7_gan_decode_v2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "import math\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(58863, 21)\n",
      "(260391, 160)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.374719</td>\n",
       "      <td>0.242699</td>\n",
       "      <td>0.804956</td>\n",
       "      <td>0.233550</td>\n",
       "      <td>0.753552</td>\n",
       "      <td>0.161305</td>\n",
       "      <td>0.899202</td>\n",
       "      <td>0.471473</td>\n",
       "      <td>0.639400</td>\n",
       "      <td>0.082626</td>\n",
       "      <td>...</td>\n",
       "      <td>0.585408</td>\n",
       "      <td>0.393872</td>\n",
       "      <td>0.928697</td>\n",
       "      <td>0.461868</td>\n",
       "      <td>0.389630</td>\n",
       "      <td>0.020627</td>\n",
       "      <td>0.514851</td>\n",
       "      <td>0.879726</td>\n",
       "      <td>0.615675</td>\n",
       "      <td>TCP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.690727</td>\n",
       "      <td>0.289209</td>\n",
       "      <td>0.624222</td>\n",
       "      <td>0.787160</td>\n",
       "      <td>0.259145</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.844552</td>\n",
       "      <td>0.869673</td>\n",
       "      <td>0.398222</td>\n",
       "      <td>0.751319</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.432327</td>\n",
       "      <td>0.184115</td>\n",
       "      <td>0.275347</td>\n",
       "      <td>0.720820</td>\n",
       "      <td>0.338340</td>\n",
       "      <td>0.552738</td>\n",
       "      <td>0.503326</td>\n",
       "      <td>0.304589</td>\n",
       "      <td>0.0_d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.700514</td>\n",
       "      <td>0.424836</td>\n",
       "      <td>0.185812</td>\n",
       "      <td>0.616382</td>\n",
       "      <td>0.469560</td>\n",
       "      <td>0.492044</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.564201</td>\n",
       "      <td>0.792898</td>\n",
       "      <td>0.874603</td>\n",
       "      <td>...</td>\n",
       "      <td>0.329597</td>\n",
       "      <td>0.540815</td>\n",
       "      <td>0.640496</td>\n",
       "      <td>0.986693</td>\n",
       "      <td>0.467639</td>\n",
       "      <td>0.614137</td>\n",
       "      <td>0.588136</td>\n",
       "      <td>0.802519</td>\n",
       "      <td>0.292566</td>\n",
       "      <td>1_k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.229812</td>\n",
       "      <td>0.721760</td>\n",
       "      <td>0.847710</td>\n",
       "      <td>0.425830</td>\n",
       "      <td>0.227527</td>\n",
       "      <td>0.530943</td>\n",
       "      <td>0.329691</td>\n",
       "      <td>0.724129</td>\n",
       "      <td>0.800538</td>\n",
       "      <td>...</td>\n",
       "      <td>0.757808</td>\n",
       "      <td>0.089902</td>\n",
       "      <td>0.220831</td>\n",
       "      <td>0.921574</td>\n",
       "      <td>0.741282</td>\n",
       "      <td>0.982664</td>\n",
       "      <td>0.848599</td>\n",
       "      <td>0.535690</td>\n",
       "      <td>0.367750</td>\n",
       "      <td>443_p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.197396</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.483233</td>\n",
       "      <td>0.585403</td>\n",
       "      <td>0.172215</td>\n",
       "      <td>0.472147</td>\n",
       "      <td>0.425286</td>\n",
       "      <td>0.326074</td>\n",
       "      <td>0.365241</td>\n",
       "      <td>0.838361</td>\n",
       "      <td>...</td>\n",
       "      <td>0.956589</td>\n",
       "      <td>0.395134</td>\n",
       "      <td>0.096285</td>\n",
       "      <td>0.809280</td>\n",
       "      <td>0.605448</td>\n",
       "      <td>0.637512</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.906251</td>\n",
       "      <td>0.830612</td>\n",
       "      <td>80_p</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6  \\\n",
       "0  0.374719  0.242699  0.804956  0.233550  0.753552  0.161305  0.899202   \n",
       "1  0.690727  0.289209  0.624222  0.787160  0.259145  1.000000  0.844552   \n",
       "2  0.700514  0.424836  0.185812  0.616382  0.469560  0.492044  0.000000   \n",
       "3  0.000000  0.229812  0.721760  0.847710  0.425830  0.227527  0.530943   \n",
       "4  0.197396  0.000000  0.483233  0.585403  0.172215  0.472147  0.425286   \n",
       "\n",
       "          7         8         9  ...        11        12        13        14  \\\n",
       "0  0.471473  0.639400  0.082626  ...  0.585408  0.393872  0.928697  0.461868   \n",
       "1  0.869673  0.398222  0.751319  ...  1.000000  0.432327  0.184115  0.275347   \n",
       "2  0.564201  0.792898  0.874603  ...  0.329597  0.540815  0.640496  0.986693   \n",
       "3  0.329691  0.724129  0.800538  ...  0.757808  0.089902  0.220831  0.921574   \n",
       "4  0.326074  0.365241  0.838361  ...  0.956589  0.395134  0.096285  0.809280   \n",
       "\n",
       "         15        16        17        18        19  values  \n",
       "0  0.389630  0.020627  0.514851  0.879726  0.615675     TCP  \n",
       "1  0.720820  0.338340  0.552738  0.503326  0.304589   0.0_d  \n",
       "2  0.467639  0.614137  0.588136  0.802519  0.292566     1_k  \n",
       "3  0.741282  0.982664  0.848599  0.535690  0.367750   443_p  \n",
       "4  0.605448  0.637512  1.000000  0.906251  0.830612    80_p  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def loadfile(filename):\n",
    "    pickle_in = open(\"C:/Users/Akarsh/Downloads/DP_scripts/store_emb/\" + filename + \".pickle\",\"rb\")\n",
    "    return pickle.load(pickle_in)\n",
    "\n",
    "def save2file(filename, data):\n",
    "    pickle_out = open(\"C:/Users/Akarsh/Downloads/DP_scripts/store_emb/\" + filename + \".pickle\", \"wb\")\n",
    "    pickle.dump(data, pickle_out, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "    pickle_out.close()\n",
    "\n",
    "main_str = str(437)\n",
    "\n",
    "filename = \"ip2vec_emb_\" + main_str\n",
    "ip2vec_emb = loadfile(filename)\n",
    "print(ip2vec_emb.shape)\n",
    "\n",
    "# public wgan\n",
    "MODE = \"dp_wgan_gp\" #\"wgan_gp\"\n",
    "# filename = MODE + epch_str \n",
    "# filename = MODE + '_final_' + main_str\n",
    "filename =  \"dp_wgan_gp_epoch_9\"\n",
    "\n",
    "gan_emb = loadfile(filename)\n",
    "print(gan_emb.shape)\n",
    "display(ip2vec_emb.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ipToEmbed = dict();   portToEmbed = dict(); byteToEmbed = dict(); \n",
    "packToEmbed = dict(); durToEmbed = dict();  embSizeIP = -1;\n",
    "\n",
    "for row in ip2vec_emb.to_numpy():\n",
    "    key = row[-1]\n",
    "    embSizeIP = len(row) - 1 #21 cols\n",
    "    if key.count('.') > 2:\n",
    "        ipToEmbed[key] = np.asarray(row).reshape(1,-1)\n",
    "    elif \"_p\" in key:\n",
    "        key = key.split(\"_\")[0]\n",
    "        portToEmbed[key] = np.asarray(row).reshape(1,-1)\n",
    "    elif \"_k\" in key:\n",
    "        key = key.split(\"_\")[0]\n",
    "        packToEmbed[key] = np.asarray(row).reshape(1,-1)\n",
    "    elif \"_b\" in key:\n",
    "        key = key.split(\"_\")[0]\n",
    "        byteToEmbed[key] = np.asarray(row).reshape(1,-1)\n",
    "    elif \"_d\" in key:\n",
    "        key = key.split(\"_\")[0]\n",
    "        durToEmbed[key] = np.asarray(row).reshape(1,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ip addresses : (149, 20) (1, 149) ['192.168.220.15', '192.168.220.16', '192.168.210.5']\n",
      "port numbers : (33231, 20) (1, 33231) ['443', '80', '53']\n",
      "bytes : (12650, 20) (1, 12650) ['66', '55', '54']\n",
      "packets : (577, 20) (1, 577) ['1', '2', '3']\n",
      "duration : (3550, 20) (1, 3550) ['0.0', '0.003', '0.002']\n",
      "vocab size: 50157\n"
     ]
    }
   ],
   "source": [
    "def compute_norm(emb_dict, feature_ip, feature_name):\n",
    "    feature_emb = []; feature_label = []; comp_id = -1;\n",
    "    for k, v in emb_dict.items():\n",
    "        feature_label.append(k)\n",
    "        feature_emb.append(v[0][:20])\n",
    "        if feature_ip == 'yes':\n",
    "            if k == \"192.168.220.255\":\n",
    "                comp_id = len(feature_emb) - 1 # count broad/multi cast IP\n",
    "        else:\n",
    "            continue\n",
    "    emb_arr = np.reshape(np.array(feature_emb, dtype=np.float32) , (len(emb_dict), embSizeIP)) \n",
    "    norm_feature = np.ndarray(shape=(1, len(emb_dict)), dtype=np.float32)\n",
    "    for i in range(0, len(emb_arr)):\n",
    "        line = emb_arr[i]\n",
    "        res = 0\n",
    "        for value in line:\n",
    "            res += (value*value)\n",
    "        res = math.sqrt(res)\n",
    "        norm_feature[0][i] = res\n",
    "    print(feature_name, ':', emb_arr.shape, norm_feature.shape, feature_label[:3])\n",
    "    return emb_arr, norm_feature, feature_label\n",
    "\n",
    "embedding_ip, ip_norm, feature_label_ip = compute_norm(ipToEmbed, feature_ip='yes', feature_name='ip addresses')\n",
    "embedding_prt, prt_norm, feature_label_prt = compute_norm(portToEmbed, feature_ip='no', feature_name='port numbers')\n",
    "embedding_byt, byt_norm, feature_label_byt = compute_norm(byteToEmbed, feature_ip='no', feature_name='bytes')\n",
    "embedding_pkt, pkt_norm, feature_label_pkt = compute_norm(packToEmbed, feature_ip='no', feature_name='packets')\n",
    "embedding_dur, dur_norm, feature_label_dur = compute_norm(durToEmbed, feature_ip='no', feature_name='duration')\n",
    "print('vocab size:', (embedding_ip.shape[0] + embedding_prt.shape[0] + embedding_byt.shape[0] + embedding_pkt.shape[0] + embedding_dur.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# IP \n",
    "ip_row = tf.placeholder(tf.float32,shape=[1,embSizeIP])\n",
    "ip_mat = tf.placeholder(tf.float32,shape=[len(embedding_ip),embSizeIP])\n",
    "ip_nor = tf.placeholder(tf.float32,shape=[1,len(embedding_ip)])\n",
    "ip_sim = tf.matmul(ip_row,ip_mat,transpose_b=True)\n",
    "ip_res = tf.math.divide(ip_sim,ip_nor)\n",
    "\n",
    "# Ports\n",
    "pt_row = tf.placeholder(tf.float32,shape=[1,embSizeIP])\n",
    "pt_mat = tf.placeholder(tf.float32,shape=[len(embedding_prt),embSizeIP])\n",
    "pt_nor = tf.placeholder(tf.float32,shape=[1,len(embedding_prt)])\n",
    "pt_sim = tf.matmul(pt_row,pt_mat,transpose_b=True)\n",
    "pt_res = tf.math.divide(pt_sim,pt_nor)\n",
    "\n",
    "# Byte\n",
    "by_row = tf.placeholder(tf.float32,shape=[1,embSizeIP])\n",
    "by_mat = tf.placeholder(tf.float32,shape=[len(embedding_byt),embSizeIP]) \n",
    "by_nor = tf.placeholder(tf.float32,shape=[1,len(embedding_byt)]) \n",
    "by_sim = tf.matmul(by_row,by_mat,transpose_b=True) \n",
    "by_res = tf.math.divide(by_sim,by_nor)\n",
    "\n",
    "# Packets\n",
    "pk_row = tf.placeholder(tf.float32,shape=[1,embSizeIP]) \n",
    "pk_mat = tf.placeholder(tf.float32,shape=[len(embedding_pkt),embSizeIP])\n",
    "pk_nor = tf.placeholder(tf.float32,shape=[1,len(embedding_pkt)])\n",
    "pk_sim = tf.matmul(pk_row,pk_mat,transpose_b=True)\n",
    "pk_res = tf.math.divide(pk_sim,  pk_nor)\n",
    "\n",
    "# Duration\n",
    "du_row = tf.placeholder(tf.float32,shape=[1,embSizeIP])\n",
    "du_mat = tf.placeholder(tf.float32,shape=[len(embedding_dur),embSizeIP])\n",
    "du_nor = tf.placeholder(tf.float32,shape=[1,len(embedding_dur)])\n",
    "du_sim = tf.matmul(du_row, du_mat,transpose_b=True) \n",
    "du_res = tf.math.divide(du_sim, du_nor)\n",
    "\n",
    "sess = tf.Session()\n",
    "init = tf.global_variables_initializer()\n",
    "\n",
    "def findIP(vec):\n",
    "    res = sess.run([ip_res], feed_dict = {ip_row: vec, ip_mat: embedding_ip, ip_nor: ip_norm})\n",
    "    return feature_label_ip[ res[0].argmax() ]\n",
    "\n",
    "def findPt(vec):\n",
    "    res = sess.run([pt_res], feed_dict = {pt_row: vec, pt_mat: embedding_prt, pt_nor: prt_norm})\n",
    "    return feature_label_prt[ res[0].argmax() ]\n",
    "\n",
    "def findBy(vec):\n",
    "    res = sess.run([by_res], feed_dict = {by_row: vec, by_mat: embedding_byt, by_nor: byt_norm})\n",
    "    return feature_label_byt[ res[0].argmax() ]\n",
    "\n",
    "def findPk(vec):\n",
    "    res = sess.run([pk_res], feed_dict = {pk_row: vec, pk_mat: embedding_pkt, pk_nor: pkt_norm})\n",
    "    return feature_label_pkt[ res[0].argmax() ]\n",
    "\n",
    "def findDu(vec):\n",
    "    res = sess.run([du_res], feed_dict = {du_row: vec, du_mat: embedding_dur, du_nor: dur_norm})\n",
    "    return feature_label_dur[ res[0].argmax() ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read lines:  50000\n",
      "Read lines:  100000\n",
      "Read lines:  150000\n",
      "Read lines:  200000\n",
      "Read lines:  250000\n"
     ]
    }
   ],
   "source": [
    "counter_read = 0; decoded_gan = []\n",
    "for row in gan_emb:\n",
    "    counter_read += 1\n",
    "    if counter_read % 50000 == 0:\n",
    "        print(\"Read lines: \", str(counter_read))\n",
    "    rl = []\n",
    "    d_id = np.array(row[0:7],dtype=np.float32).argmax()\n",
    "    rl.append(d_id)\n",
    "\n",
    "    time = (float( row[7] )) * 86400\n",
    "    if time < 0:\n",
    "        time += 86400 \n",
    "    if time > 86399: \n",
    "        time = time % 86400\n",
    "    h = int( time / 3600 ) \n",
    "    m = int( (time-(h*3600)) / 60 ) \n",
    "    s = round( (time-(h*3600)-(m*60)) ) \n",
    "    rl.append(str(h)+\":\"+str(m)+\":\"+str(s))\n",
    "    \n",
    "    # Duration\n",
    "    du = row[8:8+embSizeIP]\n",
    "    rl.append(findDu( np.array(du).reshape(1,embSizeIP)) )\n",
    "\n",
    "    # Transport Protocol\n",
    "    p_id = np.array(row[8+embSizeIP:8+embSizeIP+3],dtype=np.float32).argmax() \n",
    "    if p_id == 0: \n",
    "        rl.append(\"ICMP\")\n",
    "    if p_id == 1: \n",
    "        rl.append(\"UDP\")\n",
    "    if p_id == 2: \n",
    "        rl.append(\"TCP\")\n",
    "    \n",
    "    sip = row[11+embSizeIP:11+2*embSizeIP]\n",
    "    rl.append(findIP( np.array(sip).reshape(1,embSizeIP)) ) \n",
    "\n",
    "    sp = row[11+2*embSizeIP:11+3*embSizeIP]\n",
    "    rl.append(findPt( np.array(sp).reshape(1,embSizeIP)) ) \n",
    "\n",
    "    dip = row[11+3*embSizeIP:11+4*embSizeIP]\n",
    "    rl.append(findIP( np.array(dip).reshape(1,embSizeIP)) ) \n",
    "\n",
    "    dp = row[11+4*embSizeIP:11+5*embSizeIP]\n",
    "    rl.append(findPt( np.array(dp).reshape(1,embSizeIP))) \n",
    "\n",
    "    pk = row[11+5*embSizeIP:11+6*embSizeIP]\n",
    "    rl.append(findPk( np.array(pk).reshape(1,embSizeIP)) ) \n",
    "\n",
    "    by = row[11+6*embSizeIP:11+7*embSizeIP]\n",
    "    rl.append(findBy( np.array(by).reshape(1,embSizeIP)) ) \n",
    "\n",
    "    # Read the flags \n",
    "    synFlag     = ( float( row[11+7*embSizeIP+0] ) )\n",
    "    ackFlag     = ( float( row[11+7*embSizeIP+1] ) )\n",
    "    finFlag     = ( float( row[11+7*embSizeIP+2] ) )\n",
    "    urgentFlag  = ( float( row[11+7*embSizeIP+3] ) )\n",
    "    pushFlag    = ( float( row[11+7*embSizeIP+4] ) )\n",
    "    resetFlag   = ( float( row[11+7*embSizeIP+5] ) )\n",
    "    flags = \"\" \n",
    "    if urgentFlag > 0.5:\n",
    "        flags += \"U\"\n",
    "    else:\n",
    "        flags += \".\"\n",
    "    if ackFlag > 0.5:\n",
    "        flags += \"A\" \n",
    "    else: \n",
    "        flags += \".\" \n",
    "    if pushFlag > 0.5:  \n",
    "        flags += \"P\" \n",
    "    else: \n",
    "        flags += \".\" \n",
    "    if resetFlag > 0.5:  \n",
    "        flags += \"R\" \n",
    "    else: \n",
    "        flags += \".\" \n",
    "    if synFlag > 0.5: \n",
    "        flags += \"S\" \n",
    "    else: \n",
    "        flags += \".\"\n",
    "    if finFlag > 0.5:  \n",
    "        flags += \"F\" \n",
    "    else: \n",
    "        flags += \".\"\n",
    "    rl.append(flags)   \n",
    "\n",
    "    # clazz \n",
    "    normal = 0.0; attacker = 0.0; victim = 0.0\n",
    "    c_id = np.array(row[11+7*embSizeIP+6:11+7*embSizeIP+6+3], dtype=np.float32).argmax() \n",
    "    if c_id == 0:\n",
    "        rl.append(\"normal\")\n",
    "    if c_id == 1: \n",
    "        rl.append(\"attacker\")\n",
    "    if c_id == 2: \n",
    "        rl.append(\"victim\")\n",
    "    decoded_gan.append(rl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Proto</th>\n",
       "      <th>Src IP Addr</th>\n",
       "      <th>Src Pt</th>\n",
       "      <th>Dst IP Addr</th>\n",
       "      <th>Dst Pt</th>\n",
       "      <th>Packets</th>\n",
       "      <th>Bytes</th>\n",
       "      <th>Flags</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>10:35:34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TCP</td>\n",
       "      <td>192.168.220.255</td>\n",
       "      <td>80</td>\n",
       "      <td>192.168.220.14</td>\n",
       "      <td>48311</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>.A....</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>10:49:56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TCP</td>\n",
       "      <td>192.168.200.1</td>\n",
       "      <td>443</td>\n",
       "      <td>192.168.200.4</td>\n",
       "      <td>53991</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>.A....</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>15:24:33</td>\n",
       "      <td>0.042</td>\n",
       "      <td>TCP</td>\n",
       "      <td>192.168.220.6</td>\n",
       "      <td>41724</td>\n",
       "      <td>192.168.220.2</td>\n",
       "      <td>80</td>\n",
       "      <td>12</td>\n",
       "      <td>9135</td>\n",
       "      <td>.AP.S.</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>16:38:58</td>\n",
       "      <td>0.139</td>\n",
       "      <td>TCP</td>\n",
       "      <td>192.168.220.6</td>\n",
       "      <td>47063</td>\n",
       "      <td>192.168.200.1</td>\n",
       "      <td>443</td>\n",
       "      <td>20</td>\n",
       "      <td>5652</td>\n",
       "      <td>.AP.S.</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>15:15:22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TCP</td>\n",
       "      <td>192.168.100.2</td>\n",
       "      <td>443</td>\n",
       "      <td>192.168.220.4</td>\n",
       "      <td>45052</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>.A....</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Day      Time Duration Proto      Src IP Addr Src Pt     Dst IP Addr  \\\n",
       "0    4  10:35:34      0.0   TCP  192.168.220.255     80  192.168.220.14   \n",
       "1    4  10:49:56      0.0   TCP    192.168.200.1    443   192.168.200.4   \n",
       "2    4  15:24:33    0.042   TCP    192.168.220.6  41724   192.168.220.2   \n",
       "3    0  16:38:58    0.139   TCP    192.168.220.6  47063   192.168.200.1   \n",
       "4    2  15:15:22      0.0   TCP    192.168.100.2    443   192.168.220.4   \n",
       "\n",
       "  Dst Pt Packets Bytes   Flags   class  \n",
       "0  48311       1    66  .A....  normal  \n",
       "1  53991       1    66  .A....  normal  \n",
       "2     80      12  9135  .AP.S.  normal  \n",
       "3    443      20  5652  .AP.S.  normal  \n",
       "4  45052       1    66  .A....  normal  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(260391, 12)\n"
     ]
    }
   ],
   "source": [
    "col_names = ['Day', 'Time', 'Duration', 'Proto', 'Src IP Addr', 'Src Pt', 'Dst IP Addr', 'Dst Pt', 'Packets', 'Bytes', 'Flags', 'class']\n",
    "final_output = pd.DataFrame(decoded_gan, columns = col_names)\n",
    "display(final_output.head())\n",
    "print(final_output.shape)\n",
    "\n",
    "filename = \"final_output_\" + main_str\n",
    "save2file(filename, final_output)"
   ]
  }
 ],
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