IdealRMT-DecisionTrees/ExtractDataset.ipynb

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    "from scapy.all import *\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import argparse\n",
    "import os\n",
    "\n",
    "inputfile = \"data.pcap\"\n",
    "outputfile = \"data.csv\"\n",
    "\n",
    "mac_to_device = {\n",
    "    \"44:65:0d:56:cc:d3\": \"Amazon Echo\",\n",
    "    \"e0:76:d0:3f:00:ae\": \"August Doorbell Cam\",\n",
    "    \"70:88:6b:10:0f:c6\": \"Awair air quality monitor\",\n",
    "    \"b4:75:0e:ec:e5:a9\": \"Belkin Camera\",\n",
    "    \"ec:1a:59:83:28:11\": \"Belkin Motion Sensor\",\n",
    "    \"ec:1a:59:79:f4:89\": \"Belkin Switch\",\n",
    "    \"74:6a:89:00:2e:25\": \"Blipcare BP Meter\",\n",
    "    \"7c:70:bc:5d:5e:dc\": \"Canary Camera\",\n",
    "    \"30:8c:fb:2f:e4:b2\": \"Dropcam\",\n",
    "    \"6c:ad:f8:5e:e4:61\": \"Google Chromecast\",\n",
    "    \"28:c2:dd:ff:a5:2d\": \"Hello Barbie\",\n",
    "    \"70:5a:0f:e4:9b:c0\": \"HP Printer\",\n",
    "    \"74:c6:3b:29:d7:1d\": \"iHome PowerPlug\",\n",
    "    \"d0:73:d5:01:83:08\": \"LiFX Bulb\",\n",
    "    \"18:b4:30:25:be:e4\": \"NEST Smoke Sensor\",\n",
    "    \"70:ee:50:18:34:43\": \"Netatmo Camera\",\n",
    "    \"70:ee:50:03:b8:ac\": \"Netatmo Weather station\",\n",
    "    \"00:17:88:2b:9a:25\": \"Phillip Hue Lightbulb\",\n",
    "    \"e0:76:d0:33:bb:85\": \"Pixstart photo frame\",\n",
    "    \"88:4a:ea:31:66:9d\": \"Ring Door Bell\",\n",
    "    \"00:16:6c:ab:6b:88\": \"Samsung Smart Cam\",\n",
    "    \"d0:52:a8:00:67:5e\": \"Smart Things\",\n",
    "    \"f4:f2:6d:93:51:f1\": \"TP-Link Camera\",\n",
    "    \"50:c7:bf:00:56:39\": \"TP-Link Plug\",\n",
    "    \"18:b7:9e:02:20:44\": \"Triby Speaker\",\n",
    "    \"00:24:e4:10:ee:4c\": \"Withings Baby Monitor\",\n",
    "    \"00:24:e4:1b:6f:96\": \"Withings Scale\",\n",
    "    \"00:24:e4:20:28:c6\": \"Withings sleep sensor\",\n",
    "    \"00:24:e4:11:18:a8\": \"Withings\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "119623a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#read the pcap file and extract the features for each packet\n",
    "all_packets = rdpcap(inputfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f5584562",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = []\n",
    "for packet in all_packets:\n",
    "    size = len(packet)\n",
    "    try:\n",
    "        proto = packet.proto\n",
    "    except AttributeError:\n",
    "        proto = 0\n",
    "    try:\n",
    "        sport = packet.sport\n",
    "        dport = packet.dport\n",
    "    except AttributeError:\n",
    "        sport = 0\n",
    "        dport = 0\n",
    "\n",
    "    proto = int(proto)\n",
    "    sport = int(sport)\n",
    "    dport = int(dport)\n",
    "\n",
    "    if \"Ether\" in packet:\n",
    "        eth_dst = packet[\"Ether\"].dst\n",
    "        if eth_dst in mac_to_device:\n",
    "            classification = mac_to_device[eth_dst]\n",
    "        else:\n",
    "            classification = \"other\"\n",
    "    else:\n",
    "        classification = \"other\"\n",
    "\n",
    "    metric = [proto,sport,dport,classification]\n",
    "    results.append(metric)\n",
    "results = (np.array(results)).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2e04c2d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# store the features in the dataframe\n",
    "dataframe = pd.DataFrame({'protocl':results[0],'src':results[1],'dst':results[2],'classfication':results[3]})\n",
    "columns = ['protocl','src','dst','classfication']\n",
    "\n",
    "# save the dataframe to the csv file, if not exsit, create one.\n",
    "if os.path.exists(outputfile):\n",
    "    dataframe.to_csv(outputfile,index=False,sep=',',mode='a',columns = columns, header=False)\n",
    "else:\n",
    "    dataframe.to_csv(outputfile,index=False,sep=',',columns = columns)"
   ]
  }
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