVb5zR7yjgHcCTq+q7Ixx3A/DNuY94h7IX8L1JBzHl9quqBeN8QHN3JObu5pm308m83TJzdzqZu1u23bnbstieD3wNeCrwbeA64LlVdeNQn0MY/DDymKr6epNAdkJJVlXV4knHIW0tc1d9ZN6qr8zd8Wi2jKSq7gKWAVcDa4BLq+rGJGclOb7rdg7wAOAvk3wpyZWt4pEkSZLGrelFbapqJbByRtuZQ7ePavn4kiRJ0iQ1vaiNJubCSQcgbSNzV31k3qqvzN0xaLZmW5IkSdrZObMtSZIkNWKxvQNJ8r4k303y1UnHIo3KvFVfmbvqK3N3vCy2dywXAcdMOghpK12Eeat+ughzV/10Eebu2Fhs70Cq6h+A2yYdh7Q1zFv1lbmrvjJ3x8tiW5IkSWrEYluSJElqxGJbkiRJasRiW5IkSWrEYnsHkuRi4LPAI5OsT7J00jFJW2Leqq/MXfWVuTteXkFSkiRJasSZbUmSJKkRi21JkiSpEYttSZIkqRGL7TmQ5EVJrkry9iTvS3LAJvqdnWRFkncl+c2h9iOSLOtu757kok3svzzJwRv/bkV8hyT5ZpL7zmhfmOTNm9hnbZLzkvyfJA8f9bFGObb6ZZrzu4vtS93tXbs8P65rP27Gsf8iySVJXrAVT18N9CCnrk+yS5LfTLJ8K5/bFv+/muSyWdr+JckzutuXbGK/45P8zijH3MrnbC0gNTR/0gHsQC6oqquSPBR4a5KXAO8Gbgduq6qzgMXAsVV1zygHTHJZVT07ycnAP89y/+nA16vq8i0c6kXAmcCzgQ8meQ5wFPBD4O4kvw68Ango8PGqugL4UlUtS/JHwKOT/Ah4G/AD4MtV9e4k5wJ3AbtW1SlJLuzuvxG4EzgsybKqOm+U56upNs35fVOSw4BfZfDr+k35n8BNwIeAD44So5qa5pz6CvB84AvdfvOBs4H7APOAVwIvAw4A7qmqV3cF/78CX+n6PwHYAzgXeCqwX/fcPgws6or4N1fVD7vHvAF4bpKPDsX7u8B/Bu4LXA7sCdyT5CDgtQzy+XFVdQzwG0neBBwEnNgd4mVdIf1Z4BJmvL5JPg18FLgmyau6+K/qLuUtaY74aXaOVdWtwK7A7wH/UFWvBPZLsgfwVuCCJO9JsveMXZ+T5ALgHSM+1OuBVVv6n0aS3Rm8Qf9v4Pe75hOr6qUM3nxhUDDfB/gO8Lyu7dFJ3g4sAa7r/r6vql4BHNXNmHy/qk4DvtdtPwS4BrgYuBb4jIX2jmXa8rtzGfAs4GjgE5vp96fABQyKH02JKc6p44Ddu+3fBRYC/wE8ANibQdH9E+AJSX6l6/ee7vjLur7fAQ4Ffg1YBZxbVV8FVlfV8qFCG+Ae4H3ASUNtp3bH+bfuOBu9hEE+v57BawewoapOZ/Ae/Jiu7dKqehnwdGZ/fX9cVW8CbgHuBq4A/nGE10fSVrDYnmPdLM0dQIB7nVexqj5eVScBKxi8WQ77cFWdDJwy1LZxNuf+szzUzQxmMIYf+7Akb0vy5KHmZwEPA85jcD7NRzCYdQb4Wff3BcCVDGb+Hti13VBV/w14IYM39ZnPZ3h7498XdrcvGopdO5ApzG8YFDwA/87m8+5/VdXSqvr0ZvpozKY0p2BQxJ/a3d4F+HRXIL8Y+BHw6Ko6A/gacL+u3+3d3590fV9TVRcyeA+9CXh/V+TOes7dqvoY8Dv84n14F+AN3bHeMBx6d4zh42x87J8ymDyBe79H/9Lru3GfqlrXPddHMfgWVNIcchnJ3Dm5+8pvD2A5g5mCC5I8ClhXVT9IsoLBm/RewDkjHPPbSV4NHA5cP+O+9wOPT3JqVZ0LUFWfAT4zo9+zgN+vqp8k+S1gKXB5krP4xRvyZ4CTgScy+J8ewGOSnMdgtvoi4IsMvup9JnBNVX0lyX9Ncg6DrzhvAt7OoPD5GoPC5zeSnFZV/98Iz1XTbVrze6P/zqCQeOFQ28u7dds3j/IENXZTnVNV9Y9JTgO+BVzdxXYO8GAGy0ju1z3WI2bZ/UPdsrqfAH8N/Hb3HG4Dfgx8p1vy8YYZs9sAbwY+390+F3hvktsYzIxv9B7gfzF4r525/7ATk5wIfITBtz4zX18AuralDGby/3Yzx5O0DbyojSRJPZJkTwYF/0OBT1bVX004JEmbYbEtSZIkNeKabUmSJKkRi21JkiSpEYttSZIkqRGLbUmSJKmR3p36b6+99qqFCxdOOgz13PXXX/+9qlowzsc0d7W9zFv1lbmrvpqL3O1dsb1w4UJWrVq15Y7SZiT55rgf09zV9jJv1VfmrvpqLnLXZSSSJElSIxbbkibu4osv5uCDD2bevHkcfPDBXHzxxZMOSZKkOdG7ZSSSdiwXX3wxZ5xxBitWrODwww/n2muvZenSpQAsWbJkwtFJkrR9nNmWNFFnn302K1as4Mgjj2TXXXflyCOPZMWKFZx99tmTDk2SpO3mzHZfLH9Qw2Pf3u7Y0hasWbOGww8//F5thx9+OGvWrJlQRBK+50qaMxbbfeGbs3ZQBx10EK973eu44oorWLNmDQcddBBPf/rTOeiggyYdmnZmvudKmiMW25La2sIM4VefDfAO3vBsgAcA636xvbl9LYYkST1gsS2prS0UxQcffDBPf/rTf2lm+4orruCrX/3qmIKUJKkNi21JE7VmzRq++MUv8oY3vOHnbXfeeSdvfOMbJxiVJElzw7ORSJqogw46iGuvvfZebddee61rtiVJOwSLbUkTdcYZZ7B06VKuueYa7rzzTq655hqWLl3KGWecMenQJEnabi4jkTRRGy9cc8opp/x8zfbZZ5/tBW0kSTsEi21JE7dkyRKLa0nSDsllJJIkSVIjFtuSJElSIxbbkiRJUiMW25IkSVIjFtuSJElSIxbbkiRJUiMW25IkSVIjFtuSJElSIxbbkiRJUiMW25IkSVIjFtuSJElSIxbbkiRJUiMW25IkSVIjFtuSJElSIxbbkiRJUiMW25IkSVIjFtuSJElSI02L7STHJLkpydokp89y/8OTXJPki0m+nORpLeORJEmSxqlZsZ1kHnA+cCywCFiSZNGMbv8DuLSqDgFOBN7ZKh5JkiRp3FrObB8KrK2qm6vqDuAS4IQZfQrYo7v9IOCWhvFIkiRJYzW/4bH3BtYNba8HHjejz3LgE0lOAe4PHNUwHkmSJGmsWs5sZ5a2mrG9BLioqvYBngZ8MMkvxZTkpCSrkqzasGFDg1ClNsxd9ZF5q74ydzWNWhbb64F9h7b34ZeXiSwFLgWoqs8CuwN7zTxQVV1YVYuravGCBQsahSvNPXNXfWTeqq/MXU2jlsX2dcCBSfZPshuDH0BeOaPPt4CnAiQ5iEGx7UdRSZIk7RCaFdtVdRewDLgaWMPgrCM3JjkryfFdt1cBL01yA3Ax8KKqmrnURJIkSeqllj+QpKpWAitntJ05dHs18MSWMUiSJEmT4hUkJUmSpEYstiVJkqRGLLYlSZKkRiy2JUmSpEYstiVJkqRGLLYlSZKkRiy2JUmSpEYstiVJkqRGRi62k9y/ZSBSHzku1EfmrSSNzxaL7SSHJVnN4JLrJHl0knc2j0yaYo4L9ZF5K0njN8rM9luBo4FbAarqBuBJLYOSesBxoT4ybyVpzEZaRlJV62Y03d0gFqlXHBfqI/NWksZr/gh91iU5DKgkuwGn0n0FKe3EHBfqI/NWksZslJntk4FXAHsD64HHdNvSzsxxoT4ybyVpzDY7s51kHvCCqnremOKRpp7jQn1k3krSZGx2Zruq7gZOGFMsUi84LtRH5q0kTcYoa7Y/neQ84MPAjzY2VtU/NYtKmn6OC/WReStJYzZKsX1Y9/esobYCnjL34Ui94bhQH5m3kjRmWyy2q+rIcQQi9YnjQn1k3krS+G2x2E5y5mztVXXWbO3SzsBxoT4ybyVp/EZZRvKjodu7A8fheVklx4X6yLyVpDEbZRnJW4a3k7wZuLJZRFIPOC7UR+atJI3fSJdrn+F+wAFzHYjUc44L9ZF5K0mNjbJm+ysMfq0OMA9YwL1/yS7tdBwX6iPzVpLGb5Q128cN3b4L+E5V3dUoHqkvHBfqI/NWksZslGUk84F/r6pvAgcCL0/y4LZhSVPPcaE+Mm8lacxGKbYvB+5O8hvACmB/4C+aRiVNP8eF+si8laQxG6XYvqf7mvGZwNuq6o+Bh7UNS5p6jgv1kXkrSWM2SrF9Z5IlwAuBq7q2XUc5eJJjktyUZG2S0zfR5w+SrE5yYxJnWNQX2zwupAkybyVpzEYptl8MPAE4u6q+kWR/4ENb2inJPOB84FhgEbAkyaIZfQ4EXgM8sar+E/DKrYxfmpRtGhfShJm3kjRmo1zUZjVw6tD2N4A3jXDsQ4G1VXUzQJJLgBOA1UN9XgqcX1Xf74793dFDlyZnO8aFNDHmrSSN3yjn2T4QeCOD2endN7ZX1ZYuhLA3sG5oez3wuBl9HtE9xqcZnPN1eVV9fMthS5O1HeNCmhjzVpLGb5RlJO8H3sXgnKxHAh8APjjCfpmlrWZsz2dw+qkjgCXAe2c7DVWSk5KsSrJqw4YNIzy01NxI48Lc1ZQxb7X/W3/pAAAK3ElEQVRDM3c1jUYptu9bVZ8EUlXfrKrlwFNG2G89sO/Q9j7ALbP0+UhV3dl9nXkTg+L7XqrqwqpaXFWLFyxYMMJDS82NNC7MXU0Z81Y7NHNX02iUK0j+NMkuwNeTLAO+DfzKCPtdBxzY/QDn28CJwHNn9LmCwYz2RUn2YrCs5OZRg5cmaFvHhTRJ5q0kjdkoM9uvBO7H4Ec1jwWeD/zhlnbqzuW6DLgaWANcWlU3JjkryfFdt6uBW5OsBq4B/qSqbt36pyGN3TaNC2nCzFtJGrNRzkZyHUCSqqoXb83Bq2olsHJG25lDtws4rfsn9cb2jAtpUsxbSRq/Lc5sJ3lCN/O8ptt+dJJ3No9MmmKOC/WReStJ4zfKMpK3AUcDtwJU1Q3Ak1oGJfWA40J9ZN5K0piNUmxTVetmNN3dIBapVxwX6iPzVpLGa5SzkaxLchhQSXZj8MOaNW3Dkqae40J9ZN5K0piNMrN9MvAKBleEXA88ptuWdmaOC/WReStJYzbK2Ui+BzxvDLFIveG4UB+Zt5I0flsstruL0pwCLBzuX1XHb2ofaUfnuFAfmbeSNH6jrNm+AlgBfBS4p204Um84LtRH5q0kjdlIl2uvqnObRyL1i+NCfWTeStKYjVJsvz3Ja4FPAD/b2FhV/9QsKmn6OS7UR+atJI3ZKMX2o4AXAE/hF187Vrct7awcF+oj81aSxmyUYvsZwAFVdUfrYKQecVyoj8xbSRqzUc6zfQPw4NaBSD3juFAfmbeSNGajzGz/KvDPSa7j3mv8PFWUdmaOC/WReStJYzZKsf3a5lFI/eO4UB+Zt5I0ZqNcQfLvxxGI1CeOC/WReStJ4zfKmm1JkiRJ28BiW5IkSWpkk8V2koePMxCpDxwX6iPzVpImZ3Mz21dsvJHk8jHEIvWB40J9ZN5K0oRsrtjO0O0DWgci9YTjQn1k3krShGyu2K5N3JZ2Zo4L9ZF5K0kTsrlT/z06yQ8YzIjct7tNt11VtUfz6KTp47hQH5m3kjQhmyy2q2reOAOR+sBxoT4ybyVpcjz1nyRJktSIxbYkSZLUiMW2JEmS1IjFtiRJktRI02I7yTFJbkqyNsnpm+n37CSVZHHLeCRJkqRxalZsJ5kHnA8cCywCliRZNEu/BwKnAp9vFYskSZI0CS1ntg8F1lbVzVV1B3AJcMIs/V4P/Bnw04axSJIkSWPXstjeG1g3tL2+a/u5JIcA+1bVVQ3jkCRJkiaiZbGdWdp+fpngJLsAbwVetcUDJSclWZVk1YYNG+YwRKktc1d9ZN6qr8xdTaOWxfZ6YN+h7X2AW4a2HwgcDPxdkn8FHg9cOduPJKvqwqpaXFWLFyxY0DBkaW6Zu+oj81Z9Ze5qGrUstq8DDkyyf5LdgBOBKzfeWVW3V9VeVbWwqhYCnwOOr6pVDWOSJEmSxqZZsV1VdwHLgKuBNcClVXVjkrOSHN/qcSVJkqRpMb/lwatqJbByRtuZm+h7RMtYJEmSpHHzCpKSJElSIxbbkiRJUiMW25IkSVIjFtuSJElSIxbbkiRJUiMW25IkSVIjFtuSJElSIxbbkiRJUiMW25IkSVIjFtuSJElSIxbbkiRJUiMW25IkSVIjFtuSJElSIxbbkiRJUiMW25IkSVIjFtuSJElSIxbbkiRJUiMW25IkSVIjFtuSJElSIxbbkiRJUiMW25IkSVIjFtuSJElSIxbbkiRJUiMW25IkSVIjFtuSJElSIxbbkiRJUiMW25IkSVIjFtuSJElSI02L7STHJLkpydokp89y/2lJVif5cpJPJtmvZTySJEnSODUrtpPMA84HjgUWAUuSLJrR7YvA4qr6LeAy4M9axSNJkiSNW8uZ7UOBtVV1c1XdAVwCnDDcoaquqaofd5ufA/ZpGI8kSZI0Vi2L7b2BdUPb67u2TVkKfKxhPJIkSdJYtSy2M0tbzdoxeT6wGDhnE/eflGRVklUbNmyYwxCltsxd9ZF5q74ydzWNUjVr/bv9B06eACyvqqO77dcAVNUbZ/Q7CngH8OSq+u4Ix90AfHPuI96h7AV8b9JBTLn9qmrBOB/Q3B2Jubt55u10Mm+3bBK5+3+Bm8b5mCOYtlwxni17ZFU9cHsO0LLYng98DXgq8G3gOuC5VXXjUJ9DGPww8piq+nqTQHZCSVZV1eJJxyFtLXNXfWTeTqdp/O8ybTEZz5bNRUzNlpFU1V3AMuBqYA1waVXdmOSsJMd33c4BHgD8ZZIvJbmyVTySJEnSuM1vefCqWgmsnNF25tDto1o+viRJkjRJXkFyx3ThpAOQtpG5qz4yb6fTNP53mbaYjGfLtjumZmu2JUmSpJ2dM9uSJElSIxbbO5Ak70vy3SRfnXQs0qjMW/WVuTs5SY5JclOStUlOn+X++yT5cHf/55MsHLrvNV37TUmOHlM8pyVZneTLST6ZZL+h++7uThIxZyeKGCGeFyXZMPS4Lxm67w+TfL3794dzEc+IMb11KJ6vJfmPoftavEabHb8ZOLeL98tJfnvovq16jVxGsgNJ8iTgh8AHqurgSccjjcK8VV+Zu5ORZB6DUwv/LoOrU18HLKmq1UN9Xg78VlWdnORE4BlV9Zwki4CLgUOBXwf+FnhEVd3dOJ4jgc9X1Y+TvAw4oqqe0933w6p6wLY+/jbG8yJgcVUtm7HvnsAqBhcaLOB64LFV9f3WMc3ofwpwSFX9Ubc9p69Rd8zNjt8kTwNOAZ4GPA54e1U9blteI2e2dyBV9Q/AbZOOQ9oa5q36ytydmEOBtVV1c1XdAVwCnDCjzwnAn3e3LwOemiRd+yVV9bOq+gawtjte03iq6pqq+nG3+Tlgn+18zO2KZzOOBv6mqm7rise/AY6ZQExLGHwoamaE8XsCg0K8qupzwIOTPIxteI0stiVJUp/sDawb2l7ftc3ap7vux+3AQ0fct0U8w5YCHxva3r27xPznkjx9O2PZmnie1S2PuCzJvlu5b6uY6JbY7A98aqh5rl+jUWwq5q1+jZqeZ1uSJGmOZZa2mWtiN9VnlH1bxDPomDyfwfKDJw81P7yqbklyAPCpJF+pqn9pHM9HgYur6mdJTmbwLcBTRty3VUwbnQhcNmNpz1y/RqOYsxxyZluSJPXJemDfoe19gFs21SfJfOBBDJYMjLJvi3hIchRwBnB8Vf1sY3tV3dL9vRn4O+CQ1vFU1a1DMbwHeOyo+7aKaciJzFhC0uA1GsWmYt7q18hiW5Ik9cl1wIFJ9k+yG4PibOYZKq4ENp4l4tnAp2pwRogrgRO7s5XsDxwIfKF1PEkOAd7NoND+7lD7Q5Lcp7u9F/BEYNYfDc5xPA8b2jweWNPdvhr4vS6uhwC/17Vtr1H+m5HkkcBDgM8OtbV4jUZxJfDC7qwkjwdur6p/YxteI5eR7ECSXAwcAeyVZD3w2qpaMdmopM0zb9VX5u5kVNVdSZYxKHDmAe+rqhuTnAWsqqorgRXAB5OsZTCjfWK3741JLmVQrN0FvGJ7zkSyFfGcAzwA+MvB7zT5VlUdDxwEvDvJPQwmQN+0qTN0zHE8pyY5nsFrcBvwom7f25K8nkFxDHBWVW33j4BHjAkGP4y8pO59qrw5f41g9vEL7NrFewGwksGZSNYCPwZe3N231a+Rp/6TJEmSGnEZiSRJktSIxbYkSZLUiMW2JEmS1IjFtiRJktSIxbYkSZLUiMW2JEmS1IjFtiRJktSIxbYkSZLUyP8POWywts3+2KsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "File              ML algorithm       accuracy        Precision       Recall          F1-score        Time           \n",
      "DoS Slowhttptest  Naive Bayes        0.41            0.7             0.41            0.35            0.0073         \n",
      "DoS Slowhttptest  QDA                0.43            0.72            0.43            0.38            0.0062         \n",
      "DoS Slowhttptest  Random Forest      0.98            0.98            0.98            0.98            0.0516         \n",
      "DoS Slowhttptest  ID3                0.98            0.98            0.98            0.98            0.0187         \n",
      "DoS Slowhttptest  AdaBoost           0.99            0.99            0.99            0.99            0.297          \n",
      "DoS Slowhttptest  MLP                0.78            0.84            0.78            0.78            0.3972         \n",
      "DoS Slowhttptest  Nearest Neighbors  0.99            0.99            0.99            0.99            0.0845         \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "File              ML algorithm       accuracy        Precision       Recall          F1-score        Time           \n",
      "DoS slowloris     Naive Bayes        0.42            0.8             0.42            0.37            0.0059         \n",
      "DoS slowloris     QDA                0.49            0.78            0.49            0.46            0.0078         \n",
      "DoS slowloris     Random Forest      0.95            0.95            0.95            0.95            0.0513         \n",
      "DoS slowloris     ID3                0.96            0.96            0.96            0.96            0.0173         \n",
      "DoS slowloris     AdaBoost           0.95            0.95            0.95            0.95            0.3207         \n",
      "DoS slowloris     MLP                0.75            0.79            0.75            0.74            0.3717         \n",
      "DoS slowloris     Nearest Neighbors  0.95            0.95            0.95            0.95            0.0419         \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "File              ML algorithm       accuracy        Precision       Recall          F1-score        Time           \n",
      "FTP-Patator       Naive Bayes        1.0             1.0             1.0             1.0             0.0078         \n",
      "FTP-Patator       QDA                1.0             1.0             1.0             1.0             0.0083         \n",
      "FTP-Patator       Random Forest      1.0             1.0             1.0             1.0             0.0544         \n",
      "FTP-Patator       ID3                1.0             1.0             1.0             1.0             0.0156         \n",
      "FTP-Patator       AdaBoost           1.0             1.0             1.0             1.0             0.3859         \n",
      "FTP-Patator       MLP                1.0             1.0             1.0             1.0             2.2922         \n",
      "FTP-Patator       Nearest Neighbors  1.0             1.0             1.0             1.0             0.2547         \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "File              ML algorithm       accuracy        Precision       Recall          F1-score        Time           \n",
      "Heartbleed        Naive Bayes        1.0             1.0             1.0             1.0             0.0047         \n",
      "Heartbleed        QDA                1.0             1.0             1.0             1.0             0.0031         \n",
      "Heartbleed        Random Forest      0.99            0.99            0.99            0.99            0.0107         \n",
      "Heartbleed        ID3                0.96            0.93            0.96            0.95            0.0016         \n",
      "Heartbleed        AdaBoost           0.95            0.91            0.95            0.93            0.0031         \n",
      "Heartbleed        MLP                0.64            0.8             0.64            0.66            0.0078         \n",
      "Heartbleed        Nearest Neighbors  1.0             1.0             1.0             1.0             0.0031         \n"
     ]
    },
    {
     "data": {
      "image/png": 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LeyX5QPPKpCnmvFAfmVv1ldlVn41yGMl7gWcBPwGoqiuBp7YsSuoB54X6yNyqr8yuemuUZpuqumHGqrsa1CL1ivNCfWRu1VdmV301ytlIbkiyL1BJtmHw4YTVbcuSpp7zQn1kbtVXZle9Ncqe7aOAo4EdgLXA47tlaXPmvFAfmVv1ldlVb41yNpIfAy8aQy1Sbzgv1EfmVn1ldtVnczbbSXYBjgWWDo+vqoPblSVNN+eF+sjcqq/MrvpslGO2LwTOBD4D3N22HKk3nBfqI3OrvjK76q2Rvq69qk5ZyMaTHAScDGwJnFFVb59lzJ8AK4ACrqyqFy7kvqQxW/C8kCbI3KqvzK56a5Rm++QkJwCfB25ft7KqvrGhGyXZEjgVOJDBhxkuT7Kyqq4ZGrMb8AbgKVV1S5JHLuAxSJOwoHkhTZi5VV+ZXfXWKM327wB/BjyN37x1U93yhuwDrKmq6wCSnAccAlwzNOZlwKlVdQtAVf1o9NKliVrovJAmydyqr8yuemuUZvv5wK5Vdcc8t70DMHwC+rXAE2eMeSxAkq8yONRkRVX9w8wNJTkSOBJg5513nmcZUhMjzQuzqyljbtVXZle9Ncp5tq8Etl3AtjPLupqxvBWwG7A/sBw4I8l97quqTq+qZVW1bMmSJQsoRVp0I80Ls6spY27VV2ZXvTXKnu1HAf+a5HLufZzUXKfbWQvsNLS8I3DjLGMuq6pfA9cnuZZB8335CHVJk7TQeSFNkrlVX5ld9dYozfYJC9z25cBu3bkxvw8cCsw808iFDPZon51kewaHlVy3wPuTxmmh80KaJHOrvjK76q1RvkHyHxey4aq6M8kxwMUMjsc+q6quTnIisKqqVnbXPTPJNcBdwF9W1U8Wcn/SOC10XkiTZG7VV2ZXfTbKnu0Fq6qLgItmrDt+6HIBr+n+SZIkSZuUUT4gKUmSJGkB1ttsJ/GcOdIMzgv1kblVX5ldbQo2tGf7wnUXknxqDLVIfeC8UB+ZW/WV2VXvbajZHj5P9q6tC5F6wnmhPjK36iuzq97bULNd67ksbc6cF+ojc6u+MrvqvQ2djWSvJD9j8FflA7rLdMtVVQ9tXp00fZwX6iNzq74yu+q99TbbVbXlOAuR+sB5oT4yt+ors6tNgaf+kyRJkhqx2ZYkSZIasdmWJEmSGrHZliRJkhqx2ZYkSZIasdmWJEmSGrHZliRJkhqx2ZYkSZIasdmWJEmSGrHZliRJkhqx2ZYkSZIasdmWJEmSGrHZliRJkhqx2ZYkSZIasdmWJEmSGrHZliRJkhqx2ZYkSZIasdmWJEmSGrHZliRJkhqx2ZYkSZIasdmWJEmSGrHZliRJkhqx2ZYkSZIasdmWJEmSGrHZliRJkhpp2mwnOSjJtUnWJHn9Bsa9IEklWdayHkmSJGmcmjXbSbYETgWeDewBLE+yxyzjHgK8Evh6q1okSZKkSWi5Z3sfYE1VXVdVdwDnAYfMMu7NwDuAXzWsRZIkSRq7ls32DsANQ8tru3X3SLI3sFNVfbZhHZIkSdJEtGy2M8u6uufKZAvgPcBr59xQcmSSVUlW3XTTTYtYotSW2VUfmVv1ldnVNGrZbK8Fdhpa3hG4cWj5IcCewJeTfAd4ErBytg9JVtXpVbWsqpYtWbKkYcnS4jK76iNzq74yu5pGLZvty4HdkuySZBvgUGDluiur6taq2r6qllbVUuAy4OCqWtWwJkmSJGlsmjXbVXUncAxwMbAaOL+qrk5yYpKDW92vJEmSNC22arnxqroIuGjGuuPXM3b/lrVIkiRJ4+Y3SEqSJEmN2GxLkiRJjdhsS5IkSY3YbEuSJEmN2GxLkiRJjdhsS5IkSY3YbEuSJEmN2GxLkiRJjdhsS5IkSY3YbEuSJEmN2GxLkiRJjdhsS5IkSY2kqiZdw7wkuQn47qTrmHLbAz+edBFT7jFVtWScd2h2R2J2N8zcTidzOzezO53M7tw2Oru9a7Y1tySrqmrZpOuQ5svsqo/MrfrK7I6Hh5FIkiRJjdhsS5IkSY3YbG+aTp90AdICmV31kblVX5ndMfCYbUmSJKkR92xLkiRJjdhsb0KSnJXkR0m+OelapFGZW/WV2VVfmd3xstnetJwNHDTpIqR5Ohtzq346G7Orfjobszs2NtubkKr6CnDzpOuQ5sPcqq/MrvrK7I6XzbYkSZLUiM22JEmS1IjNtiRJktSIzbYkSZLUiM32JiTJucDXgMclWZvkiEnXJM3F3KqvzK76yuyOl98gKUmSJDXinm1JkiSpEZttSZIkqRGbbUmSJKkRm+0FSnJYkud2l387yYp53n6LJBcMLV8w4/qzkzx4ntsc6TZJ3pXkvbOsX5Fkz1nWH5bks0k+kOR986lprm1rOvU130k+n+S/d5cPTrJ2Pff/T0lOTbIyyW/Npw4tTI8z9cUkr+4uvz3J0nneR+a4fv8kx8yy7oYkD0py/yRnr+e271zP+nue66F1F8w2dr71Spq/rSZdwKYkyVbAScD9gC2BVwEvB3YF7q6q13Uvmt8Brgf26H7hnMHgE8FvAf4z8BdD2zwQ+H3gAcCngGuB1wIBvg2cBpzVbe93RqjxfsDDgbuTPKiqbkvyYeBHwL7ABUl+H/g94JHAa7qbnlZVn01yYbedQ4DnAfcHVnRjVgC/Aj4D/BtwAvA94KPAfsAjk3yiqi6d88nU1OlDvoGfAUu7ywcBl69n3Peq6ugkLwSe3N23xqwnmboF2DvJtkP3sTdwGIPfoV8DvgQcDTwC+IequjDJ1cA5wN93Z3pY9xhfB5wNrAUu7WrYN8lPq+pjQ/f7JeDVwLuG7vc4YAnwkO652qVb/2pgZ+BhwDeAnwN/kuQg4IdV9WYGr79vBh7XbXen7rkO8AEGr+V/CXw1yXcZ/A74D+CNVXX7CM+TpPWw2d44r+j2HmwL/CtwIINf9KuBxwA7MHhx/SWwf5JHdrf7cFV9P8nzqmoFQJIfV9VfJ/lj4OCh+3glcAXwU2Af4Ond9n7J4EX6QOALVfWRJE8YoebnAxcBtzN4MV4F3FhVJyT5YDfmLgbvemwNPKNb97IkfwDc0C2/pKr+KMljgGO7dW+qquuT/B3wbga/pD5RVd9McilwQVV9c4QaNR36mG8YNAsvAG5i8AfjbHZKcjKD18D/PuJ2tfH6mql3AH81tPwaBo07wN7A5xk00z8EXgRcyOB19e1Jnj3jMf4nBo3t54CvALcCMxttGPyh+HjgUd3j3R14KoPm/n7A7kNj9+tej/943Xjg4qr6eJJPdst3VdWbkvw34CXAnsC60719GDgT+N9V9dbu3aGrgE/baEsbz2Z743yg29v728ChDBrUr1bVKQBJHgHsVVV/nuQjwAO7293a/Rw+72IN/RxevwXwlqq6s9vmO4Fzquqqbvlg4I5u7L1eFJO8isGL/Buq6pfd6hcyaEKKwZ6QK2a5/cur6pAkLx2q+cPdY31Dkr1mqTvDj6GqLkvyfeCIbvzdM588Tb0+5hsGezNXA88E3raex3ZDVf2/czx+Lb5eZqrbYfBQYLtu1TbAyVV1S3e71wIrga8Dn55R870eYzf+MAb5fD/wifU+W4P8vnFoO1ev+2Oj285sz8U6M5+zmc9XZoy/5zZV9Y7udfudSd5UVd/aQI2S5mCzvbguBk7rXty3ZfA23wOTvA547Czjr07yLuBkYEmSkxi8LfgyBm+BA5wCnJHkZmAVgxfntyb5AYO3+P4HcHKSJfzm7XMAqupex2V3xxp+v6pe3i2/G7iTwduLr2KwFwXgmu7tyt2B/9WtOyrJsxi8Tfo+4GNJPsTgl+GbuzFvTvIL4NwkBzA4zOThwBcYHKbyl0nOqqp/nPOZ1DSa6nwPrf9VksdW1a+HmpEHJTmtu/zx+T90NdKLTHXeymBv/Bu7bbwvyQ8ZHOLyv4GjgKfwm0Z+fY/x+G4bdwFXMzjk7jVJbquqj8yo59vda+r9qurqJHcn+VsGh8i8dWjopUnew+D1+UvrqX+r7rCb3RjsmX8Mg0NqAD7I4A8IAJIc2Y27G/jJBp4TSSPwS20kSeqxJE9jcMjfbwGvrSobZGmK2GxLkiRJjXjqP0mSJKkRm21JkiSpEZttSZIkqRGbbUmSJKmR3p36b/vtt6+lS5dOugz13BVXXPHjqloyzvs0u9pY5lZ9ZXbVV4uR3d4120uXLmXVqlWTLkM9130d8ViZXW0sc6u+Mrvqq8XIroeRSJIkSY00a7aTnJXkR0m+uZ7rk+SUJGuSXJXkCa1qkSRJkiah5Z7ts/nN1+fO5tkMvg52N+BIBl8XK0mSJG0ymjXbVfUV4OYNDDkE+GgNXAZsm+TRreqRJEmSxm2SH5DcAbhhaHltt+4HMwcmOZLB3m923nnnsRQ3dVY8rOG2b2237c2c2VUfmVt8ze0ps6tpNMlmO7Osq9kGVtXpwOkAy5Ytm3XMpi5/8zOqFv+hJ6FWLPpm1TG76iNziw1xT5ldTaNJno1kLbDT0PKOwI0TqkWSJEladJNstlcCL+nOSvIk4Naqus8hJJIkSVJfNTuMJMm5wP7A9knWAicAWwNU1WnARcBzgDXAL4DDW9UiSZIkTUKzZruqls9xfQFHt7p/SZIkadL8BklJkiSpEZttSZIkqRGbbUmSJKkRm21JkiSpEZttSZIkqRGbbUmSJKkRm21JkiSpEZttSZIkqRGbbUmSJKkRm21JkiSpEZttSZIkqRGbbUmSJKkRm21JkiSpEZttSZIkqRGbbUmSJKkRm21JkiSpEZttSZIkqRGbbUmSJKkRm21JkiSpEZttSZIkqRGbbUmSJKkRm21JkiSpEZttSZIkqRGbbUmSJKkRm21JkiSpEZttSZIkqRGbbUmSJKkRm21JkiSpEZttSZIkqRGbbUmSJKkRm21JkiSpEZttSZIkqZGmzXaSg5Jcm2RNktfPcv3OSS5J8s9JrkrynJb19F2SRf+33XbbTfphaTM035xKktRXW7XacJItgVOBA4G1wOVJVlbVNUPD/ho4v6o+mGQP4CJgaaua+qyqJl2CtGjWl+ckZl2StElpuWd7H2BNVV1XVXcA5wGHzBhTwEO7yw8DbmxYjyRJkjRWzfZsAzsANwwtrwWeOGPMCuDzSY4FHgQ8o2E9kiRJ0li13LM924GWM98fXg6cXVU7As8Bzklyn5qSHJlkVZJVN910U4NSpTbMrvrI3KqvzK6m0cjNdpIHzXPba4GdhpZ35L6HiRwBnA9QVV8D7g9sP3NDVXV6VS2rqmVLliyZZxlSO3PNC7OraWRutakyu5pGczbbSfZNcg2wulveK8kHRtj25cBuSXZJsg1wKLByxpjvAU/vtrs7g2bbP0U19TZiXkgTY24lafxG2bP9HuBZwE8AqupK4Klz3aiq7gSOAS5m8MJ+flVdneTEJAd3w14LvCzJlcC5wGHlqQjUDwuaF9KEmVtJGrORPiBZVTfMONftXSPe7iIGp/MbXnf80OVrgKeMsi1p2ix0XkiTZG4labxGabZvSLIvUN3hIK+kewtS2ow5L9RH5laSxmyUw0iOAo5mcCq/tcDju2Vpc+a8UB+ZW0kasw3u2e6+BfLPqupFY6pHmnrOC/WRuZWkydjgnu2quov7fuujtFlzXqiPzK0kTcYox2x/Ncn7gU8Ct61bWVXfaFaVNP2cF+ojcytJYzZKs71v9/PEoXUFPG3xy5F6w3kxooc//OHccsstI4+fcaaMWW233XbcfPPNG1PW5srcStKYzdlsV9UB4yhE6hPnxehuueUWFvv0+aM05LovcytJ4zdns53k+NnWV9WJs62XNgfOC/WRuZWk8RvlMJLbhi7fH3gunpdVcl6oj8ytJI3ZKIeRvHt4Ocm7gJXNKpJ6wHmhPjK3kjR+o3ypzUwPBHZd7EKknnNeqI/MrSQ1Nsox2//C4NPqAFsCS7j3J9mlzY7zYnR1wkNhxcMWf5uaN3MrSeM3yjHbzx26fCfww6q6s1E9Ul84L0aUv/lZk7OR1IpF3eTmwtxK0piNchjJVsC/V9V3gd2AVyTZtm1Z0tRzXqiPzK0kjdkozfangLuS/BfgTGAX4BNNq5Kmn/NCfWRuJWnMRmm27+7eZvxD4L1V9Wrg0W3Lkqae80J9ZG4lacwKYRWjAAALR0lEQVRGabZ/nWQ58BLgs926rduVJPWC80J9ZG4lacxGabYPB54MnFRV1yfZBfhY27Kkqee8UB+ZW0kas1G+1OYa4JVDy9cDb29ZlDTtnBfqI3MrSeM3ynm2dwPeBuzB4Ot9AagqvwhBmy3nhfrI3ErS+I1yGMlHgA8yOCfrAcBHgXNaFiX1gPNCfWRuJWnMRmm2H1BVXwRSVd+tqhXA09qWJU0954X6yNxK0piN8g2Sv0qyBfCtJMcA3wce2bYsaeo5L9RH5laSxmyUPduvAh7I4EM1vwu8GHhpy6KkHnBeqI/MrSSN2ShnI7kcIElV1eHtS5Kmn/NCfWRuJWn85tyzneTJSa4BVnfLeyX5QPPKpCnmvFAfmVtJGr9RDiN5L/As4CcAVXUl8NSWRUk94LxQH5lbSRqzUZptquqGGavualCL1CvOC/WRuZWk8RrlbCQ3JNkXqCTbMPhgzeq2ZUlTz3mhPjK3kjRmo+zZPgo4GtgBWAs8vluWNmfOC/WRuZWkMRvlbCQ/Bl40hlqk3nBeqI/MrSSN35zNdpJdgGOBpcPjq+rgdmVJ0815oT4yt5I0fqMcs30hcCbwGeDutuVIveG8UB+ZW0kas5G+rr2qTlnIxpMcBJwMbAmcUVVvn2XMnwArgAKurKoXLuS+pDFb8LyQJsjcStKYjdJsn5zkBODzwO3rVlbVNzZ0oyRbAqcCBzL4IM7lSVZW1TVDY3YD3gA8papuSfLIBTwGaRIWNC+kCTO3kjRmozTbvwP8GfA0fvO2Y3XLG7IPsKaqrgNIch5wCHDN0JiXAadW1S0AVfWj0UuXJmqh80KaJHMrSWM2SrP9fGDXqrpjntveARj+8oS1wBNnjHksQJKvMjjUZEVV/cPMDSU5EjgSYOedd55nGVITI80Ls6spY261STO7mkajnGf7SmDbBWw7s6yrGctbAbsB+wPLgTOS3Oe+qur0qlpWVcuWLFmygFKkRTfSvDC7mjLmVps0s6tpNMqe7UcB/5rkcu59jN9cp4paC+w0tLwjcOMsYy6rql8D1ye5lkHzffkIdUmTtNB5IU2SuZWkMRul2T5hgdu+HNitO6/r94FDgZlnGrmQwR7ts5Nsz+CwkusWeH/SOC10XkiTZG4lacxG+QbJf1zIhqvqziTHABczOB77rKq6OsmJwKqqWtld98wk1wB3AX9ZVT9ZyP1J47TQeSFNkrmVpPEbZc/2glXVRcBFM9YdP3S5gNd0/yRJkqRNStNmW5IAktk+L71w22233aJuT5KkVtbbbCfZuaq+N85ipGnnvJi/wRtYo0kyr/EajbmVpMnZ0Kn/Llx3IcmnxlCL1AfOC/WRuZWkCdlQsz38vu+urQuResJ5oT4yt5I0IRtqtms9l6XNmfNCfWRuJWlCNvQByb2S/IzBHpEHdJfplquqHtq8Omn6OC/UR+ZWkiZkvc12VW05zkKkPnBeqI/MrSRNzoYOI5EkSZK0EWy2JUmSpEZstiVJkqRGbLYlSZKkRmy2JUmSpEZstiVJkqRGbLYlSZKkRmy2JUmSpEZstiVJkqRGbLYlSZKkRmy2JUmSpEZstiVJkqRGbLYlSZKkRmy2JUmSpEZstiVJkqRGbLYlSZKkRmy2JUmSpEZstiVJkqRGbLYlSZKkRmy2JUmSpEZstiVJkqRGbLYlSZKkRmy2JUmSpEZstiVJkqRGbLYlSZKkRpo220kOSnJtkjVJXr+BcS9IUkmWtaxHkiRJGqdmzXaSLYFTgWcDewDLk+wxy7iHAK8Evt6qFkmSJGkSWu7Z3gdYU1XXVdUdwHnAIbOMezPwDuBXDWuRJEmSxq5ls70DcMPQ8tpu3T2S7A3sVFWfbViHJEmSNBEtm+3Msq7uuTLZAngP8No5N5QcmWRVklU33XTTIpYotWV21UfmVn1ldjWNWjbba4GdhpZ3BG4cWn4IsCfw5STfAZ4ErJztQ5JVdXpVLauqZUuWLGlYsrS4zK76yNyqr8yuplHLZvtyYLckuyTZBjgUWLnuyqq6taq2r6qlVbUUuAw4uKpWNaxJkiRJGptmzXZV3QkcA1wMrAbOr6qrk5yY5OBW9ytJkiRNi61abryqLgIumrHu+PWM3b9lLZIkSdK4+Q2SkiRJUiM225IkSVIjNtuSJElSIzbbkiRJUiM225IkSVIjTc9GIkmzSWb7gtn1X1dVs4yUJGn62WxLGjubZ0nS5sLDSCRJkqRGbLYlSZKkRmy2JUmSpEZstiVJkqRGbLYlSZKkRtK3swIkuQn47qTrmHLbAz+edBFT7jFVtWScd2h2R2J2N8zcTidzO7dJZPc/gGvHeZ8jmLasWM/cHldVD9mYDfTu1H/jnqx9lGRVVS2bdB26N7M7N7M7fczt3Mzt1Lp22v5fpi0r1jO3JKs2dhseRiJJkiQ1YrMtSZIkNWKzvWk6fdIFSAtkdtVH5nY6TeP/y7TVZD1z2+iaevcBSUmSJKkv3LMtSZIkNWKzvQlJclaSHyX55qRrkUZlbtVXZndykhyU5Noka5K8fpbr75fkk931X0+ydOi6N3Trr03yrDHV85ok1yS5KskXkzxm6Lq7kvyf7t/KMdVzWJKbhu73L4aue2mSb3X/XroY9YxY03uG6vm3JD8duq7Fc7TB+ZuBU7p6r0ryhKHr5vUceRjJJiTJU4GfAx+tqj0nXY80CnOrvjK7k5FkS+DfgAOBtcDlwPKqumZozCuA/6uqjkpyKPD8qvrTJHsA5wL7AL8F/C/gsVV1V+N6DgC+XlW/SPJyYP+q+tPuup9X1YMXev8LrOcwYFlVHTPjtg8HVgHLgAKuAH63qm5pXdOM8ccCe1fVn3fLi/ocddvc4PxN8hzgWOA5wBOBk6vqiQt5jtyzvQmpqq8AN0+6Dmk+zK36yuxOzD7Amqq6rqruAM4DDpkx5hDgf3aXLwCeniTd+vOq6vaquh5Y022vaT1VdUlV/aJbvAzYcSPvc6Pq2YBnAV+oqpu75vELwEETqGk5gz+Kmhlh/h7CoBGvqroM2DbJo1nAc2SzLUmS+mQH4Iah5bXdulnHVNWdwK3AI0a8bYt6hh0BfG5o+f5JViW5LMkfbGQt86nnj7rDIy5IstM8b9uqJrpDbHYBvjS0erGfo1Gsr+Z5P0e9+wZJSZK0Wcss62YeE7u+MaPctkU9g4HJixkcfvB7Q6t3rqobk+wKfCnJv1TVtxvX8xng3Kq6PclRDN4FeNqIt21V0zqHAhfMOLRnsZ+jUSxahtyzLUmS+mQtsNPQ8o7Ajesbk2Qr4GEMDhkY5bYt6iHJM4DjgIOr6vZ166vqxu7ndcCXgb1b11NVPxmq4cPA745621Y1DTmUGYeQNHiORrG+muf9HNlsS5KkPrkc2C3JLkm2YdCczTxDxUpg3VkiXgB8qQZnhFgJHNqdrWQXYDfgn1rXk2Rv4EMMGu0fDa3fLsn9usvbA08BZv3Q4CLX8+ihxYOB1d3li4FndnVtBzyzW7exRvk/I8njgO2Arw2ta/EcjWIl8JLurCRPAm6tqh+wgOfIw0g2IUnOBfYHtk+yFjihqs6cbFXShplb9ZXZnYyqujPJMQwanC2Bs6rq6iQnAquqaiVwJnBOkjUM9mgf2t326iTnM2jW7gSO3pgzkcyjnncCDwb+bvA5Tb5XVQcDuwMfSnI3gx2gb1/fGToWuZ5XJjmYwXNwM3BYd9ubk7yZQXMMcGJVbfSHgEesCQYfjDyv7n2qvEV/jmD2+Qts3dV7GnARgzORrAF+ARzeXTfv58hT/0mSJEmNeBiJJEmS1IjNtiRJktSIzbYkSZLUiM22JEmS1IjNtiRJktSIzbYkSZLUiM22JEmS1IjNtiRJktTI/w+LVs6LkLZ7jAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "File              ML algorithm       accuracy        Precision       Recall          F1-score        Time           \n",
      "Infiltration      Naive Bayes        0.81            0.79            0.81            0.78            0.0031         \n",
      "Infiltration      QDA                0.85            0.84            0.85            0.83            0.0016         \n",
      "Infiltration      Random Forest      0.92            0.92            0.92            0.92            0.0136         \n",
      "Infiltration      ID3                0.88            0.89            0.88            0.89            0.0028         \n",
      "Infiltration      AdaBoost           0.92            0.92            0.92            0.92            0.05           \n",
      "Infiltration      MLP                0.57            0.67            0.57            0.52            0.0109         \n",
      "Infiltration      Nearest Neighbors  0.88            0.88            0.88            0.88            0.0031         \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "File              ML algorithm       accuracy        Precision       Recall          F1-score        Time           \n",
      "PortScan          Naive Bayes        0.43            0.8             0.43            0.39            0.2033         \n",
      "PortScan          QDA                0.84            0.89            0.84            0.85            0.2342         \n",
      "PortScan          Random Forest      1.0             1.0             1.0             1.0             2.2375         \n",
      "PortScan          ID3                1.0             1.0             1.0             1.0             0.7297         \n",
      "PortScan          AdaBoost           1.0             1.0             1.0             1.0             13.733         \n",
      "PortScan          MLP                0.72            0.64            0.72            0.61            16.5019        \n",
      "PortScan          Nearest Neighbors  1.0             1.0             1.0             1.0             49.3462        \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "File              ML algorithm       accuracy        Precision       Recall          F1-score        Time           \n",
      "SSH-Patator       Naive Bayes        0.4             0.8             0.4             0.33            0.0078         \n",
      "SSH-Patator       QDA                0.45            0.79            0.45            0.41            0.0084         \n",
      "SSH-Patator       Random Forest      0.96            0.96            0.96            0.96            0.068          \n",
      "SSH-Patator       ID3                0.96            0.96            0.96            0.96            0.0262         \n",
      "SSH-Patator       AdaBoost           0.96            0.96            0.96            0.96            0.4487         \n",
      "SSH-Patator       MLP                0.84            0.83            0.84            0.82            0.4074         \n",
      "SSH-Patator       Nearest Neighbors  0.95            0.95            0.95            0.95            0.0483         \n"
     ]
    },
    {
     "data": {
      "image/png": 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4UB+Zt+orc1e9Nc7dSDYDJKmqenH7kKTFz3GhPjJv1Vfmrvps1pntJE9Kci1wXbf82CQXNI9MWsQcF+oj81Z9Ze6qz8Y5jeTNwLHAzQBVdTXwlJZBST3guFAfmbfqK3NXvTVOsU1VbR9Zdcc4+yU5Lsn1SbYlOX2aNs9Ncm2SrUkuG6dfaTGY67iQFpJ5q74yd9VX49yNZHuSo4BKsi+DixOum22nJMuA84FjgB3A5iQbu6dA7W6zCjgDeHJV3ZrEK4vVF3MaF9ICM2/VV+auemucme2TgVOAFQyK5sd1y7M5AthWVTdU1e3A5cDxI21+HTi/qm4FqKqvjRu4tMDmOi6khWTeqq/MXfXWOHcj+Trwq3PoewUw/CefHcCRI20eDpDkk8Ay4Kyq+qvRjpKcBJwEcOihh84hFGl+jTsuzF0tJuat+srcVZ/NWmwnOQw4FVg53L6q1sy26xTraorjrwKOBg4G/j7Jo6rqG3faqepC4EKA1atXj/YhTdy448Lc1WJi3qqvzF312TjnbH8AuAj4IPCDPeh7B3DI0PLBwE1TtPl0VX0f+FKS6xkU35v34DjSQpjruJAWknmrvjJ31VtjPa69qt4yh743A6u630ZvBNYCzxtp8wFgHXBJkoMYnFZywxyOJU3aXMeFtJDMW/WVuaveGqfYPjfJa4APA9/bvbKq/nGmnapqV5L1wFUMzse+uKq2JtkAbKmqjd22p3c3qr8DeFVV3TzH70WapDmNC2mBmbfqK3NXvTVOsf1o4AXA0/jRn26qW55RVW0CNo2sO3PodQEv776kPpnzuJAWkHmrvjJ31VvjFNvPAh7W3b5P0oDjQn1k3qqvzF311jj32b4auH/rQKSecVyoj8xb9ZW5q94aZ2b7wcC/JtnMnc+Tmu3Wf9JS5rhQH5m36itzV701TrH9muZRSP3juFAfmbfqK3NXvTXOEyQ/PolApD5xXKiPzFv1lbmrPhvnnG1JkiRJc2CxLUmSJDUybbGd5NBJBiL1geNCfWTeqq/MXS0FM81sf2D3iyTvn0AsUh84LtRH5q36ytxV781UbGfo9cNaByL1hONCfWTeqq/MXfXeTMV2TfNaujtzXKiPzFv1lbmr3pvp1n+PTfJNBr9V3rt7TbdcVXW/5tFJi4/jQn1k3qqvzF313rTFdlUt29vOkxwHnAssA95ZVWePbD8ReANwY7fqvKp6594eV2plPsaFNGnmrfrK3NVSMM4TJOckyTLgfOAYYAewOcnGqrp2pOn7qmp9qzgkSZKkhdLyPttHANuq6oaquh24HDi+4fEkSZKkRaVlsb0C2D60vKNbN+qEJNckuTLJIQ3jkSRJkiaqZbGdKdaNXkn8QWBlVT0G+Bvg3VN2lJyUZEuSLTt37pznMKV2zF31kXmrvjJ3tRi1LLZ3AMMz1QcDNw03qKqbq+p73eI7gCdM1VFVXVhVq6tq9fLly5sEK7Vg7qqPzFv1lbmrxahlsb0ZWJXksCT7AmuBjcMNkjxkaHENcF3DeCRJkqSJanY3kqralWQ9cBWDW/9dXFVbk2wAtlTVRuC0JGuAXcAtwImt4pEkSZImrVmxDVBVm4BNI+vOHHp9BnBGyxgkSZKkhdLyNBJJkiTpbs1iW5IkSWrEYluSJElqxGJbkiRJasRiW5IkSWrEYluSJElqxGJbkiRJasRiW5IkSWrEYluSJElqxGJbkiRJasRiW5IkSWrEYluSJElqxGJbkiRJaqRpsZ3kuCTXJ9mW5PQZ2j0nSSVZ3TIeSZIkaZL2adVxkmXA+cAxwA5gc5KNVXXtSLv9gdOAz7SKRdICOuuARv3e1qZfSZLmUbNiGzgC2FZVNwAkuRw4Hrh2pN0fAn8EvLJhLJIWikWxJOlurOVpJCuA7UPLO7p1P5Tk8cAhVfWhmTpKclKSLUm27Ny5c/4jlRoxd9VH5q36ytzVYtSy2M4U6+qHG5N7AOcAr5ito6q6sKpWV9Xq5cuXz2OIUlvmrvrIvFVfmbtajFoW2zuAQ4aWDwZuGlreH3gU8LdJ/g14IrDRiyQlSZK0VLQstjcDq5IclmRfYC2wcffGqrqtqg6qqpVVtRL4NLCmqrY0jEmSJEmamGbFdlXtAtYDVwHXAVdU1dYkG5KsaXVcSZIkabFoeTcSqmoTsGlk3ZnTtD26ZSySJEnSpPkESUmSJKkRi21JkiSpEYttSZIkqRGLbUmSJKkRi21JkiSpEYttSZIkqRGLbUmSJKkRi21JkiSpEYttSZIkqZGmT5DUPDrrgIZ939aub0mSpLsxi+2+sCCWJEnqHU8jkSRJkhppWmwnOS7J9Um2JTl9iu0nJ/nnJP+U5BNJDm8ZjyRJkjRJzYrtJMuA84FnAIcD66Yopi+rqkdX1eOAPwLe1CoeSZIkadJazmwfAWyrqhuq6nbgcuD44QZV9c2hxfsA1TAeSZIkaaJaXiC5Atg+tLwDOHK0UZJTgJcD+wJPaxiPJEmSNFEti+1Mse4uM9dVdT5wfpLnAb8PvOguHSUnASd1i99Ocv18BroEHQR8faGDWOQeOomDmLt7zNydmXm7OJm3szN3Fydzd3Z7nbupanPmRpInAWdV1bHd8hkAVfX6adrfA7i1qhreUPruIcmWqlq90HFIe8rcVR+Zt+orc3cyWp6zvRlYleSwJPsCa4GNww2SrBpa/G/AFxrGI0mSJE1Us9NIqmpXkvXAVcAy4OKq2ppkA7ClqjYC65P8PPB94FamOIVEkiRJ6qumT5Csqk3AppF1Zw69/u2Wx78bu3ChA5DmyNxVH5m36itzdwKanbMtSZIk3d35uHZJkiSpEYvtJSTJxUm+luRfFjoWaVzmrfrK3FVfmbuTZbG9tFwCHLfQQUh76BLMW/XTJZi76qdLMHcnxmJ7CamqvwNuWeg4pD1h3qqvzF31lbk7WRbbkiRJUiMW25IkSVIjFtuSJElSIxbbkiRJUiMW20tIkvcCnwIekWRHkpcsdEzSbMxb9ZW5q74ydyfLJ0hKkiRJjTizLUmSJDVisS1JkiQ1YrEtSZIkNWKxvYeS/FSSK5Ocl+TVGXj70NdBSS5Jct+u/clJjh7p4x+SnJ9kY5Ifn+IYZyV51DTHn3bbUJtZ/1+TvDzJFVOsPzHJL0yx/ugkH+++7z8d5xjj9q3FYynkdxffm7vXj07yn0nuOxx3t+0vu+/zfyd5zEx9au6WUE6dM118sxmj/5VJ3jjFum8lWdEtXz7NvhuS3HuK9UcnWT+y7so9iDnjtpU0s30WOoAeejpwUVX9JUCSA4EDqmrt7gZjfEZ9papOSfI84EndB+XjgPsBpwA/AzwoyWXAyhm2/RTwmG7bS4FTgQOBzwHvmiWGJwBfTPLQqvpykrOBAI8A3pnkycAvAv8F+J/dPn9WVecleTewf5JHAr/Z7XcB8AXgzcA3gWuAPwfOB/4N+FAX+35JqKoPzfYmaUEslfy+d3fc5wN/NU2b71TV+iRHAccwyFnNv6WSU/dJ8hNDMT8UeAWDz78vAhcCvwvcH7i6qt6R5JPAB4GPJjkGWA7s3x37jxl8Vm4Fvg8clWR9VZ03dMxNXZ+nDB33N4GHAw8Afh84FFiW5FeAnwO+DfyAwWfu05McBtyzqtYD90zyBww+518HfBc4C/jPLs6rgT/tXn8xyRrg/wJvqqqvzvL+SJqGM9t77iLgZ5JclOQ3quoWYGOSd3QzL/fr2r05yduA503RxyFJzgWezI8KgV3ACuDxwCeAC6rqE7NsO7aqTgHeCazr2r6vqmb8oZHkiQx+uFwGvDjJAcB/qarfYXArIIDbgX0ZfBg/u1t3QpILuvXfZvCD6jeAkxj8MFgHXNzF9PPAvYA7gA8Af9/FfpmF9qLW+/zu/HkX272Bb0zT5j7dDPiJDMaC2lgqOXU2g8J3t98C/gO4GXg0UAwmsG4Bntu1+W5VnQ18C3gKg1y8HXgkg2L5Y8B7uxj/z0ihDbAduC3JowG62f8XArcBtwI/PdT2uVV1Utffbp+pqlcwKPIBfoxBkX0qg8/sk4H/UVW/xuAXU4Bru5jDYKLkHRba0t5xZnsPVdU3gd8DSPIXSd5RVZcBlyVZCzyra/rSqvp2kpO7ti9k8MH4BmB7Vf327j6TPLeq1iR5DbAfg1mJ3WbaViP/wuBD+IeGj1tVN3arXwzcB/hJ4EjgHAY/AAC+1/37Owx+GB0FPLVb9/5uZnsd8AsMPoyHj32n5aranuQ0BsX6McCX0KK2RPIb4JPAR7rv5flM7TtV9dKZ3g/tvSWUU19j8Bl2JPCvDCar3lNV13T7ncCgUL00ycdG+r4HsLWqzho5zlOBS4BXT/3uAfBHwO4iPMCNI/08e6T9VN9bjWzf/TX8mV3D+1TVFUk+B7wsyZ9V1ceQNCcW23soyS8BxzKYObkWuH+SNzCY6X0w8Cp+VJz+UFVdClza9TG6+atJXg0cAXwc2Ay8KsnFs2z7SJK3MJgheRmwfrTj4eN2x74P8MCqek63fBqDgvqrSV7BYOZoG/B3wB8wKMpv7Xb/5SSP6L7PlwE7gbd1294KfB44p/vw/1g3G/MSBjPcf8PgNJPfS7JPVX1guvdYC6fv+T3iZ6rq+0mGi+03J9nF9KeWaJ4tsZw6B7geeDeDAvh1Sb7KYOb6PcDZSR4CLBvpc2uSHyR5E4O/trwOOIPBzPjnGZyq8ZNJXl5VbxrZ9xtJPgv8QlV9K4Pz1/8Xg0L54qGmVyR5O4MJk1umif/7DE49WQW8nsHpI3+Y5LvceUacJM8BnggcAOyYpj9JY/ChNpIk9VySnwZOAH4ceH1VfX6BQ5LUsdiWJEmSGvECSUmSJKkRi21JkiSpEYttSZIkqRGLbUmSJKmR3t3676CDDqqVK1cudBjquc9+9rNfr6rls7ecP+au9pZ5q74yd9VX85G7vSu2V65cyZYtWxY6DPVcki9P+pjmrvaWeau+MnfVV/ORu55GIkmSJDVisS1JkiQ1YrEtSZIkNWKxLUmSJDXSuwsk77bOOqBh37e161uSJOluzGK7LyyIJUmSesdiW5KkUf41UdI8sdiWJGmUBbGkeeIFkpIkSVIjFtuSJElSIxbbkiRJUiOesy1p4pLsUfuqahSJJEltWWxLmrjpiuckFtaSpCXF00gkSZKkRpoW20mOS3J9km1JTp9i+0OTfCTJNUn+NsnBLeORJEmSJqlZsZ1kGXA+8AzgcGBdksNHmr0RuLSqHgNsAF7fKh5JkiRp0lrObB8BbKuqG6rqduBy4PiRNocDH+lef2yK7ZIkSVJvtSy2VwDbh5Z3dOuGXQ2c0L1+FrB/kgeOdpTkpCRbkmzZuXNnk2ClFsxd9ZF5q74yd7UYtSy2p7q31+htBl4J/GySzwE/C9wI7LrLTlUXVtXqqlq9fPny+Y9UasTchQMPPJAkY30BY7U78MADF/i7WtrMW/WVuavFqOWt/3YAhwwtHwzcNNygqm4Cng2Q5L7ACVV1W8OYJE3YrbfeOu+389vT+3RLkrRQWs5sbwZWJTksyb7AWmDjcIMkByXZHcMZwMUN45EkSZImqlmxXVW7gPXAVcB1wBVVtTXJhiRrumZHA9cn+TzwYOC1reKRJEmSJq3pEySrahOwaWTdmUOvrwSubBmDJEmStFB8gqQkSZLUiMW2JEmS1IjFtiRJktSIxbYkSZLUiMW2JEmS1EjTu5FIUr3mfnDWAfPfpyRJPWCxLamp/ME3mzxBss6a1y4lSWrC00gkSZKkRiy2JUmSpEYstiVJkqRGLLYlSZKkRiy2JUmSpEaaFttJjktyfZJtSU6fYvuhST6W5HNJrknyzJbxSJIkSZPUrNhOsgw4H3gGcDiwLsnhI81+H7iiqh4PrAUuaBWPJEmSNGktZ7aPALZV1Q1VdTtwOXD8SJsCdj+d4gDgpobxSJIkSRPV8qE2K4DtQ8s7gCNH2pwFfDjJqcB9gJ9vGI8kSZI0US1ntjPFutHHyK0DLqmqg4FnAu9JcpeYkpyUZEuSLTt37mwQqtSGuas+Mm/VV+auFqOWxfYO4JCh5YO562kiLwGuAKiqTwH3Ag4a7aiqLqyq1VW1evny5Y3Cleafuas+Mm/VV+auFqOWxfZmYFWSw5Lsy+ACyI0jbb4C/BxUt6RXAAAQkElEQVRAkkcyKLb9VVSSJElLQrNiu6p2AeuBq4DrGNx1ZGuSDUnWdM1eAfx6kquB9wInVtXoqSaSJElSL7W8QJKq2gRsGll35tDra4Ent4xBkiRJWihNi21JAkimul567h7wgAfMa3+SJLVisS2pqT05MyzJHrWXJGmxa/q4dkmSJOnubOxiO8l9WgYi9ZHjQn1k3krS5MxabCc5Ksm1DO4oQpLHJrmgeWTSIua4UB+Zt5I0eePMbJ8DHAvcDFBVVwNPaRmU1AOOC/WReStJEzbWaSRVtX1k1R0NYpF6xXGhPjJvJWmyxrkbyfYkRwHVPQnyNLo/QUp3Y44L9ZF5K0kTNs7M9snAKcAKYAfwuG5ZujtzXKiPzFtJmrAZZ7aTLANeUFW/OqF4pEXPcaE+Mm8laWHMOLNdVXcAx08oFqkXHBfqI/NWkhbGOOdsfzLJecD7gO/sXllV/9gsKmnxc1yoj8xbSZqwcYrto7p/NwytK+Bps+2Y5DjgXGAZ8M6qOntk+znAU7vF/YAHVdX9x4hJWmhzHhfSAjJvJWnCZi22q+qps7WZSnd+4PnAMQwuxNmcZGNVXTvU98uG2p8KPH4ux5Imba7jQlpI5q0kTd6sxXaSM6daX1Ubplo/5AhgW1Xd0PVzOYPzBa+dpv064DWzxSMtBnsxLqQFY95K0uSNc+u/7wx93QE8A1g5xn4rgOGHJ+zo1t1FkocChwEfHaNfaTGY67iQFpJ5K0kTNs5pJH88vJzkjcDGMfrOVN1N03YtcGV3tfxdO0pOAk4COPTQQ8c4tNTWuOPC3NViYt5qqTN3tRiN9bj2EfsBDxuj3Q7gkKHlg4Gbpmm7FnjvdB1V1YVVtbqqVi9fvnzsQKUJmnJcmLta5MxbLSnmrhajcc7Z/md+NCO9DFjOna9kn85mYFWSw4AbGRTUz5ui/0cADwA+NWbM0oLbi3EhLRjzVpImb5xb//3C0OtdwL9X1a7ZdqqqXUnWA1cx+FC/uKq2JtkAbKmq3X+6XAdcXlXTnWIiLUZzGhfSAjNvJWnCxim29wF2VNX3khwNnJDk0qr6xmw7VtUmYNPIujNHls8aP1xp0ZjzuJAWkHkrSRM2zjnb7wfuSPKTwEUM7hpyWdOopMXPcaE+Mm8lacLGKbZ/0P2Z8dnAm7sH0TykbVjSoue4UB+Zt5I0YeMU299Psg54IfChbt2PtQtJ6gXHhfrIvJWkCRun2H4x8CTgtVX1pe7uIn/SNixp0XNcqI/MW0masHEeanMtcNrQ8peAs1sGJS12jgv1kXkrSZM3zn22VwGvBw4H7rV7fVWN82AbaUlyXKiPzFtJmrxxTiN5F/BWBvdkfSpwKfCelkFJPeC4UB+Zt5I0YeMU2/euqo8Aqaovd/fFflrbsKRFz3GhPjJvJWnCxnmozX8muQfwhe6JkDcCD2oblrToOS7UR+atJE3YODPbLwX2Y3BRzROA5wMvahmU1AOOC/WReStJEzbO3Ug2AySpqnpx+5Ckxc9xoT4ybyVp8mad2U7ypCTXAtd1y49NckHzyKRFzHGhPjJvJWnyxjmN5M3AscDNAFV1NfCUlkFJPeC4UB+Zt5I0YeMU21TV9pFVd4yzX5LjklyfZFuS06dp89wk1ybZmuSycfqVFoO5jgtpIZm3kjRZ49yNZHuSo4BKsi+DC2uum22nJMuA84FjgB3A5iQbuyeY7W6zCjgDeHJV3ZrEq+LVF3MaFxpIskfbqqplOHcn5q0kTdg4M9snA6cAKxgUzY/rlmdzBLCtqm6oqtuBy4HjR9r8OnB+Vd0KUFVfGzdwaYHNdVyIQfG8J1+aN+atJE3YOHcj+Trwq3PoewUw/OfKHcCRI20eDpDkk8Ay4Kyq+qvRjpKcBJwEcOihh84hFGl+jTsuzF0tJuatljpzV4vRrMV2ksOAU4GVw+2ras1su06xbnSKah9gFXA0cDDw90keVVXfuNNOVRcCFwKsXr3aaS4tuHHHhbmrxcS81VJn7moxGuec7Q8AFwEfBH6wB33vAA4ZWj4YuGmKNp+uqu8DX0pyPYPie/MeHEdaCHMdF9JCMm8lacLGelx7Vb1lDn1vBlZ1Myk3AmuB5420+QCwDrgkyUEMTiu5YQ7HkiZtruNCWkjmrSRN2DjF9rlJXgN8GPje7pVV9Y8z7VRVu5KsB65icD72xVW1NckGYEtVbey2Pb17yMIdwKuq6uY5fi/SJM1pXEgLzLyVpAkbp9h+NPAC4Gn86M+O1S3PqKo2AZtG1p059LqAl3dfUp/MeVxIC8i8laQJG6fYfhbwsO72fZIGHBfqI/NWkiZsnPtsXw3cv3UgUs84LtRH5q0kTdg4M9sPBv41yWbufI7fbLf+k5Yyx4X6yLyVpAkbp9h+TfMopP5xXKiPzFtJmrBxniD58UkEIvWJ40J9ZN5K0uSNc862JEmSpDmw2JYkSZIambbYTnLoJAOR+sBxoT4ybyVp4cw0s/2B3S+SvH8CsUh94LhQH5m3krRAZiq2M/T6Ya0DkXrCcaE+Mm8laYHMVGzXNK+luzPHhfrIvJWkBTLTrf8em+SbDGZE7t29pluuqrpf8+ikxcdxoT4ybyVpgUxbbFfVsr3tPMlxwLnAMuCdVXX2yPYTgTcAN3arzquqd+7tcaVW5mNcSJNm3krSwhnnCZJzkmQZcD5wDLAD2JxkY1VdO9L0fVW1vlUckiRJ0kJpeZ/tI4BtVXVDVd0OXA4c3/B4kiRJ0qLSstheAWwfWt7RrRt1QpJrklyZ5JCG8UiSJEkT1bLYzhTrRq+C/yCwsqoeA/wN8O4pO0pOSrIlyZadO3fOc5hSO+au+si8VV+Zu1qMWhbbO4DhmeqDgZuGG1TVzVX1vW7xHcATpuqoqi6sqtVVtXr58uVNgpVaMHfVR+at+src1WLUstjeDKxKcliSfYG1wMbhBkkeMrS4BriuYTySJEnSRDW7G0lV7UqyHriKwa3/Lq6qrUk2AFuqaiNwWpI1wC7gFuDEVvFIkiRJk9as2Aaoqk3AppF1Zw69PgM4o2UMkiRJ0kJpeRqJJEmSdLdmsS1JkiQ1YrEtSZIkNWKxLUmSJDVisS1JkiQ1YrEtSZIkNWKxLUmSJDVisS1JkiQ1YrEtSZIkNWKxLUmSJDVisS1JkiQ1YrEtSZIkNWKxLUmSJDXStNhOclyS65NsS3L6DO2ek6SSrG4ZjyRJkjRJzYrtJMuA84FnAIcD65IcPkW7/YHTgM+0ikWSJElaCC1nto8AtlXVDVV1O3A5cPwU7f4Q+CPgPxvGIkmSJE1cy2J7BbB9aHlHt+6HkjweOKSqPjRTR0lOSrIlyZadO3fOf6RSI+au+si8VV+Zu1qMWhbbmWJd/XBjcg/gHOAVs3VUVRdW1eqqWr18+fJ5DFFqy9xVH5m36itzV4tRy2J7B3DI0PLBwE1Dy/sDjwL+Nsm/AU8ENnqRpCRJkpaKlsX2ZmBVksOS7AusBTbu3lhVt1XVQVW1sqpWAp8G1lTVloYxSZIkSRPTrNiuql3AeuAq4DrgiqrammRDkjWtjitJkiQtFvu07LyqNgGbRtadOU3bo1vGIkmSJE2aT5CUJEmSGrHYliRJkhqx2JYkSZIasdiWJEmSGrHYliRJkhqx2JYkSZIasdiWJEmSGrHYliRJkhqx2JYkSZIasdiWJEmSGrHYliRJkhqx2JYkSZIaaVpsJzkuyfVJtiU5fYrtJyf55yT/lOQTSQ5vGY8kSZI0Sc2K7STLgPOBZwCHA+umKKYvq6pHV9XjgD8C3tQqHkmSJGnSWs5sHwFsq6obqup24HLg+OEGVfXNocX7ANUwHkmSJGmiWhbbK4DtQ8s7unV3kuSUJF9kMLN92lQdJTkpyZYkW3bu3NkkWKkFc1d9ZN6qr8xdLUapajOZnOSXgWOr6te65RcAR1TVqdO0f17X/kWz9LsT+PJ8x7vEHAR8faGDWOQeWlXLJ3lAc3cs5u7MzNvFybyd3ULk7reA6yd5zDEstlwxntk9oqr235sO9pmvSKawAzhkaPlg4KYZ2l8OvHW2Tic9WPsoyZaqWr3QcejOzN3ZmbuLj3k7O/N20bp+sf2/LLZcMZ7ZJdmyt320PI1kM7AqyWFJ9gXWAhuHGyRZNbT434AvNIxHkiRJmqhmM9tVtSvJeuAqYBlwcVVtTbIB2FJVG4H1SX4e+D5wKzDjKSSSJElSn7Q8jYSq2gRsGll35tDr3255/LuxCxc6AGmOzF31kXm7OC3G/5fFFpPxzG6vY2p2gaQkSZJ0d+fj2iVJkqRGLLaXkCQXJ/lakn9Z6FikcZm36itzd+EkOS7J9Um2JTl9iu33TPK+bvtnkqwc2nZGt/76JMdOKJ6XJ7k2yTVJPpLkoUPb7kjyT93XxtF9G8VzYpKdQ8f9taFtL0ryhe5r3q6lGyOmc4bi+XySbwxta/EezTh+M/CWLt5rkvz00LY9eo88jWQJSfIU4NvApVX1qIWORxqHeau+MncXRpJlwOeBYxjcZngzsK6qrh1q81vAY6rq5CRrgWdV1a8kORx4L4OnXP848DfAw6vqjsbxPBX4TFV9N8lvAkdX1a90275dVfed6/HnGM+JwOqqWj+y74HAFmA1g6d6fxZ4QlXd2jqmkfanAo+vqv/eLc/re9T1OeP4TfJM4FTgmcCRwLlVdeRc3iNntpeQqvo74JaFjkPaE+at+srcXTBHANuq6oaqup3BczqOH2lzPPDu7vWVwM8lSbf+8qr6XlV9CdjW9dc0nqr6WFV9t1v8NINnj7QyzvsznWOBv66qW7ri8a+B4xYgpnUMfilqZozxezyDQryq6tPA/ZM8hDm8RxbbkiSpT1YA24eWd3TrpmxTVbuA24AHjrlvi3iGvQT4y6Hle3WPmP90kl/ay1j2JJ4TutMjrkyy+yGELd6fPeq3O8XmMOCjQ6vn+z0ax3Qx7/F71PTWf5IkSfMsU6wbPSd2ujbj7NsinkHD5PkMTj/42aHVh1bVTUkeBnw0yT9X1Rcbx/NB4L1V9b0kJzP4K8DTxty3VUy7rQWuHDm1Z77fo3HMWw45sy1JkvpkB3DI0PLBwE3TtUmyD3AAg1MGxtm3RTx0D/H7PWBNVX1v9/qquqn79wbgb4HHt46nqm4eiuEdwBPG3bdVTEPWMnIKSYP3aBzTxbzH75HFtiRJ6pPNwKokhyXZl0FxNnqHio386KnUzwE+WoM7QmwE1nZ3KzkMWAX8Q+t4kjweeDuDQvtrQ+sfkOSe3euDgCcDU140OM/xPGRocQ1wXff6KuDpXVwPAJ7erdtb4/yfkeQRwAOATw2ta/EejWMj8MLuriRPBG6rqq8yh/fI00iWkCTvBY4GDkqyA3hNVV20sFFJMzNv1Vfm7sKoql1J1jMocJYBF1fV1iQbgC1VtRG4CHhPkm0MZrTXdvtuTXIFg2JtF3DK3tyJZA/ieQNwX+DPBtdp8pWqWgM8Enh7kh8wmAA9e7o7dMxzPKclWcPgPbgFOLHb95Ykf8igOAbYUFV7fRHwmDHB4MLIy+vOt8qb9/cIph6/wI918b6NwRPQn8ngItrvAi/utu3xe+St/yRJkqRGPI1EkiRJasRiW5IkSWrEYluSJElqxGJbkiRJasRiW5IkSWrEYluSJElqxGJbkiRJasRiW5IkSWrk/wdblCZPSRRMgQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "File              ML algorithm       accuracy        Precision       Recall          F1-score        Time           \n",
      "Web Attack        Naive Bayes        0.73            0.86            0.73            0.74            0.0044         \n",
      "Web Attack        QDA                0.84            0.89            0.84            0.84            0.0042         \n",
      "Web Attack        Random Forest      0.97            0.97            0.97            0.97            0.0317         \n",
      "Web Attack        ID3                0.97            0.97            0.97            0.97            0.0097         \n",
      "Web Attack        AdaBoost           0.97            0.97            0.97            0.97            0.1716         \n",
      "Web Attack        MLP                0.64            0.63            0.64            0.6             0.1182         \n",
      "Web Attack        Nearest Neighbors  0.93            0.94            0.93            0.93            0.0174         \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "mission accomplished!\n",
      "Total operation time: =  3601.525671482086 seconds\n"
     ]
    }
   ],
   "source": [
    "##  \"all_data.csv\" file is required for the operation of the program.\n",
    "##  \"all_data.csv\" file must be located in the same directory as the program.\n",
    "\n",
    "##  the purpose of this program is to apply machine learning algorithms to the dataset and observe the performance of algorithms.\n",
    "##  the algorithms used are:Naive Bayes, QDA, Random Forest, ID3, AdaBoost, MLP, Nearest Neighbors\n",
    "##  As the program display output data include: file name, machine learning algorithm name, accuracy,Precision, Recall, F1-score,Time\n",
    "##  the program will create a CSV file that prints the results and a folder containing graphics.\n",
    "\n",
    "##  the some codes parts used for calculation and graphing are taken from the following site.\n",
    "##  http://scikit-learn.org\n",
    "\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis as QDA\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.metrics import average_precision_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import precision_score\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import os\n",
    "import pandas as pd\n",
    "import csv\n",
    "import time\n",
    "import warnings\n",
    "import math\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "\n",
    "result=\"./results/results_1.csv\" #a CSV file is named in which the results are saved.\n",
    "csv_files=os.listdir(\"attacks\")# CSV files names: #The names of the files in the attacks folder are taken and assigned to a list (csv_files).\n",
    "path=\".\\\\attacks\\\\\"\n",
    "repetition=10\n",
    "\n",
    "\n",
    "def folder(f_name): #this function creates a folder named \"results\" and \"result_graph_1\" in the program directory.\n",
    "    try:\n",
    "        if not os.path.exists(f_name):\n",
    "            os.makedirs(f_name)\n",
    "    except OSError:\n",
    "        print (\"The folder could not be created!\")\n",
    "\n",
    "folder_name=\"./results/\"\n",
    "folder(folder_name)\n",
    "folder_name=\"./results/result_graph_1/\"\n",
    "folder(folder_name)\n",
    "\n",
    "\n",
    "#The machine learning algorithms to be used are defined in a dictionary (ml_list).\n",
    "ml_list={\n",
    "\"Naive Bayes\":GaussianNB(),\n",
    "\"QDA\":QDA(),\n",
    "\"Random Forest\":RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),\n",
    "\"ID3\" :DecisionTreeClassifier(max_depth=5,criterion=\"entropy\"),\n",
    "\"AdaBoost\":AdaBoostClassifier(),\n",
    "\"MLP\":MLPClassifier(hidden_layer_sizes=(13,13,13),max_iter=500),\n",
    "\"Nearest Neighbors\":KNeighborsClassifier(3)}\n",
    "\n",
    "\n",
    "\n",
    "# the features to be used for each attack type is defined in a dictionary(features).\n",
    "# the first 4 of the features created by the file \"04_1_feature_selection_for_attack_files.py\" are used here.\n",
    "features={\"Bot\":[\"Bwd Packet Length Mean\",\"Flow IAT Max\",\"Flow Duration\",\"Flow IAT Min\",\"Label\"],\n",
    "\"DDoS\":[\"Bwd Packet Length Std\",\"Total Backward Packets\",\"Fwd IAT Total\",\"Flow Duration\",\"Label\"],\n",
    "\"DoS GoldenEye\":[\"Flow IAT Max\",\"Bwd Packet Length Std\",\"Flow IAT Min\",\"Total Backward Packets\",\"Label\"],\n",
    "\"DoS Hulk\":[\"Bwd Packet Length Std\",\"Fwd Packet Length Std\",\"Fwd Packet Length Max\",\"Flow IAT Min\",\"Label\"],\n",
    "\"DoS Slowhttptest\":[\"Flow IAT Mean\",\"Fwd Packet Length Min\",\"Bwd Packet Length Mean\",\"Total Length of Bwd Packets\",\"Label\"],\n",
    "\"DoS slowloris\":[\"Flow IAT Mean\",\"Total Length of Bwd Packets\",\"Bwd Packet Length Mean\",\"Total Fwd Packets\",\"Label\"],\n",
    "\"FTP-Patator\":[\"Fwd Packet Length Max\",\"Fwd Packet Length Std\",\"Fwd Packet Length Mean\",\"Bwd Packet Length Std\",\"Label\"],\n",
    "\"Heartbleed\":[\"Total Backward Packets\",\"Fwd Packet Length Max\",\"Flow IAT Min\",\"Bwd Packet Length Max\",\"Label\"],\n",
    "\"Infiltration\":[\"Fwd Packet Length Max\",\"Fwd Packet Length Mean\",\"Flow Duration\",\"Total Length of Fwd Packets\",\"Label\"],\n",
    "\"PortScan\":[\"Flow Bytes/s\",\"Total Length of Fwd Packets\",\"Fwd IAT Total\",\"Flow Duration\",\"Label\"],\n",
    "\"SSH-Patator\":[\"Fwd Packet Length Max\",\"Flow Duration\",\"Flow IAT Max\",\"Total Length of Fwd Packets\",\"Label\"],\n",
    "\"Web Attack\":[\"Bwd Packet Length Std\",\"Total Length of Fwd Packets\",\"Flow Bytes/s\",\"Flow IAT Max\",\"Label\"]}\n",
    "\n",
    "seconds=time.time()#time stamp for all processing time\n",
    "\n",
    "\n",
    "\n",
    "with open(result, \"w\", newline=\"\",encoding=\"utf-8\") as f:#a CSV file is created to save the results obtained.\n",
    "    wrt = csv.writer(f)\n",
    "    wrt.writerow([\"File\",\"ML algorithm\",\"accuracy\",\"Precision\", \"Recall\" , \"F1-score\",\"Time\"])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "for j in csv_files: #this loop runs on the list containing the filenames.Operations are repeated for all attack files\n",
    "    print ('%-17s %-17s  %-15s %-15s %-15s %-15s %-15s' % (\"File\",\"ML algorithm\",\"accuracy\",\"Precision\", \"Recall\" , \"F1-score\",\"Time\"))# print output header\n",
    "    a=[]\n",
    "    \n",
    "    feature_list=list(features[j[0:-4]])\n",
    "    df=pd.read_csv(path+j,usecols=feature_list)#read an attack file.\n",
    "    df=df.fillna(0)\n",
    "    attack_or_not=[]\n",
    "    for i in df[\"Label\"]: #it changes the normal label to \"1\" and the attack tag to \"0\" for use in the machine learning algorithm\n",
    "        \n",
    "        if i ==\"BENIGN\":\n",
    "            attack_or_not.append(1)\n",
    "        else:\n",
    "            attack_or_not.append(0)           \n",
    "    df[\"Label\"]=attack_or_not\n",
    "\n",
    "    \n",
    "    y = df[\"Label\"] #this section separates the label and the data into two separate pieces, as Label=y Data=X \n",
    "    del df[\"Label\"]\n",
    "    feature_list.remove('Label')\n",
    "    X = df[feature_list]\n",
    "\n",
    "    \n",
    "    for ii in ml_list: #this loop runs on the list containing the machine learning algorithm names. Operations are repeated for all the 7 algorithm\n",
    "        precision=[]\n",
    "        recall=[]\n",
    "        f1=[]\n",
    "        accuracy=[]\n",
    "        t_time=[]\n",
    "        for i in range(repetition): # This loop allows cross-validation and machine learning algorithm to be repeated 10 times\n",
    "            second=time.time()#time stamp for processing time\n",
    "\n",
    "            # cross-validation\n",
    "            X_train, X_test, y_train, y_test = train_test_split(X, y,#  data (X) and labels (y) are divided into 2 parts to be sent to the machine learning algorithm (80% train,%20 test). \n",
    "                test_size = 0.20, random_state = repetition)#  So, in total there are 4 tracks: training data(X_train), training tag (y_train), test data(X_test) and test tag(y_test).\n",
    "\n",
    "\n",
    "            #machine learning algorithm is applied in this section\n",
    "            clf = ml_list[ii]#choose algorithm from ml_list dictionary                                                                          \n",
    "            clf.fit(X_train, y_train)\n",
    "            predict =clf.predict(X_test)\n",
    "        \n",
    "            #makes \"classification report\" and assigns the precision, f-measure, and recall values.s.    \n",
    "                  \n",
    "            f_1=f1_score(y_test, predict, average='macro')\n",
    "            pr=precision_score(y_test, predict, average='macro')\n",
    "            rc=recall_score(y_test, predict, average='macro')\n",
    "\n",
    "            \n",
    "            \n",
    "            \n",
    "            \n",
    "            precision.append(float(pr))\n",
    "            recall.append(float(rc))\n",
    "            f1.append(float(f_1))\n",
    "            accuracy.append(clf.score(X_test, y_test))\n",
    "            t_time.append(float((time.time()-second)) )\n",
    "\n",
    "\n",
    "            \n",
    "        print ('%-17s %-17s  %-15s %-15s %-15s %-15s %-15s' % (j[0:-4],ii,str(round(np.mean(accuracy),2)),str(round(np.mean(precision),2)), \n",
    "            str(round(np.mean(recall),2)),str(round(np.mean(f1),2)),str(round(np.mean(t_time),4))))#the result of the ten repetitions is printed on the screen.\n",
    "\n",
    "        with open(result, \"a\", newline=\"\",encoding=\"utf-8\") as f: # all the values found are saved in the opened file.\n",
    "            wrt = csv.writer(f)\n",
    "            for i in range(0,len(t_time)):\n",
    "                wrt.writerow([j[0:-4],ii,accuracy[i],precision[i],recall[i],f1[i],t_time[i]])#file name, algorithm name, precision, recall and f-measure are writed in CSV file\n",
    "        a.append(f1)\n",
    "\n",
    "\n",
    "     # In this section, Box graphics are created for the results of machine learning algorithms and saved in the feaure_graph folder.\n",
    "\n",
    "     \n",
    "    ml=[\"Naive Bayes\",\"QDA\",\"Random Forest\",\"ID3\",\"AdaBoost\",\"MLP\",\"Nearest Neighbors\"]\n",
    "    temp=0\n",
    "    fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(12, 6), sharey=True)\n",
    "    for c in range(2):\n",
    "        for b in range(4):\n",
    "            axes[c, b].boxplot(a[temp] )\n",
    "            axes[c, b].set_title(str(j[0:-4])+\" - \"+str(ml[temp]),fontsize=7)\n",
    "            axes[c, b].set_ylabel((\"F measure\"))\n",
    "            temp+=1\n",
    "            if temp==7:\n",
    "                break\n",
    "        if temp==7:\n",
    "            break\n",
    "    plt.savefig(folder_name+j[0:-4]+\".pdf\",bbox_inches='tight', papertype = 'a4', orientation = 'portrait', format = 'pdf')\n",
    "    plt.show()\n",
    "    print(\"\\n------------------------------------------------------------------------------------------------------\\n\\n\")\n",
    "    \n",
    "print(\"mission accomplished!\")\n",
    "print(\"Total operation time: = \",time.time()- seconds ,\"seconds\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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