Gradient_Boosting_Machine.ipynb

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  {
   "cell_type": "markdown",
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   "source": [
    "This Notebook will use a few machine learning algorithms to evaluate the KDD-99 dataset and then another one to Ensemble the methods together to hopefully create a better model than any single model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kdd = pd.read_csv(\"data/kddcup_data_10_percent\")\n",
    "protocol_types = pd.unique(kdd.protocol_type.ravel())\n",
    "protocol_dict = dict(zip(protocol_types,range(protocol_types.size)))\n",
    "\n",
    "service_types = pd.unique(kdd.service.ravel())\n",
    "service_dict = dict(zip(service_types,range(service_types.size)))\n",
    "\n",
    "flag_types = pd.unique(kdd.flag.ravel())\n",
    "flag_dict = dict(zip(flag_types,range(flag_types.size)))\n",
    "classification_dict = {\"back.\": \"dos\",\"land.\":\"dos\",\"neptune.\": \"dos\", \"pod.\": \"dos\", \"smurf.\":\"dos\",\"teardrop.\":\"dos\",\"loadmodule.\":\"u2r\",\"buffer_overflow.\":\"u2r\",\"perl.\":\"u2r\",\"rootkit.\":\"u2r\",\"ftp_write.\":\"r2l\",\"guess_passwd.\":\"r2l\",\"imap.\":\"r2l\",\"multihop.\":\"r2l\",\"phf.\":\"r2l\",\"spy.\":\"r2l\",\"warezclient.\":\"r2l\",\"warezmaster.\":\"r2l\",\"ipsweep.\":\"probe\",\"nmap.\":\"probe\",\"portsweep.\":\"probe\",\"satan.\":\"probe\"}\n",
    "classification_dict_test = {\"back.\": \"dos\",\"land.\":\"dos\",\"neptune.\": \"dos\", \"pod.\": \"dos\", \"smurf.\":\"dos\",\"teardrop.\":\"dos\",\"loadmodule.\":\"u2r\",\"buffer_overflow.\":\"u2r\",\"perl.\":\"u2r\",\"rootkit.\":\"u2r\",\"ftp_write.\":\"r2l\",\"guess_passwd.\":\"r2l\",\"imap.\":\"r2l\",\"multihop.\":\"r2l\",\"phf.\":\"r2l\",\"spy.\":\"r2l\",\"warezclient.\":\"r2l\",\"warezmaster.\":\"r2l\",\"ipsweep.\":\"probe\",\"nmap.\":\"probe\",\"portsweep.\":\"probe\",\"satan.\":\"probe\",\"snmpgetattack.\":\"dos\",\"apache2.\":\"dos\",\"udpstorm.\":\"dos\",\"processtable.\":\"dos\",\"mailbomb.\":\"dos\",\"xterm.\":\"u2r\",\"ps.\":\"u2r\",\"sqlattack.\":\"u2r\",\"named.\":\"r2l\",\"xlock.\":\"r2l\",\"xsnoop.\":\"r2l\",\"sendmail.\":\"r2l\",\"httptunnel.\":\"r2l\",\"worm.\":\"r2l\",\"saint.\":\"probe\",\"mscan.\":\"probe\",\"snmpguess.\":\"probe\"}\n",
    "\n",
    "\n",
    "kdd[\"protocol_type\"].replace(protocol_dict, inplace=True)\n",
    "kdd[\"service\"].replace(service_dict, inplace=True)\n",
    "kdd[\"flag\"].replace(flag_dict, inplace=True)\n",
    "kdd[\"classification\"].replace(classification_dict, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kdd_test = pd.read_csv(\"data/kddcup_test_data\")\n",
    "\n",
    "kdd_test[\"protocol_type\"].replace(protocol_dict, inplace=True)\n",
    "kdd_test[\"service\"].replace(service_dict, inplace=True)\n",
    "kdd_test[\"flag\"].replace(flag_dict, inplace=True)\n",
    "kdd_test[\"classification\"].replace(classification_dict_test, inplace=True)\n",
    "kdd_test[\"service\"].replace({\"icmp\":65}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "values = kdd.values\n",
    "kdd_X_train = values[:,:41]\n",
    "kdd_y_train = values[:,41]\n",
    "\n",
    "values_test = kdd_test.values\n",
    "kdd_X_test = values_test[:,:41]\n",
    "kdd_y_test = values_test[:,41]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "clf = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, max_depth=3, random_state=0)\n",
    "clf.fit(kdd_X_train,kdd_y_train)\n",
    "clf.predict(kdd_X_test,kdd_y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": []
  }
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