zeek_dns.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fe6c9c83-9df9-4564-be52-0f251bb12b29",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find anamolous DNS queries\n",
    "from ssh2.session import Session\n",
    "import socket\n",
    "\n",
    "class SSHCommand:\n",
    "    address = \"\"\n",
    "    username = \"\"\n",
    "    password = \"\"\n",
    "    \n",
    "    def __init__(self, address=\"127.0.0.1\", username=\"none\", password=\"none\"):\n",
    "        self.address = address\n",
    "        self.username = username\n",
    "        self.password = password\n",
    "    \n",
    "    def execute(self, command = \"\"):\n",
    "        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n",
    "        sock.connect((self.address, 22))\n",
    "        session = Session()\n",
    "        session.handshake(sock)\n",
    "        session.userauth_password(self.username, self.password)\n",
    "        channel = session.open_session()\n",
    "        channel.execute(command)\n",
    "        result = \"\"\n",
    "        size, result = channel.read()\n",
    "        while size > 0:\n",
    "            size, data = channel.read()\n",
    "            result = result + data\n",
    "        channel.close()\n",
    "        session.disconnect()\n",
    "        return result.decode('ascii')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1799c120-ea35-4b46-aab5-7b118a277add",
   "metadata": {},
   "outputs": [],
   "source": [
    "ssh = SSHCommand(address='172.16.1.17', username='admin', password='password')\n",
    "result = ssh.execute('echo -e \\\"timestamp\\tuid\\torig_host\\torig_port\\tresp_host\\tresp_port\\tproto\\ttrans_id\\tquery\\tqclass\\tqclass_name\\tqtype\\tqtype_name\\trcode\\trcode_name\\tAA\\tTC\\tRD\\tRA\\tZ\\tAnswers\\\" && zcat /nsm/zeek/logs/2021-09-08/dns* | jq -j \\'select(.answers != null) | .ts, \\\"\\t\\\", .uid, \\\"\\t\\\", .[\\\"id.orig_h\\\"], \\\"\\t\\\", .[\\\"id.orig_p\\\"], \\\"\\t\\\", .[\\\"id.resp_h\\\"], \\\"\\t\\\", .[\\\"id.resp_p\\\"], \\\"\\t\\\", .proto, \\\"\\t\\\", .trans_id, \\\"\\t\\\", .query, \\\"\\t\\\", .qclass, \\\"\\t\\\", .qclass_name, \\\"\\t\\\", .qtype, \\\"\\t\\\", .qtype_name, \\\"\\t\\\", .rcode, \\\"\\t\\\", .rcode_name, \\\"\\t\\\", .AA, \\\"\\t\\\", .TC, \\\"\\t\\\", .RD, \\\"\\t\\\", .RA, \\\"\\t\\\", .Z, \\\"\\t\\\", ([(.answers | sort | join(\\\",\\\"))] |@csv), \\\"\\n\\\"\\'')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a40b3151-bfc7-4c3c-880c-c2be820cc03d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>uid</th>\n",
       "      <th>orig_host</th>\n",
       "      <th>orig_port</th>\n",
       "      <th>resp_host</th>\n",
       "      <th>resp_port</th>\n",
       "      <th>proto</th>\n",
       "      <th>trans_id</th>\n",
       "      <th>query</th>\n",
       "      <th>qclass</th>\n",
       "      <th>...</th>\n",
       "      <th>qtype</th>\n",
       "      <th>qtype_name</th>\n",
       "      <th>rcode</th>\n",
       "      <th>rcode_name</th>\n",
       "      <th>AA</th>\n",
       "      <th>TC</th>\n",
       "      <th>RD</th>\n",
       "      <th>RA</th>\n",
       "      <th>Z</th>\n",
       "      <th>Answers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.631113e+09</td>\n",
       "      <td>CY22WW3lOQrdbOrVg</td>\n",
       "      <td>172.16.1.10</td>\n",
       "      <td>25571</td>\n",
       "      <td>172.16.1.14</td>\n",
       "      <td>53</td>\n",
       "      <td>udp</td>\n",
       "      <td>27554</td>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>NOERROR</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.631113e+09</td>\n",
       "      <td>CAXZRvqa055chpJA7</td>\n",
       "      <td>172.16.1.10</td>\n",
       "      <td>57029</td>\n",
       "      <td>172.16.1.14</td>\n",
       "      <td>53</td>\n",
       "      <td>udp</td>\n",
       "      <td>27140</td>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>NOERROR</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.631113e+09</td>\n",
       "      <td>CENjs72eJbrTxhr7yj</td>\n",
       "      <td>172.16.1.10</td>\n",
       "      <td>16551</td>\n",
       "      <td>172.16.1.14</td>\n",
       "      <td>53</td>\n",
       "      <td>udp</td>\n",
       "      <td>46374</td>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>NOERROR</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.631113e+09</td>\n",
       "      <td>C5u5sY0Vdd2WcMFZ3</td>\n",
       "      <td>172.16.1.10</td>\n",
       "      <td>34862</td>\n",
       "      <td>172.16.1.14</td>\n",
       "      <td>53</td>\n",
       "      <td>udp</td>\n",
       "      <td>38058</td>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>NOERROR</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.631113e+09</td>\n",
       "      <td>C1QsD02Y8im0lfnDz8</td>\n",
       "      <td>172.16.1.10</td>\n",
       "      <td>25756</td>\n",
       "      <td>172.16.1.14</td>\n",
       "      <td>53</td>\n",
       "      <td>udp</td>\n",
       "      <td>48892</td>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>NOERROR</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8348</th>\n",
       "      <td>1.631124e+09</td>\n",
       "      <td>Ca1UXq1vfweNhlnFLa</td>\n",
       "      <td>172.16.1.10</td>\n",
       "      <td>39473</td>\n",
       "      <td>172.16.1.14</td>\n",
       "      <td>53</td>\n",
       "      <td>udp</td>\n",
       "      <td>42261</td>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>NOERROR</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8349</th>\n",
       "      <td>1.631124e+09</td>\n",
       "      <td>CYuaJB4oYq68NLZmW9</td>\n",
       "      <td>172.16.1.10</td>\n",
       "      <td>38644</td>\n",
       "      <td>172.16.1.14</td>\n",
       "      <td>53</td>\n",
       "      <td>udp</td>\n",
       "      <td>23308</td>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>NOERROR</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8350</th>\n",
       "      <td>1.631124e+09</td>\n",
       "      <td>CU79Py1aNyn3w93cr3</td>\n",
       "      <td>172.16.1.10</td>\n",
       "      <td>42741</td>\n",
       "      <td>172.16.1.14</td>\n",
       "      <td>53</td>\n",
       "      <td>udp</td>\n",
       "      <td>57355</td>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>NOERROR</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8351</th>\n",
       "      <td>1.631124e+09</td>\n",
       "      <td>C5vAU04ppPL6l8LIUi</td>\n",
       "      <td>172.16.1.10</td>\n",
       "      <td>3682</td>\n",
       "      <td>172.16.1.14</td>\n",
       "      <td>53</td>\n",
       "      <td>udp</td>\n",
       "      <td>57582</td>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>NOERROR</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8352</th>\n",
       "      <td>1.631124e+09</td>\n",
       "      <td>CXS84M3YGg3wmERQq4</td>\n",
       "      <td>172.16.1.10</td>\n",
       "      <td>42474</td>\n",
       "      <td>172.16.1.14</td>\n",
       "      <td>53</td>\n",
       "      <td>udp</td>\n",
       "      <td>47484</td>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>NOERROR</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8353 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         timestamp                 uid    orig_host  orig_port    resp_host  \\\n",
       "0     1.631113e+09   CY22WW3lOQrdbOrVg  172.16.1.10      25571  172.16.1.14   \n",
       "1     1.631113e+09   CAXZRvqa055chpJA7  172.16.1.10      57029  172.16.1.14   \n",
       "2     1.631113e+09  CENjs72eJbrTxhr7yj  172.16.1.10      16551  172.16.1.14   \n",
       "3     1.631113e+09   C5u5sY0Vdd2WcMFZ3  172.16.1.10      34862  172.16.1.14   \n",
       "4     1.631113e+09  C1QsD02Y8im0lfnDz8  172.16.1.10      25756  172.16.1.14   \n",
       "...            ...                 ...          ...        ...          ...   \n",
       "8348  1.631124e+09  Ca1UXq1vfweNhlnFLa  172.16.1.10      39473  172.16.1.14   \n",
       "8349  1.631124e+09  CYuaJB4oYq68NLZmW9  172.16.1.10      38644  172.16.1.14   \n",
       "8350  1.631124e+09  CU79Py1aNyn3w93cr3  172.16.1.10      42741  172.16.1.14   \n",
       "8351  1.631124e+09  C5vAU04ppPL6l8LIUi  172.16.1.10       3682  172.16.1.14   \n",
       "8352  1.631124e+09  CXS84M3YGg3wmERQq4  172.16.1.10      42474  172.16.1.14   \n",
       "\n",
       "      resp_port proto  trans_id              query  qclass  ... qtype  \\\n",
       "0            53   udp     27554  vcsa.deploy.local       1  ...     1   \n",
       "1            53   udp     27140  vcsa.deploy.local       1  ...     1   \n",
       "2            53   udp     46374  vcsa.deploy.local       1  ...     1   \n",
       "3            53   udp     38058  vcsa.deploy.local       1  ...     1   \n",
       "4            53   udp     48892  vcsa.deploy.local       1  ...     1   \n",
       "...         ...   ...       ...                ...     ...  ...   ...   \n",
       "8348         53   udp     42261  vcsa.deploy.local       1  ...     1   \n",
       "8349         53   udp     23308  vcsa.deploy.local       1  ...     1   \n",
       "8350         53   udp     57355  vcsa.deploy.local       1  ...     1   \n",
       "8351         53   udp     57582  vcsa.deploy.local       1  ...     1   \n",
       "8352         53   udp     47484  vcsa.deploy.local       1  ...     1   \n",
       "\n",
       "      qtype_name rcode  rcode_name    AA     TC    RD    RA  Z      Answers  \n",
       "0              A     0     NOERROR  True  False  True  True  0  172.16.1.10  \n",
       "1              A     0     NOERROR  True  False  True  True  0  172.16.1.10  \n",
       "2              A     0     NOERROR  True  False  True  True  0  172.16.1.10  \n",
       "3              A     0     NOERROR  True  False  True  True  0  172.16.1.10  \n",
       "4              A     0     NOERROR  True  False  True  True  0  172.16.1.10  \n",
       "...          ...   ...         ...   ...    ...   ...   ... ..          ...  \n",
       "8348           A     0     NOERROR  True  False  True  True  0  172.16.1.10  \n",
       "8349           A     0     NOERROR  True  False  True  True  0  172.16.1.10  \n",
       "8350           A     0     NOERROR  True  False  True  True  0  172.16.1.10  \n",
       "8351           A     0     NOERROR  True  False  True  True  0  172.16.1.10  \n",
       "8352           A     0     NOERROR  True  False  True  True  0  172.16.1.10  \n",
       "\n",
       "[8353 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from io import StringIO\n",
    "import pandas as pd\n",
    "\n",
    "def string_to_df(data):\n",
    "    return pd.read_csv(StringIO(data), sep=\"\\t\")\n",
    "\n",
    "df = string_to_df(result)\n",
    "#all_pkts = df['orig_pkts'] + df['resp_pkts']\n",
    "#all_bytes = df['orig_bytes'] + df['resp_bytes']\n",
    "#total_pkts = df.sum('all_pkts')\n",
    "#print(total_pkts)\n",
    "#df[\"all_pkts\"] = all_pkts\n",
    "#df[\"all_bytes\"] = all_bytes\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9e57dc86-8a50-4e88-bb63-97190b331de9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trans_id</th>\n",
       "      <th>query</th>\n",
       "      <th>qclass</th>\n",
       "      <th>qclass_name</th>\n",
       "      <th>qtype</th>\n",
       "      <th>qtype_name</th>\n",
       "      <th>rcode</th>\n",
       "      <th>rcode_name</th>\n",
       "      <th>AA</th>\n",
       "      <th>TC</th>\n",
       "      <th>RD</th>\n",
       "      <th>RA</th>\n",
       "      <th>Z</th>\n",
       "      <th>Answers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27554</td>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>C_INTERNET</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>NOERROR</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27140</td>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>C_INTERNET</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>NOERROR</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46374</td>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>C_INTERNET</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>NOERROR</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38058</td>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>C_INTERNET</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>NOERROR</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48892</td>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>C_INTERNET</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>NOERROR</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8348</th>\n",
       "      <td>42261</td>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>C_INTERNET</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>NOERROR</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8349</th>\n",
       "      <td>23308</td>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>C_INTERNET</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>NOERROR</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8350</th>\n",
       "      <td>57355</td>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>C_INTERNET</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>NOERROR</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8351</th>\n",
       "      <td>57582</td>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>C_INTERNET</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>NOERROR</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8352</th>\n",
       "      <td>47484</td>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>C_INTERNET</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>NOERROR</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8353 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      trans_id              query  qclass qclass_name  qtype qtype_name  \\\n",
       "0        27554  vcsa.deploy.local       1  C_INTERNET      1          A   \n",
       "1        27140  vcsa.deploy.local       1  C_INTERNET      1          A   \n",
       "2        46374  vcsa.deploy.local       1  C_INTERNET      1          A   \n",
       "3        38058  vcsa.deploy.local       1  C_INTERNET      1          A   \n",
       "4        48892  vcsa.deploy.local       1  C_INTERNET      1          A   \n",
       "...        ...                ...     ...         ...    ...        ...   \n",
       "8348     42261  vcsa.deploy.local       1  C_INTERNET      1          A   \n",
       "8349     23308  vcsa.deploy.local       1  C_INTERNET      1          A   \n",
       "8350     57355  vcsa.deploy.local       1  C_INTERNET      1          A   \n",
       "8351     57582  vcsa.deploy.local       1  C_INTERNET      1          A   \n",
       "8352     47484  vcsa.deploy.local       1  C_INTERNET      1          A   \n",
       "\n",
       "      rcode rcode_name    AA     TC    RD    RA  Z      Answers  \n",
       "0         0    NOERROR  True  False  True  True  0  172.16.1.10  \n",
       "1         0    NOERROR  True  False  True  True  0  172.16.1.10  \n",
       "2         0    NOERROR  True  False  True  True  0  172.16.1.10  \n",
       "3         0    NOERROR  True  False  True  True  0  172.16.1.10  \n",
       "4         0    NOERROR  True  False  True  True  0  172.16.1.10  \n",
       "...     ...        ...   ...    ...   ...   ... ..          ...  \n",
       "8348      0    NOERROR  True  False  True  True  0  172.16.1.10  \n",
       "8349      0    NOERROR  True  False  True  True  0  172.16.1.10  \n",
       "8350      0    NOERROR  True  False  True  True  0  172.16.1.10  \n",
       "8351      0    NOERROR  True  False  True  True  0  172.16.1.10  \n",
       "8352      0    NOERROR  True  False  True  True  0  172.16.1.10  \n",
       "\n",
       "[8353 rows x 14 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df.drop([57145], inplace=True)\n",
    "df.drop(columns=['timestamp', 'uid', 'orig_host', 'orig_port', 'resp_host', 'resp_port', 'proto'], inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f6b8354c-c21d-4f98-bc9d-3598f5056d60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>query</th>\n",
       "      <th>qtype</th>\n",
       "      <th>rcode</th>\n",
       "      <th>Answers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8348</th>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8349</th>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8350</th>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8351</th>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8352</th>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8353 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  query  qtype  rcode      Answers\n",
       "0     vcsa.deploy.local      1      0  172.16.1.10\n",
       "1     vcsa.deploy.local      1      0  172.16.1.10\n",
       "2     vcsa.deploy.local      1      0  172.16.1.10\n",
       "3     vcsa.deploy.local      1      0  172.16.1.10\n",
       "4     vcsa.deploy.local      1      0  172.16.1.10\n",
       "...                 ...    ...    ...          ...\n",
       "8348  vcsa.deploy.local      1      0  172.16.1.10\n",
       "8349  vcsa.deploy.local      1      0  172.16.1.10\n",
       "8350  vcsa.deploy.local      1      0  172.16.1.10\n",
       "8351  vcsa.deploy.local      1      0  172.16.1.10\n",
       "8352  vcsa.deploy.local      1      0  172.16.1.10\n",
       "\n",
       "[8353 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(columns=['qclass', 'qclass_name', 'qtype_name', 'rcode_name', 'AA', 'TC', 'RD', 'RA', 'Z', 'trans_id'], inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f4dd919f-5e21-4ed8-b300-9430bb67b93e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>query</th>\n",
       "      <th>qtype</th>\n",
       "      <th>rcode</th>\n",
       "      <th>Answers</th>\n",
       "      <th>qlength</th>\n",
       "      <th>qparts</th>\n",
       "      <th>suffixlength</th>\n",
       "      <th>domainlength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8348</th>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8349</th>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8350</th>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8351</th>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8352</th>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8353 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  query  qtype  rcode      Answers  qlength  qparts  \\\n",
       "0     vcsa.deploy.local      1      0  172.16.1.10       17       3   \n",
       "1     vcsa.deploy.local      1      0  172.16.1.10       17       3   \n",
       "2     vcsa.deploy.local      1      0  172.16.1.10       17       3   \n",
       "3     vcsa.deploy.local      1      0  172.16.1.10       17       3   \n",
       "4     vcsa.deploy.local      1      0  172.16.1.10       17       3   \n",
       "...                 ...    ...    ...          ...      ...     ...   \n",
       "8348  vcsa.deploy.local      1      0  172.16.1.10       17       3   \n",
       "8349  vcsa.deploy.local      1      0  172.16.1.10       17       3   \n",
       "8350  vcsa.deploy.local      1      0  172.16.1.10       17       3   \n",
       "8351  vcsa.deploy.local      1      0  172.16.1.10       17       3   \n",
       "8352  vcsa.deploy.local      1      0  172.16.1.10       17       3   \n",
       "\n",
       "      suffixlength  domainlength  \n",
       "0                5             6  \n",
       "1                5             6  \n",
       "2                5             6  \n",
       "3                5             6  \n",
       "4                5             6  \n",
       "...            ...           ...  \n",
       "8348             5             6  \n",
       "8349             5             6  \n",
       "8350             5             6  \n",
       "8351             5             6  \n",
       "8352             5             6  \n",
       "\n",
       "[8353 rows x 8 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def query_length(query):\n",
    "    return len(query)\n",
    "\n",
    "def query_parts(query):\n",
    "    return len(query.split('.'))\n",
    "\n",
    "def query_suffix_length(query):\n",
    "    return len(query.split('.')[-1])\n",
    "\n",
    "def query_domain_length(query):\n",
    "    try:\n",
    "        length =  len(query.split('.')[-2])\n",
    "    except:\n",
    "        length = 0\n",
    "    return length\n",
    "\n",
    "qlengths = [query_length(query) for query in df['query']]\n",
    "qparts = [query_parts(query) for query in df['query']]\n",
    "qsufflengths = [query_suffix_length(query) for query in df['query']]\n",
    "qdomlengths = [query_domain_length(query) for query in df['query']]\n",
    "\n",
    "df['qlength'] = qlengths\n",
    "df['qparts'] = qparts\n",
    "df['suffixlength'] = qsufflengths\n",
    "df['domainlength'] = qdomlengths\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3e7b1a97-bb43-462d-85a2-cb3241cbb4c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1, 17,  3,  5,  6],\n",
       "       [ 1, 17,  3,  5,  6],\n",
       "       [ 1, 17,  3,  5,  6],\n",
       "       ...,\n",
       "       [ 1, 17,  3,  5,  6],\n",
       "       [ 1, 17,  3,  5,  6],\n",
       "       [ 1, 17,  3,  5,  6]], dtype=int64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df[['qtype', 'qlength', 'qparts', 'suffixlength', 'domainlength']].to_numpy()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ddeb023b-4546-4fc8-8629-8d9a3e2a2972",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(5)\n",
    "pca.fit(data)\n",
    "reduced = pca.transform(data)\n",
    "\n",
    "dbscan = DBSCAN(eps=5, min_samples=5)\n",
    "dbscan.fit(reduced)\n",
    "y_labels = dbscan.labels_\n",
    "y_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "11596bb2-e679-4b95-b95b-1e05b55eef44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def scatter3d(data, y_kmeans):\n",
    "    clusters = len(np.unique(y_kmeans))\n",
    "    colors=list(\"rgbcmyk\")\n",
    "    fig = plt.figure()\n",
    "    fig.set_figheight(15)\n",
    "    fig.set_figwidth(15)\n",
    "    ax = fig.add_subplot(111, projection='3d')\n",
    "    for cluster in range(clusters):\n",
    "        ax.scatter(data[y_kmeans==cluster, 0], data[y_kmeans==cluster, 1], data[y_kmeans==cluster, 2], c=colors[cluster%len(colors)])\n",
    "    ax.set_xlabel('X Label')\n",
    "    ax.set_ylabel('Y Label')\n",
    "    ax.set_zlabel('Z Label')\n",
    "    plt.show()\n",
    "\n",
    "scatter3d(reduced, y_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b7a723cc-3ab2-41bd-a361-e36213a1edf7",
   "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>query</th>\n",
       "      <th>qtype</th>\n",
       "      <th>rcode</th>\n",
       "      <th>Answers</th>\n",
       "      <th>qlength</th>\n",
       "      <th>qparts</th>\n",
       "      <th>suffixlength</th>\n",
       "      <th>domainlength</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8348</th>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8349</th>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8350</th>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8351</th>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8352</th>\n",
       "      <td>vcsa.deploy.local</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>172.16.1.10</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6996 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  query  qtype  rcode      Answers  qlength  qparts  \\\n",
       "0     vcsa.deploy.local      1      0  172.16.1.10       17       3   \n",
       "1     vcsa.deploy.local      1      0  172.16.1.10       17       3   \n",
       "2     vcsa.deploy.local      1      0  172.16.1.10       17       3   \n",
       "3     vcsa.deploy.local      1      0  172.16.1.10       17       3   \n",
       "4     vcsa.deploy.local      1      0  172.16.1.10       17       3   \n",
       "...                 ...    ...    ...          ...      ...     ...   \n",
       "8348  vcsa.deploy.local      1      0  172.16.1.10       17       3   \n",
       "8349  vcsa.deploy.local      1      0  172.16.1.10       17       3   \n",
       "8350  vcsa.deploy.local      1      0  172.16.1.10       17       3   \n",
       "8351  vcsa.deploy.local      1      0  172.16.1.10       17       3   \n",
       "8352  vcsa.deploy.local      1      0  172.16.1.10       17       3   \n",
       "\n",
       "      suffixlength  domainlength  cluster  \n",
       "0                5             6        0  \n",
       "1                5             6        0  \n",
       "2                5             6        0  \n",
       "3                5             6        0  \n",
       "4                5             6        0  \n",
       "...            ...           ...      ...  \n",
       "8348             5             6        0  \n",
       "8349             5             6        0  \n",
       "8350             5             6        0  \n",
       "8351             5             6        0  \n",
       "8352             5             6        0  \n",
       "\n",
       "[6996 rows x 9 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cluster'] = y_labels\n",
    "df.loc[df['cluster']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9d3979f2-ec2c-48c6-9556-623ac2af1f30",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>37</th>\n",
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       "      <th>8319</th>\n",
       "      <td>10.1.16.172.in-addr.arpa</td>\n",
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       "      <td>photon-machine,vcsa,vcsa.deploy.local</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>8323</th>\n",
       "      <td>10.1.16.172.in-addr.arpa</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>photon-machine,vcsa,vcsa.deploy.local</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>8326</th>\n",
       "      <td>10.1.16.172.in-addr.arpa</td>\n",
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       "      <td>7</td>\n",
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       "      <th>8329</th>\n",
       "      <td>10.1.16.172.in-addr.arpa</td>\n",
       "      <td>12</td>\n",
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       "      <td>photon-machine,vcsa,vcsa.deploy.local</td>\n",
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       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>8331</th>\n",
       "      <td>10.1.16.172.in-addr.arpa</td>\n",
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      "text/plain": [
       "                         query  qtype  rcode  \\\n",
       "23    10.1.16.172.in-addr.arpa     12      0   \n",
       "29    10.1.16.172.in-addr.arpa     12      0   \n",
       "33    10.1.16.172.in-addr.arpa     12      0   \n",
       "34    10.1.16.172.in-addr.arpa     12      0   \n",
       "37    10.1.16.172.in-addr.arpa     12      0   \n",
       "...                        ...    ...    ...   \n",
       "8319  10.1.16.172.in-addr.arpa     12      0   \n",
       "8323  10.1.16.172.in-addr.arpa     12      0   \n",
       "8326  10.1.16.172.in-addr.arpa     12      0   \n",
       "8329  10.1.16.172.in-addr.arpa     12      0   \n",
       "8331  10.1.16.172.in-addr.arpa     12      0   \n",
       "\n",
       "                                    Answers  qlength  qparts  suffixlength  \\\n",
       "23    photon-machine,vcsa,vcsa.deploy.local       24       6             4   \n",
       "29    photon-machine,vcsa,vcsa.deploy.local       24       6             4   \n",
       "33    photon-machine,vcsa,vcsa.deploy.local       24       6             4   \n",
       "34    photon-machine,vcsa,vcsa.deploy.local       24       6             4   \n",
       "37    photon-machine,vcsa,vcsa.deploy.local       24       6             4   \n",
       "...                                     ...      ...     ...           ...   \n",
       "8319  photon-machine,vcsa,vcsa.deploy.local       24       6             4   \n",
       "8323  photon-machine,vcsa,vcsa.deploy.local       24       6             4   \n",
       "8326  photon-machine,vcsa,vcsa.deploy.local       24       6             4   \n",
       "8329  photon-machine,vcsa,vcsa.deploy.local       24       6             4   \n",
       "8331  photon-machine,vcsa,vcsa.deploy.local       24       6             4   \n",
       "\n",
       "      domainlength  cluster  \n",
       "23               7        1  \n",
       "29               7        1  \n",
       "33               7        1  \n",
       "34               7        1  \n",
       "37               7        1  \n",
       "...            ...      ...  \n",
       "8319             7        1  \n",
       "8323             7        1  \n",
       "8326             7        1  \n",
       "8329             7        1  \n",
       "8331             7        1  \n",
       "\n",
       "[1327 rows x 9 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['cluster']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aaa66406-0168-4178-9afb-6806d05f182b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>motd.ubuntu.com</td>\n",
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       "      <td>0</td>\n",
       "      <td>2a05:d018:91c:3200:2846:99fb:81b6:1e11,2a05:d0...</td>\n",
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       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4567</th>\n",
       "      <td>ntp.ubuntu.com</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>2001:67c:1560:8003::c7,2001:67c:1560:8003::c8</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4718</th>\n",
       "      <td>ntp.ubuntu.com</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>2001:67c:1560:8003::c7,2001:67c:1560:8003::c8</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5118</th>\n",
       "      <td>ntp.ubuntu.com</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>2001:67c:1560:8003::c7,2001:67c:1560:8003::c8</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6102</th>\n",
       "      <td>ntp.ubuntu.com</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>2001:67c:1560:8003::c7,2001:67c:1560:8003::c8</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6309</th>\n",
       "      <td>ntp.ubuntu.com</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>2001:67c:1560:8003::c7,2001:67c:1560:8003::c8</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6693</th>\n",
       "      <td>ntp.ubuntu.com</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>2001:67c:1560:8003::c7,2001:67c:1560:8003::c8</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7081</th>\n",
       "      <td>motd.ubuntu.com</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>2a05:d018:91c:3200:2846:99fb:81b6:1e11,2a05:d0...</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7701</th>\n",
       "      <td>ntp.ubuntu.com</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>2001:67c:1560:8003::c7,2001:67c:1560:8003::c8</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7861</th>\n",
       "      <td>ntp.ubuntu.com</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>2001:67c:1560:8003::c7,2001:67c:1560:8003::c8</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8242</th>\n",
       "      <td>ntp.ubuntu.com</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>2001:67c:1560:8003::c7,2001:67c:1560:8003::c8</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                query  qtype  rcode  \\\n",
       "150    ntp.ubuntu.com     28      0   \n",
       "1427   ntp.ubuntu.com     28      0   \n",
       "1600   ntp.ubuntu.com     28      0   \n",
       "1978   ntp.ubuntu.com     28      0   \n",
       "2962   ntp.ubuntu.com     28      0   \n",
       "3129   ntp.ubuntu.com     28      0   \n",
       "3194  motd.ubuntu.com     28      0   \n",
       "3573   ntp.ubuntu.com     28      0   \n",
       "4567   ntp.ubuntu.com     28      0   \n",
       "4718   ntp.ubuntu.com     28      0   \n",
       "5118   ntp.ubuntu.com     28      0   \n",
       "6102   ntp.ubuntu.com     28      0   \n",
       "6309   ntp.ubuntu.com     28      0   \n",
       "6693   ntp.ubuntu.com     28      0   \n",
       "7081  motd.ubuntu.com     28      0   \n",
       "7701   ntp.ubuntu.com     28      0   \n",
       "7861   ntp.ubuntu.com     28      0   \n",
       "8242   ntp.ubuntu.com     28      0   \n",
       "\n",
       "                                                Answers  qlength  qparts  \\\n",
       "150       2001:67c:1560:8003::c7,2001:67c:1560:8003::c8       14       3   \n",
       "1427      2001:67c:1560:8003::c7,2001:67c:1560:8003::c8       14       3   \n",
       "1600      2001:67c:1560:8003::c7,2001:67c:1560:8003::c8       14       3   \n",
       "1978      2001:67c:1560:8003::c7,2001:67c:1560:8003::c8       14       3   \n",
       "2962      2001:67c:1560:8003::c7,2001:67c:1560:8003::c8       14       3   \n",
       "3129      2001:67c:1560:8003::c7,2001:67c:1560:8003::c8       14       3   \n",
       "3194  2a05:d018:91c:3200:2846:99fb:81b6:1e11,2a05:d0...       15       3   \n",
       "3573      2001:67c:1560:8003::c7,2001:67c:1560:8003::c8       14       3   \n",
       "4567      2001:67c:1560:8003::c7,2001:67c:1560:8003::c8       14       3   \n",
       "4718      2001:67c:1560:8003::c7,2001:67c:1560:8003::c8       14       3   \n",
       "5118      2001:67c:1560:8003::c7,2001:67c:1560:8003::c8       14       3   \n",
       "6102      2001:67c:1560:8003::c7,2001:67c:1560:8003::c8       14       3   \n",
       "6309      2001:67c:1560:8003::c7,2001:67c:1560:8003::c8       14       3   \n",
       "6693      2001:67c:1560:8003::c7,2001:67c:1560:8003::c8       14       3   \n",
       "7081  2a05:d018:91c:3200:2846:99fb:81b6:1e11,2a05:d0...       15       3   \n",
       "7701      2001:67c:1560:8003::c7,2001:67c:1560:8003::c8       14       3   \n",
       "7861      2001:67c:1560:8003::c7,2001:67c:1560:8003::c8       14       3   \n",
       "8242      2001:67c:1560:8003::c7,2001:67c:1560:8003::c8       14       3   \n",
       "\n",
       "      suffixlength  domainlength  cluster  \n",
       "150              3             6        2  \n",
       "1427             3             6        2  \n",
       "1600             3             6        2  \n",
       "1978             3             6        2  \n",
       "2962             3             6        2  \n",
       "3129             3             6        2  \n",
       "3194             3             6        2  \n",
       "3573             3             6        2  \n",
       "4567             3             6        2  \n",
       "4718             3             6        2  \n",
       "5118             3             6        2  \n",
       "6102             3             6        2  \n",
       "6309             3             6        2  \n",
       "6693             3             6        2  \n",
       "7081             3             6        2  \n",
       "7701             3             6        2  \n",
       "7861             3             6        2  \n",
       "8242             3             6        2  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
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   ],
   "source": [
    "df.loc[df['cluster']==2]"
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  {
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   "execution_count": 14,
   "id": "7d0ab413-b4fd-40c6-8595-2a3e1c9ba461",
   "metadata": {},
   "outputs": [
    {
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       "      <td>2600:1404:d400:282::2ef,2600:1404:d400:29d::2e...</td>\n",
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       "      <th>2068</th>\n",
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       "      <td>2600:1480:3000:cc::,eip-ntt.shd-download.vmwar...</td>\n",
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       "      <th>4609</th>\n",
       "      <td>partnerweb.vmware.com</td>\n",
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       "      <td>2600:1404:d400:282::2ef,2600:1404:d400:29d::2e...</td>\n",
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       "      <th>4767</th>\n",
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       "      <td>2600:1480:3000:cc::,eip-ntt.shd-download.vmwar...</td>\n",
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       "      <th>4768</th>\n",
       "      <td>hostupdate.vmware.com</td>\n",
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       "      <td>0</td>\n",
       "      <td>2600:1480:3000:cc::,eip-ntt.shd-download.vmwar...</td>\n",
       "      <td>21</td>\n",
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       "      <td>6</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>7353</th>\n",
       "      <td>partnerweb.vmware.com</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>2600:1404:d400:282::2ef,2600:1404:d400:29d::2e...</td>\n",
       "      <td>21</td>\n",
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       "      <td>0</td>\n",
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       "                      query  qtype  rcode  \\\n",
       "1853  partnerweb.vmware.com     28      0   \n",
       "2066  hostupdate.vmware.com     28      0   \n",
       "2068  hostupdate.vmware.com     28      0   \n",
       "4609  partnerweb.vmware.com     28      0   \n",
       "4767  hostupdate.vmware.com     28      0   \n",
       "4768  hostupdate.vmware.com     28      0   \n",
       "7353  partnerweb.vmware.com     28      0   \n",
       "7480  hostupdate.vmware.com     28      0   \n",
       "7482  hostupdate.vmware.com     28      0   \n",
       "\n",
       "                                                Answers  qlength  qparts  \\\n",
       "1853  2600:1404:d400:282::2ef,2600:1404:d400:29d::2e...       21       3   \n",
       "2066  2600:1480:3000:cc::,eip-ntt.shd-download.vmwar...       21       3   \n",
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       "4768  2600:1480:3000:cc::,eip-ntt.shd-download.vmwar...       21       3   \n",
       "7353  2600:1404:d400:282::2ef,2600:1404:d400:29d::2e...       21       3   \n",
       "7480  2600:1480:3000:cc::,eip-ntt.shd-download.vmwar...       21       3   \n",
       "7482  2600:1480:3000:cc::,eip-ntt.shd-download.vmwar...       21       3   \n",
       "\n",
       "      suffixlength  domainlength  cluster  \n",
       "1853             3             6        3  \n",
       "2066             3             6        3  \n",
       "2068             3             6        3  \n",
       "4609             3             6        3  \n",
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       "7482             3             6        3  "
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     "execution_count": 14,
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    "df.loc[df['cluster']==3]"
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   "execution_count": 15,
   "id": "7db4704f-6ab9-4e1d-8f5e-13023e01d9ce",
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   "outputs": [
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