Homework_2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/LoSgu8/Network-Measurements-Lab/blob/main/Homework_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Kqmnp5VMYbt"
      },
      "source": [
        "## Network Measurements Lab - Homework #2\n",
        "## Giacomo Sguotti - 10667547"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uOK-HXZWkJyt"
      },
      "source": [
        "In the attached .zip can be found:\n",
        "*   This Jupiter Notebook\n",
        "*   Three directories 26_03_22_captures/, 27_03_22_captures/ and 28_03_22_captures/ which contains tshark captures exported to .csv performed relatively on 26/03/22, 27/03/22 and 28/03/22\n",
        "*   Three files 26_03_22.csv, 27_03_22.csv and 28_03_22.csv that contain DataFrames where each row contains a curl request stats obtained from the tshark captures described above on the specific day. These files are needed to run the 6th point of this Jupyter Notebook\n",
        "\n",
        "All these must be uploaded in the root folder of the Jupiter Notebook to avoid errors during its execution.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x0xuTnJOIlfW"
      },
      "source": [
        "##1. Construct the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SH0DEXoCwwv5",
        "outputId": "09d923bf-4bd5-494f-edc2-06c2c45feb5e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following additional packages will be installed:\n",
            "  libc-ares2 libcap2-bin liblua5.2-0 libmaxminddb0 libnl-genl-3-200 libpam-cap\n",
            "  libpcap0.8 libsbc1 libsmi2ldbl libspandsp2 libwireshark-data libwireshark11\n",
            "  libwiretap8 libwscodecs2 libwsutil9 wireshark-common\n",
            "Suggested packages:\n",
            "  mmdb-bin snmp-mibs-downloader wireshark-doc\n",
            "The following NEW packages will be installed:\n",
            "  libc-ares2 libcap2-bin liblua5.2-0 libmaxminddb0 libnl-genl-3-200 libpam-cap\n",
            "  libpcap0.8 libsbc1 libsmi2ldbl libspandsp2 libwireshark-data libwireshark11\n",
            "  libwiretap8 libwscodecs2 libwsutil9 tshark wireshark-common\n",
            "0 upgraded, 17 newly installed, 0 to remove and 39 not upgraded.\n",
            "Need to get 16.5 MB of archives.\n",
            "After this operation, 95.6 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic/main amd64 libcap2-bin amd64 1:2.25-1.2 [20.6 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu bionic/main amd64 libpam-cap amd64 1:2.25-1.2 [7,268 B]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libpcap0.8 amd64 1.8.1-6ubuntu1.18.04.2 [118 kB]\n",
            "Get:4 http://archive.ubuntu.com/ubuntu bionic/main amd64 liblua5.2-0 amd64 5.2.4-1.1build1 [108 kB]\n",
            "Get:5 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libmaxminddb0 amd64 1.3.1-1 [25.6 kB]\n",
            "Get:6 http://archive.ubuntu.com/ubuntu bionic/main amd64 libnl-genl-3-200 amd64 3.2.29-0ubuntu3 [11.2 kB]\n",
            "Get:7 http://archive.ubuntu.com/ubuntu bionic/main amd64 libsmi2ldbl amd64 0.4.8+dfsg2-15 [100 kB]\n",
            "Get:8 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libspandsp2 amd64 0.0.6+dfsg-0.1 [273 kB]\n",
            "Get:9 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 libwireshark-data all 2.6.10-1~ubuntu18.04.0 [1,425 kB]\n",
            "Get:10 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libc-ares2 amd64 1.14.0-1ubuntu0.1 [37.5 kB]\n",
            "Get:11 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 libwsutil9 amd64 2.6.10-1~ubuntu18.04.0 [55.5 kB]\n",
            "Get:12 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 libwiretap8 amd64 2.6.10-1~ubuntu18.04.0 [177 kB]\n",
            "Get:13 http://archive.ubuntu.com/ubuntu bionic/main amd64 libsbc1 amd64 1.3-2 [33.1 kB]\n",
            "Get:14 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 libwscodecs2 amd64 2.6.10-1~ubuntu18.04.0 [17.8 kB]\n",
            "Get:15 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 libwireshark11 amd64 2.6.10-1~ubuntu18.04.0 [13.6 MB]\n",
            "Get:16 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 wireshark-common amd64 2.6.10-1~ubuntu18.04.0 [391 kB]\n",
            "Get:17 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 tshark amd64 2.6.10-1~ubuntu18.04.0 [134 kB]\n",
            "Fetched 16.5 MB in 0s (33.2 MB/s)\n",
            "Preconfiguring packages ...\n",
            "Selecting previously unselected package libcap2-bin.\n",
            "(Reading database ... 156210 files and directories currently installed.)\n",
            "Preparing to unpack .../00-libcap2-bin_1%3a2.25-1.2_amd64.deb ...\n",
            "Unpacking libcap2-bin (1:2.25-1.2) ...\n",
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            "Preparing to unpack .../01-libpam-cap_1%3a2.25-1.2_amd64.deb ...\n",
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            "Preparing to unpack .../06-libsmi2ldbl_0.4.8+dfsg2-15_amd64.deb ...\n",
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            "Selecting previously unselected package libspandsp2:amd64.\n",
            "Preparing to unpack .../07-libspandsp2_0.0.6+dfsg-0.1_amd64.deb ...\n",
            "Unpacking libspandsp2:amd64 (0.0.6+dfsg-0.1) ...\n",
            "Selecting previously unselected package libwireshark-data.\n",
            "Preparing to unpack .../08-libwireshark-data_2.6.10-1~ubuntu18.04.0_all.deb ...\n",
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            "Preparing to unpack .../09-libc-ares2_1.14.0-1ubuntu0.1_amd64.deb ...\n",
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            "Selecting previously unselected package libwiretap8:amd64.\n",
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            "Preparing to unpack .../13-libwscodecs2_2.6.10-1~ubuntu18.04.0_amd64.deb ...\n",
            "Unpacking libwscodecs2:amd64 (2.6.10-1~ubuntu18.04.0) ...\n",
            "Selecting previously unselected package libwireshark11:amd64.\n",
            "Preparing to unpack .../14-libwireshark11_2.6.10-1~ubuntu18.04.0_amd64.deb ...\n",
            "Unpacking libwireshark11:amd64 (2.6.10-1~ubuntu18.04.0) ...\n",
            "Selecting previously unselected package wireshark-common.\n",
            "Preparing to unpack .../15-wireshark-common_2.6.10-1~ubuntu18.04.0_amd64.deb ...\n",
            "Unpacking wireshark-common (2.6.10-1~ubuntu18.04.0) ...\n",
            "Selecting previously unselected package tshark.\n",
            "Preparing to unpack .../16-tshark_2.6.10-1~ubuntu18.04.0_amd64.deb ...\n",
            "Unpacking tshark (2.6.10-1~ubuntu18.04.0) ...\n",
            "Setting up libsbc1:amd64 (1.3-2) ...\n",
            "Setting up libpam-cap:amd64 (1:2.25-1.2) ...\n",
            "Setting up libcap2-bin (1:2.25-1.2) ...\n",
            "Setting up libspandsp2:amd64 (0.0.6+dfsg-0.1) ...\n",
            "Setting up libnl-genl-3-200:amd64 (3.2.29-0ubuntu3) ...\n",
            "Setting up libsmi2ldbl:amd64 (0.4.8+dfsg2-15) ...\n",
            "Setting up libwsutil9:amd64 (2.6.10-1~ubuntu18.04.0) ...\n",
            "Setting up libwireshark-data (2.6.10-1~ubuntu18.04.0) ...\n",
            "Setting up libwscodecs2:amd64 (2.6.10-1~ubuntu18.04.0) ...\n",
            "Setting up liblua5.2-0:amd64 (5.2.4-1.1build1) ...\n",
            "Setting up libc-ares2:amd64 (1.14.0-1ubuntu0.1) ...\n",
            "Setting up libmaxminddb0:amd64 (1.3.1-1) ...\n",
            "Setting up libpcap0.8:amd64 (1.8.1-6ubuntu1.18.04.2) ...\n",
            "Setting up libwiretap8:amd64 (2.6.10-1~ubuntu18.04.0) ...\n",
            "Setting up libwireshark11:amd64 (2.6.10-1~ubuntu18.04.0) ...\n",
            "Setting up wireshark-common (2.6.10-1~ubuntu18.04.0) ...\n",
            "Setting up tshark (2.6.10-1~ubuntu18.04.0) ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1.3) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/python3.7/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n",
            "\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
            "Processing triggers for shared-mime-info (1.9-2) ...\n",
            "Processing triggers for hicolor-icon-theme (0.17-2) ...\n"
          ]
        }
      ],
      "source": [
        "!apt-get install tshark"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1_EIfPJMINKW"
      },
      "source": [
        "This bash script, for each link in WEBSITES performs 10 curl requests and outputs a .csv  file (and optionally a .pcap file) for each of them.\n",
        "Its execution can be avoided by uploading captures/."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BjgUPEuhwb-l",
        "outputId": "7326cad2-998d-457f-d4ef-9599a674bf53"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "https://www.indiatimes.com\n",
            "Running as user \"root\" and group \"root\". This could be dangerous.\n",
            "Capturing on 'eth0'\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "\t Request #1\n",
            "131 \t Request #2\n",
            "253 \t Request #3\n",
            "378 \t Request #4\n",
            "498 \t Request #5\n",
            "631 \t Request #6\n",
            "796 \t Request #7\n",
            "918 \t Request #8\n",
            "1068 \t Request #9\n",
            "1215 \t Request #10\n",
            "1331 https://www.washingtonpost.com\n",
            "\n",
            "Running as user \"root\" and group \"root\". This could be dangerous.\n",
            "Capturing on 'eth0'\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "\t Request #1\n",
            "118 \t Request #2\n",
            "242 \t Request #3\n",
            "422 \t Request #4\n",
            "540 \t Request #5\n",
            "660 \t Request #6\n",
            "792 \t Request #7\n",
            "928 \t Request #8\n",
            "1062 \t Request #9\n",
            "1186 \t Request #10\n",
            "1304 https://www.ndtv.com\n",
            "\n",
            "Running as user \"root\" and group \"root\". This could be dangerous.\n",
            "Capturing on 'eth0'\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "\t Request #1\n",
            "124 \t Request #2\n",
            "218 \t Request #3\n",
            "330 \t Request #4\n",
            "446 \t Request #5\n",
            "564 \t Request #6\n",
            "673 \t Request #7\n",
            "783 \t Request #8\n",
            "895 \t Request #9\n",
            "1007 \t Request #10\n",
            "1110 https://www.cnbc.com\n",
            "\n",
            "Running as user \"root\" and group \"root\". This could be dangerous.\n",
            "Capturing on 'eth0'\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "\t Request #1\n",
            "319 \t Request #2\n",
            "528 \t Request #3\n",
            "746 \t Request #4\n",
            "1055 \t Request #5\n",
            "1316 \t Request #6\n",
            "1565 \t Request #7\n",
            "1757 \t Request #8\n",
            "1995 \t Request #9\n",
            "2260 \t Request #10\n",
            "2503 https://www.timesofindia.com\n",
            "\n",
            "Running as user \"root\" and group \"root\". This could be dangerous.\n",
            "Capturing on 'eth0'\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "\t Request #1\n",
            "27 \t Request #2\n",
            "54 \t Request #3\n",
            "81 \t Request #4\n",
            "109 \t Request #5\n",
            "136 \t Request #6\n",
            "163 \t Request #7\n",
            "188 \t Request #8\n",
            "215 \t Request #9\n",
            "242 \t Request #10\n",
            "269 https://www.express.co.uk\n",
            "\n",
            "Running as user \"root\" and group \"root\". This could be dangerous.\n",
            "Capturing on 'eth0'\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "\t Request #1\n",
            "65 \t Request #2\n",
            "129 \t Request #3\n",
            "190 \t Request #4\n",
            "255 \t Request #5\n",
            "337 \t Request #6\n",
            "396 \t Request #7\n",
            "457 \t Request #8\n",
            "522 \t Request #9\n",
            "587 \t Request #10\n",
            "651 https://www.rt.com\n",
            "\n",
            "Running as user \"root\" and group \"root\". This could be dangerous.\n",
            "Capturing on 'eth0'\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "\t Request #1\n",
            "51 \t Request #2\n",
            "290 \t Request #3\n",
            "340 \t Request #4\n",
            "399 \t Request #5\n",
            "638 \t Request #6\n",
            "701 \t Request #7\n",
            "753 \t Request #8\n",
            "809 \t Request #9\n",
            "864 \t Request #10\n",
            "911 https://www.news18.com\n",
            "\n",
            "Running as user \"root\" and group \"root\". This could be dangerous.\n",
            "Capturing on 'eth0'\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "\t Request #1\n",
            "187 \t Request #2\n",
            "414 \t Request #3\n",
            "604 \t Request #4\n",
            "818 \t Request #5\n",
            "1031 \t Request #6\n",
            "1185 \t Request #7\n",
            "1361 \t Request #8\n",
            "1617 \t Request #9\n",
            "1847 \t Request #10\n",
            "2076 https://www.nypost.com\n",
            "\n",
            "Running as user \"root\" and group \"root\". This could be dangerous.\n",
            "Capturing on 'eth0'\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "\t Request #1\n",
            "32 \t Request #2\n",
            "64 \t Request #3\n",
            "94 \t Request #4\n",
            "124 \t Request #5\n",
            "153 \t Request #6\n",
            "184 \t Request #7\n",
            "216 \t Request #8\n",
            "248 \t Request #9\n",
            "278 \t Request #10\n",
            "306 https://www.abc.net.au\n",
            "\n",
            "Running as user \"root\" and group \"root\". This could be dangerous.\n",
            "Capturing on 'eth0'\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "\t Request #1\n",
            "101 \t Request #2\n",
            "201 \t Request #3\n",
            "304 \t Request #4\n",
            "403 \t Request #5\n",
            "501 \t Request #6\n",
            "603 \t Request #7\n",
            "701 \t Request #8\n",
            "798 \t Request #9\n",
            "903 \t Request #10\n",
            "995 https://www.bbc.co.uk\n",
            "\n",
            "Running as user \"root\" and group \"root\". This could be dangerous.\n",
            "Capturing on 'eth0'\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "\t Request #1\n",
            "84 \t Request #2\n",
            "170 \t Request #3\n",
            "258 \t Request #4\n",
            "350 \t Request #5\n",
            "430 \t Request #6\n",
            "520 \t Request #7\n",
            "604 \t Request #8\n",
            "690 \t Request #9\n",
            "772 \t Request #10\n",
            "862 https://www.msn.com\n",
            "\n",
            "Running as user \"root\" and group \"root\". This could be dangerous.\n",
            "Capturing on 'eth0'\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "\t Request #1\n",
            "49 \t Request #2\n",
            "98 \t Request #3\n",
            "143 \t Request #4\n",
            "190 \t Request #5\n",
            "235 \t Request #6\n",
            "281 \t Request #7\n",
            "327 \t Request #8\n",
            "371 \t Request #9\n",
            "418 \t Request #10\n",
            "463 https://www.cnn.com\n",
            "\n",
            "Running as user \"root\" and group \"root\". This could be dangerous.\n",
            "Capturing on 'eth0'\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "\t Request #1\n",
            "154 \t Request #2\n",
            "299 \t Request #3\n",
            "442 \t Request #4\n",
            "593 \t Request #5\n",
            "749 \t Request #6\n",
            "894 \t Request #7\n",
            "1025 \t Request #8\n",
            "1179 \t Request #9\n",
            "1295 \t Request #10\n",
            "1435 https://www.news.google.com\n",
            "\n",
            "Running as user \"root\" and group \"root\". This could be dangerous.\n",
            "Capturing on 'eth0'\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "\t Request #1\n",
            "26 \t Request #2\n",
            "56 \t Request #3\n",
            "86 \t Request #4\n",
            "113 \t Request #5\n",
            "139 \t Request #6\n",
            "166 \t Request #7\n",
            "193 \t Request #8\n",
            "221 \t Request #9\n",
            "248 \t Request #10\n",
            "273 https://www.dailymail.co.uk\n",
            "\n",
            "Running as user \"root\" and group \"root\". This could be dangerous.\n",
            "Capturing on 'eth0'\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "\t Request #1\n",
            "34 \t Request #2\n",
            "68 \t Request #3\n",
            "104 \t Request #4\n",
            "140 \t Request #5\n",
            "176 \t Request #6\n",
            "212 \t Request #7\n",
            "246 \t Request #8\n",
            "280 \t Request #9\n",
            "316 \t Request #10\n",
            "350 https://www.nytimes.com\n",
            "\n",
            "Running as user \"root\" and group \"root\". This could be dangerous.\n",
            "Capturing on 'eth0'\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "\t Request #1\n",
            "205 \t Request #2\n",
            "391 \t Request #3\n",
            "614 \t Request #4\n",
            "794 \t Request #5\n",
            "987 \t Request #6\n",
            "1144 \t Request #7\n",
            "1341 \t Request #8\n",
            "1551 \t Request #9\n",
            "1766 \t Request #10\n",
            "1942 https://www.theguardian.com\n",
            "\n",
            "Running as user \"root\" and group \"root\". This could be dangerous.\n",
            "Capturing on 'eth0'\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "\t Request #1\n",
            "34 \t Request #2\n",
            "68 \t Request #3\n",
            "102 \t Request #4\n",
            "136 \t Request #5\n",
            "170 \t Request #6\n",
            "203 \t Request #7\n",
            "237 \t Request #8\n",
            "269 \t Request #9\n",
            "301 \t Request #10\n",
            "335 https://www.foxnews.com\n",
            "\n",
            "Running as user \"root\" and group \"root\". This could be dangerous.\n",
            "Capturing on 'eth0'\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "\t Request #1\n",
            "96 \t Request #2\n",
            "194 \t Request #3\n",
            "287 \t Request #4\n",
            "377 \t Request #5\n",
            "478 \t Request #6\n",
            "587 \t Request #7\n",
            "691 \t Request #8\n",
            "783 \t Request #9\n",
            "879 \t Request #10\n",
            "978 https://www.finance.yahoo.com\n",
            "\n",
            "Running as user \"root\" and group \"root\". This could be dangerous.\n",
            "Capturing on 'eth0'\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "\t Request #1\n",
            "36 \t Request #2\n",
            "75 \t Request #3\n",
            "111 \t Request #4\n",
            "150 \t Request #5\n",
            "189 \t Request #6\n",
            "228 \t Request #7\n",
            "264 \t Request #8\n",
            "303 \t Request #9\n",
            "342 \t Request #10\n",
            "381 https://www.news.yahoo.com\n",
            "\n",
            "Running as user \"root\" and group \"root\". This could be dangerous.\n",
            "Capturing on 'eth0'\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "tshark: cap_set_proc() fail return: Operation not permitted\n",
            "\n",
            "\t Request #1\n",
            "39 \t Request #2\n",
            "78 \t Request #3\n",
            "117 \t Request #4\n",
            "156 \t Request #5\n",
            "195 \t Request #6\n",
            "232 \t Request #7\n",
            "271 \t Request #8\n",
            "309 \t Request #9\n",
            "348 \t Request #10\n",
            "387 \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "%%shell\n",
        "# LIST OF WEBSITES TO VISIT\n",
        "WEBSITES=\"https://www.indiatimes.com https://www.washingtonpost.com https://www.ndtv.com https://www.cnbc.com https://www.timesofindia.com https://www.express.co.uk https://www.rt.com https://www.news18.com https://www.nypost.com https://www.abc.net.au https://www.bbc.co.uk https://www.msn.com https://www.cnn.com https://www.news.google.com https://www.dailymail.co.uk https://www.nytimes.com https://www.theguardian.com https://www.foxnews.com https://www.finance.yahoo.com https://www.news.yahoo.com\"\n",
        "\n",
        "# BPF FILTER TO APPLY, IT IGNORES ALL TRAFFIC BETWEEN SERVERS BELONGING TO THE SAME DATACENTER\n",
        "# AND NON TCP TRAFFIC\n",
        "FILTER=\"not (src net 172.16.0.0/12 && dst net 172.16.0.0/12) && tcp\"\n",
        "\n",
        "mkdir -p ./captures\n",
        "# FOR EACH WEBSITE IN WEBSITES: START TSHARK CAPTURE IN BACKGROUND, PERFORM 10 CURLS AND KILL TSHARK\n",
        "for i in $WEBSITES\n",
        "do\n",
        "  echo $i\n",
        "  tshark -i eth0 -T fields -E header=y -E separator=, -E occurrence=f -e frame.number \\\n",
        "  -e frame.time -e ip.len -e ip.proto -e ip.src -e ip.dst -e tcp.srcport -e tcp.dstport \\\n",
        "  -e tcp.len -f \"${FILTER}\" -w './captures/'${i:8}.pcap > ./captures/${i:8}.csv &\n",
        "\n",
        "  sleep 1 # TO WAIT TSHARK START\n",
        "  RUNNING_PID=$!\n",
        "  for j in {1..10}\n",
        "  do\n",
        "    echo -e \"\\t Request #$j\"\n",
        "    curl -s $i > /dev/null\n",
        "    sleep 5 # wait 5 seconds between a capture and another\n",
        "  done\n",
        "  sleep 1 # WAIT LAST HTTP REQUEST BEFORE KILLING TSHARK CAPTURE\n",
        "  kill ${RUNNING_PID}\n",
        "done\n",
        "exit"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "agqn2xGzTML9"
      },
      "source": [
        "##2. Extract **biflow** features from each capture"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2YXlDm36CYrq"
      },
      "outputs": [],
      "source": [
        "import ipaddress\n",
        "\n",
        "def get_direction(src_ip):\n",
        "  # if a packet comes from a private ip except 169.254.169.254 (local DNS server) its direction is uplink\n",
        "  if (ipaddress.ip_address(src_ip).is_private and src_ip != '169.254.169.254'):\n",
        "    return 'up'\n",
        "  else:\n",
        "    return 'down'\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jEsYhoz9-3qB"
      },
      "outputs": [],
      "source": [
        "def extract_features(flow,df_flow, reverse_flow, df_reverse_flow):\n",
        "  flow_features = {}\n",
        "\n",
        "  # Using the flow direction store the TCP ports\n",
        "  direction = get_direction(flow[0])\n",
        "  if direction == 'up':\n",
        "    flow_features['dport'] = flow[3]\n",
        "    flow_features['sport'] = flow[2]\n",
        "  else:\n",
        "    flow_features['dport'] = flow[2]\n",
        "    flow_features['sport'] = flow[3]\n",
        "  \n",
        "  # FLOW STATS\n",
        "  pkt_sizes = df_flow['ip.len'].astype(float)\n",
        "  iat = pd.to_datetime(df_flow['frame.time']).diff(1).dt.total_seconds().iloc[1:]\n",
        "  flow_features['num_packets '+direction] = len(df_flow)\n",
        "  flow_features['total_bytes '+direction] = pkt_sizes.sum()\n",
        "  flow_features['min_pkt_size '+direction] = pkt_sizes.min() \n",
        "  flow_features['max_pkt_size '+direction] = pkt_sizes.max()\n",
        "  flow_features['mean_pkt_size '+direction] = pkt_sizes.mean()\n",
        "  flow_features['std_pkt_size '+direction] = pkt_sizes.std()\n",
        "  flow_features['min_iat '+direction] = iat.min()\n",
        "  flow_features['max_iat '+direction] = iat.max()\n",
        "  flow_features['mean_iat '+direction] = iat.mean()\n",
        "  flow_features['std_iat '+direction] = iat.std()\n",
        "  if not df_reverse_flow.empty: \n",
        "    # REVERSE FLOW STATS\n",
        "    if direction == 'up':\n",
        "      reverse_direction='down'\n",
        "    else:\n",
        "      reverse_direction='up'\n",
        "\n",
        "    flow_features['num_packets '+reverse_direction] = len(df_reverse_flow)\n",
        "    pkt_sizes = df_reverse_flow['ip.len'].astype(float)\n",
        "    flow_features['total_bytes '+reverse_direction] = pkt_sizes.sum()\n",
        "    flow_features['min_pkt_size '+reverse_direction] = pkt_sizes.min() \n",
        "    flow_features['max_pkt_size '+reverse_direction] = pkt_sizes.max()\n",
        "    flow_features['mean_pkt_size '+reverse_direction] = pkt_sizes.mean()\n",
        "    flow_features['std_pkt_size '+reverse_direction] = pkt_sizes.std()\n",
        "    iat = pd.to_datetime(df_reverse_flow['frame.time']).diff(1).dt.total_seconds().iloc[1:]\n",
        "    flow_features['min_iat '+reverse_direction] = iat.min()\n",
        "    flow_features['max_iat '+reverse_direction] = iat.max()\n",
        "    flow_features['mean_iat '+reverse_direction] = iat.mean()\n",
        "    flow_features['std_iat '+reverse_direction] = iat.std()  \n",
        "  \n",
        "  return flow_features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O-TuNrlfwKR3"
      },
      "source": [
        "## 3. Create a dataset DataFrame where each row corresponds to a curl request"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n2sueDOW9XBU",
        "outputId": "36b03ab3-f7a0-4977-db0e-a7f7c8ae60ea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file   www.msn.com.csv\n",
            "Processing file   www.indiatimes.com.csv\n",
            "Processing file   www.theguardian.com.csv\n",
            "Processing file   www.dailymail.co.uk.csv\n",
            "Processing file   www.cnbc.com.csv\n",
            "Processing file   www.ndtv.com.csv\n",
            "Processing file   www.abc.net.au.csv\n",
            "Processing file   www.news.google.com.csv\n",
            "Processing file   www.express.co.uk.csv\n",
            "Processing file   www.cnn.com.csv\n",
            "Processing file   www.nypost.com.csv\n",
            "Processing file   www.washingtonpost.com.csv\n",
            "Processing file   www.foxnews.com.csv\n",
            "Processing file   www.finance.yahoo.com.csv\n",
            "Processing file   www.rt.com.csv\n",
            "Processing file   www.bbc.co.uk.csv\n",
            "Processing file   www.timesofindia.com.csv\n",
            "Processing file   www.news18.com.csv\n",
            "Processing file   www.news.yahoo.com.csv\n",
            "Processing file   www.nytimes.com.csv\n"
          ]
        }
      ],
      "source": [
        "from os import listdir\n",
        "from os.path import isfile, join\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "path = './26_03_22_captures/'\n",
        "tsharkfiles = [f for f in listdir(path) if isfile(join(path, f))]\n",
        "\n",
        "biflow_features = pd.DataFrame()\n",
        "for f in tsharkfiles:\n",
        "  if (f.startswith('www') and f.endswith('.csv')):\n",
        "    print('Processing file  ', f)\n",
        "    df_tcp = pd.read_csv(join(path,f))\n",
        "    df_flows_tcp = df_tcp.groupby([\"ip.src\",\"ip.dst\",\"tcp.srcport\",\"tcp.dstport\"])\n",
        "    to_ignore_flows = []\n",
        "    for flow,df_flow in df_flows_tcp: #flow is a tuple representing the flow, df_flow is the dataframe with all packets of the flow\n",
        "      if flow not in to_ignore_flows: # ignore the already analyzed reverse flows\n",
        "        # if exists the reverse flow get its dataframe, otherwise set df_reverse_flow as empty dataframe\n",
        "        reverse_flow = (flow[1], flow[0], flow[3], flow[2])\n",
        "        \n",
        "        if reverse_flow in df_flows_tcp.groups:\n",
        "          df_reverse_flow = df_flows_tcp.get_group(reverse_flow)\n",
        "          to_ignore_flows.append(reverse_flow)  \n",
        "        else:\n",
        "          df_reverse_flow = pd.DataFrame()\n",
        "\n",
        "        flow_features = extract_features(flow, df_flow, reverse_flow, df_reverse_flow)\n",
        "        flow_features['website'] = f[4:len(f)-4]\n",
        "        biflow_features = biflow_features.append(flow_features,ignore_index=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XTZoXoSBvxDy"
      },
      "outputs": [],
      "source": [
        "# export the created dataset\n",
        "#biflow_features.to_csv('26_03_22.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uAgeVu7F9NZw"
      },
      "outputs": [],
      "source": [
        "# import an already created dataset if you don't want to run the tshark module\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "biflow_features = pd.read_csv('26_03_22.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "n0nV44_6o84H",
        "outputId": "020591ab-d96d-4f98-937b-097d1c8a0fbb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "     Unnamed: 0  dport    sport  num_packets up  total_bytes up  \\\n",
              "0             0  443.0  58242.0            18.0          1768.0   \n",
              "1             1  443.0  58254.0            18.0          1768.0   \n",
              "2             2  443.0  58274.0            18.0          1768.0   \n",
              "3             3  443.0  58292.0            18.0          1768.0   \n",
              "4             4  443.0  58304.0            18.0          1768.0   \n",
              "..          ...    ...      ...             ...             ...   \n",
              "196         196  443.0  52426.0            68.0          4442.0   \n",
              "197         197  443.0  52438.0            69.0          4506.0   \n",
              "198         198  443.0  52456.0            81.0          5118.0   \n",
              "199         199  443.0  52466.0            70.0          4546.0   \n",
              "200         200  443.0  52492.0            61.0          4090.0   \n",
              "\n",
              "     min_pkt_size up  max_pkt_size up  mean_pkt_size up  std_pkt_size up  \\\n",
              "0               40.0            569.0         98.222222       120.591462   \n",
              "1               40.0            569.0         98.222222       120.591462   \n",
              "2               40.0            569.0         98.222222       120.591462   \n",
              "3               40.0            569.0         98.222222       120.591462   \n",
              "4               40.0            569.0         98.222222       120.591462   \n",
              "..               ...              ...               ...              ...   \n",
              "196             40.0            569.0         65.323529        65.419984   \n",
              "197             40.0            569.0         65.304348        64.904750   \n",
              "198             40.0            569.0         63.185185        60.071023   \n",
              "199             40.0            569.0         64.942857        64.503654   \n",
              "200             40.0            569.0         67.049180        68.902933   \n",
              "\n",
              "     min_iat up  ...  total_bytes down  min_pkt_size down  max_pkt_size down  \\\n",
              "0      0.000010  ...            4804.0               52.0             2948.0   \n",
              "1      0.000003  ...            4856.0               52.0             2948.0   \n",
              "2      0.000004  ...            4856.0               52.0             2948.0   \n",
              "3      0.000006  ...            4856.0               52.0             2948.0   \n",
              "4      0.000005  ...            4857.0               52.0             2948.0   \n",
              "..          ...  ...               ...                ...                ...   \n",
              "196    0.000003  ...          682907.0               52.0            37855.0   \n",
              "197    0.000007  ...          683424.0               52.0            30460.0   \n",
              "198    0.000007  ...          683450.0               52.0            34804.0   \n",
              "199    0.000005  ...          682575.0               52.0            39148.0   \n",
              "200    0.000005  ...          682060.0               52.0            39148.0   \n",
              "\n",
              "     mean_pkt_size down  std_pkt_size down  min_iat down  max_iat down  \\\n",
              "0            320.266667         738.005562      0.000000      0.008135   \n",
              "1            303.500000         716.128480      0.000001      0.008505   \n",
              "2            303.500000         716.128480      0.000000      0.008046   \n",
              "3            303.500000         716.128480      0.000000      0.008022   \n",
              "4            303.562500         716.121030      0.000000      0.008808   \n",
              "..                  ...                ...           ...           ...   \n",
              "196         9105.426667        9375.864881      0.000000      0.101507   \n",
              "197         7855.448276        7840.386185      0.000001      0.100975   \n",
              "198         7947.093023        8130.720952      0.000001      0.095903   \n",
              "199         9892.391304        9796.493431      0.000000      0.007941   \n",
              "200        11000.967742       10919.329869      0.000001      0.008353   \n",
              "\n",
              "     mean_iat down  std_iat down          website  \n",
              "0         0.001664      0.002853  dailymail.co.uk  \n",
              "1         0.001538      0.002827  dailymail.co.uk  \n",
              "2         0.001796      0.002960  dailymail.co.uk  \n",
              "3         0.001414      0.002590  dailymail.co.uk  \n",
              "4         0.001766      0.002943  dailymail.co.uk  \n",
              "..             ...           ...              ...  \n",
              "196       0.001913      0.011796   indiatimes.com  \n",
              "197       0.001971      0.010933   indiatimes.com  \n",
              "198       0.002100      0.010985   indiatimes.com  \n",
              "199       0.000627      0.001406   indiatimes.com  \n",
              "200       0.000783      0.001714   indiatimes.com  \n",
              "\n",
              "[201 rows x 24 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-55c7548e-d5c7-4738-b7cb-2993f2d33383\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Unnamed: 0</th>\n",
              "      <th>dport</th>\n",
              "      <th>sport</th>\n",
              "      <th>num_packets up</th>\n",
              "      <th>total_bytes up</th>\n",
              "      <th>min_pkt_size up</th>\n",
              "      <th>max_pkt_size up</th>\n",
              "      <th>mean_pkt_size up</th>\n",
              "      <th>std_pkt_size up</th>\n",
              "      <th>min_iat up</th>\n",
              "      <th>...</th>\n",
              "      <th>total_bytes down</th>\n",
              "      <th>min_pkt_size down</th>\n",
              "      <th>max_pkt_size down</th>\n",
              "      <th>mean_pkt_size down</th>\n",
              "      <th>std_pkt_size down</th>\n",
              "      <th>min_iat down</th>\n",
              "      <th>max_iat down</th>\n",
              "      <th>mean_iat down</th>\n",
              "      <th>std_iat down</th>\n",
              "      <th>website</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>443.0</td>\n",
              "      <td>58242.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>1768.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>569.0</td>\n",
              "      <td>98.222222</td>\n",
              "      <td>120.591462</td>\n",
              "      <td>0.000010</td>\n",
              "      <td>...</td>\n",
              "      <td>4804.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2948.0</td>\n",
              "      <td>320.266667</td>\n",
              "      <td>738.005562</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.008135</td>\n",
              "      <td>0.001664</td>\n",
              "      <td>0.002853</td>\n",
              "      <td>dailymail.co.uk</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>443.0</td>\n",
              "      <td>58254.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>1768.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>569.0</td>\n",
              "      <td>98.222222</td>\n",
              "      <td>120.591462</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>...</td>\n",
              "      <td>4856.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2948.0</td>\n",
              "      <td>303.500000</td>\n",
              "      <td>716.128480</td>\n",
              "      <td>0.000001</td>\n",
              "      <td>0.008505</td>\n",
              "      <td>0.001538</td>\n",
              "      <td>0.002827</td>\n",
              "      <td>dailymail.co.uk</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>443.0</td>\n",
              "      <td>58274.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>1768.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>569.0</td>\n",
              "      <td>98.222222</td>\n",
              "      <td>120.591462</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>...</td>\n",
              "      <td>4856.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2948.0</td>\n",
              "      <td>303.500000</td>\n",
              "      <td>716.128480</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.008046</td>\n",
              "      <td>0.001796</td>\n",
              "      <td>0.002960</td>\n",
              "      <td>dailymail.co.uk</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>443.0</td>\n",
              "      <td>58292.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>1768.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>569.0</td>\n",
              "      <td>98.222222</td>\n",
              "      <td>120.591462</td>\n",
              "      <td>0.000006</td>\n",
              "      <td>...</td>\n",
              "      <td>4856.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2948.0</td>\n",
              "      <td>303.500000</td>\n",
              "      <td>716.128480</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.008022</td>\n",
              "      <td>0.001414</td>\n",
              "      <td>0.002590</td>\n",
              "      <td>dailymail.co.uk</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>443.0</td>\n",
              "      <td>58304.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>1768.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>569.0</td>\n",
              "      <td>98.222222</td>\n",
              "      <td>120.591462</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>...</td>\n",
              "      <td>4857.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2948.0</td>\n",
              "      <td>303.562500</td>\n",
              "      <td>716.121030</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.008808</td>\n",
              "      <td>0.001766</td>\n",
              "      <td>0.002943</td>\n",
              "      <td>dailymail.co.uk</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>196</th>\n",
              "      <td>196</td>\n",
              "      <td>443.0</td>\n",
              "      <td>52426.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>4442.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>569.0</td>\n",
              "      <td>65.323529</td>\n",
              "      <td>65.419984</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>...</td>\n",
              "      <td>682907.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>37855.0</td>\n",
              "      <td>9105.426667</td>\n",
              "      <td>9375.864881</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.101507</td>\n",
              "      <td>0.001913</td>\n",
              "      <td>0.011796</td>\n",
              "      <td>indiatimes.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>197</td>\n",
              "      <td>443.0</td>\n",
              "      <td>52438.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>4506.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>569.0</td>\n",
              "      <td>65.304348</td>\n",
              "      <td>64.904750</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>...</td>\n",
              "      <td>683424.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>30460.0</td>\n",
              "      <td>7855.448276</td>\n",
              "      <td>7840.386185</td>\n",
              "      <td>0.000001</td>\n",
              "      <td>0.100975</td>\n",
              "      <td>0.001971</td>\n",
              "      <td>0.010933</td>\n",
              "      <td>indiatimes.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>198</td>\n",
              "      <td>443.0</td>\n",
              "      <td>52456.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>5118.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>569.0</td>\n",
              "      <td>63.185185</td>\n",
              "      <td>60.071023</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>...</td>\n",
              "      <td>683450.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>34804.0</td>\n",
              "      <td>7947.093023</td>\n",
              "      <td>8130.720952</td>\n",
              "      <td>0.000001</td>\n",
              "      <td>0.095903</td>\n",
              "      <td>0.002100</td>\n",
              "      <td>0.010985</td>\n",
              "      <td>indiatimes.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>199</td>\n",
              "      <td>443.0</td>\n",
              "      <td>52466.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>4546.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>569.0</td>\n",
              "      <td>64.942857</td>\n",
              "      <td>64.503654</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>...</td>\n",
              "      <td>682575.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>39148.0</td>\n",
              "      <td>9892.391304</td>\n",
              "      <td>9796.493431</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.007941</td>\n",
              "      <td>0.000627</td>\n",
              "      <td>0.001406</td>\n",
              "      <td>indiatimes.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>200</td>\n",
              "      <td>443.0</td>\n",
              "      <td>52492.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>4090.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>569.0</td>\n",
              "      <td>67.049180</td>\n",
              "      <td>68.902933</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>...</td>\n",
              "      <td>682060.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>39148.0</td>\n",
              "      <td>11000.967742</td>\n",
              "      <td>10919.329869</td>\n",
              "      <td>0.000001</td>\n",
              "      <td>0.008353</td>\n",
              "      <td>0.000783</td>\n",
              "      <td>0.001714</td>\n",
              "      <td>indiatimes.com</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>201 rows × 24 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-55c7548e-d5c7-4738-b7cb-2993f2d33383')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-55c7548e-d5c7-4738-b7cb-2993f2d33383 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-55c7548e-d5c7-4738-b7cb-2993f2d33383');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "display(biflow_features)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gadxK119w0d1"
      },
      "source": [
        "## 4. Split the dataset in train (70%) and test (30%) set\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5FWmR4mwwShS"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "#X = biflow_features.drop(['ip_proto','direction','sport','dport'],axis=1).dropna();\n",
        "X = biflow_features.drop(['sport','dport'],axis=1).dropna();\n",
        "Y = X['website']\n",
        "X = X.drop(['website'],axis=1)\n",
        "\n",
        "# Normalization\n",
        "scaler = StandardScaler()\n",
        "X_norm = scaler.fit_transform(X)\n",
        "\n",
        "# Split X_norm in train and test set\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X_norm, Y, test_size=0.3, shuffle=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QnPKy1pZxYzt"
      },
      "source": [
        "## 5. Evaluate the performance (accuracy and confusion matrix) of a k-NN approach. Plot the accuracy-vs-K relation on a figure, for k = 1..10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        },
        "id": "mu967Hllx6un",
        "outputId": "78bb3a50-39ee-45c4-dac5-a7588d941f0b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "K = 1 -> 0.95\n",
            "K = 2 -> 0.95\n",
            "K = 3 -> 0.9666666666666667\n",
            "K = 4 -> 0.9666666666666667\n",
            "K = 5 -> 0.9666666666666667\n",
            "K = 6 -> 0.95\n",
            "K = 7 -> 0.9333333333333333\n",
            "K = 8 -> 0.9333333333333333\n",
            "K = 9 -> 0.9333333333333333\n",
            "K = 10 -> 0.9333333333333333\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Get accuracy for each value of K in 1 to 10\n",
        "ACCURACY = []\n",
        "for k in range(1,11):\n",
        "  knn = KNeighborsClassifier(n_neighbors=k, weights='distance')\n",
        "  knn.fit(X_train, Y_train)\n",
        "  knn_predict = knn.predict(X_test)\n",
        "  accuracy = accuracy_score(Y_test, knn_predict)\n",
        "  ACCURACY.append(accuracy)\n",
        "  print('K =',k,'->',accuracy)\n",
        "\n",
        "# Plot Accuracy over K\n",
        "fig, ax = plt.subplots(figsize=(15, 10));\n",
        "ax.grid(True)\n",
        "ax.set_title(\"Accuracy-vs-K relation (1 attempt)\")\n",
        "ax.set_xlabel(\"K Value\")\n",
        "ax.set_ylabel(\"Accuracy\")\n",
        "ax.set_xticks(range(1,11))\n",
        "_ = ax.plot(range(1,11), ACCURACY, 'b')\n",
        "_ = ax.scatter(range(1,11), ACCURACY, s=90, facecolors='none', edgecolors='b')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        },
        "id": "MrMCyGj6wHi7",
        "outputId": "a6233d8e-443e-4997-f7cf-bc566d3ac21b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Since the accuracy depends on how the set is splitted, let's analyze it from \n",
        "# the statistical point of view by collecting data with different shuffles\n",
        "\n",
        "nb_attempts = 100 # parameter used also for the following \n",
        "\n",
        "ACCURACY = [[0 for x in range(nb_attempts)] for y in range(10)] \n",
        "\n",
        "# For each attempt get accuracy for each value of K in 1 to 10\n",
        "for attempt in range(nb_attempts):\n",
        "  for k in range(1,11):\n",
        "    knn = KNeighborsClassifier(n_neighbors=k, weights='distance')\n",
        "    # ReSplit X_norm in train and test set\n",
        "    X_train, X_test, Y_train, Y_test = train_test_split(X_norm, Y, test_size=0.3, shuffle=True)\n",
        "    knn.fit(X_train, Y_train)\n",
        "    knn_predict = knn.predict(X_test)\n",
        "    ACCURACY[k-1][attempt] = accuracy_score(Y_test, knn_predict)\n",
        "\n",
        "plt.figure(figsize=(15,10))\n",
        "\n",
        "plt.grid(visible=True)\n",
        "plt.title(\"Classical Approach: Accuracy-vs-K relation (\"+ str(nb_attempts) +\" shuffles)\")\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('K value')\n",
        "plt.xticks(range(1,11))\n",
        "_ = plt.boxplot(ACCURACY)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DsT1TXNU-rpS"
      },
      "source": [
        "The obtained accuracy depends on the homepages updates during the curls and how the dataset is splitted.\n",
        "\n",
        "From boxplot it is possible to note that the average of the values ​​decreases as k increases, furthermore increasing k increases the variance of accuracy.\n",
        "\n",
        "In general the obtained accuracy is acceptable (over 70% in most of the cases)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vtv0q5svyzup"
      },
      "source": [
        "## 6. Create a new test set, visiting the same web sites (3 times) after some time (e.g., 1 day). Evaluate the performance obtained using the old training set and comment the results."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 375
        },
        "id": "XPTyqjakAHy0",
        "outputId": "445702cd-881a-4e38-9d1c-8d90df69f719"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# load the dataset obtained in 26/03/22\n",
        "old_biflow_features = pd.read_csv('26_03_22.csv')\n",
        "\n",
        "X = old_biflow_features.drop(['sport','dport'],axis=1).dropna()\n",
        "Y = X['website']\n",
        "X = X.drop(['website'],axis=1)\n",
        "\n",
        "ACCURACY_OLD_TRAIN = [0 for y in range(10)]\n",
        "\n",
        "# As before, I analyzed the statistical accuracy with the same number of attempts\n",
        "#for attempt in range(nb_attempts):\n",
        "  # extract from it the 70% of the rows for training\n",
        "X_train = X\n",
        "Y_train = Y\n",
        "\n",
        "# concatenate to the existing testing set the datasets obtained in the next days\n",
        "csv_files = ['27_03_22.csv', '28_03_22.csv']\n",
        "i=0 # variable to check if for is running the frist cycle (i=0) or not (i=1)\n",
        "for csv_file in csv_files:\n",
        "    new_biflow_features = pd.read_csv(csv_file)\n",
        "\n",
        "    #Y_TEST\n",
        "    # if it is the first cycle assign website column to Y_test \n",
        "    if i==0:\n",
        "        Y_test = new_biflow_features['website']\n",
        "    else: # otherwise concat new Y to Y_test\n",
        "        Y_test = pd.concat([Y_test, new_biflow_features['website']])\n",
        "\n",
        "    #X_TEST same procedure as Y_TEST\n",
        "    new_biflow_features = new_biflow_features.drop(['sport','dport','website'],axis=1).dropna()\n",
        "    if i==0:\n",
        "        X_test = new_biflow_features\n",
        "        i+=1\n",
        "    else:\n",
        "        X_test = np.concatenate([X_test, new_biflow_features.to_numpy()])\n",
        "\n",
        "# Normalize X_train and X_test sets\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.fit_transform(X_test)\n",
        "\n",
        "# extract ACCURACY_OLD_TRAIN[k-1][attempt] for each k\n",
        "for k in range(1,11):\n",
        "    knn = KNeighborsClassifier(n_neighbors=k, weights='distance')\n",
        "    knn.fit(X_train, Y_train)\n",
        "    knn_predict = knn.predict(X_test)\n",
        "    accuracy = accuracy_score(Y_test, knn_predict)\n",
        "    ACCURACY_OLD_TRAIN[k-1] = accuracy\n",
        "\n",
        "\n",
        "# plot Accuracy over K comparing with the previous result to evaluate the perfomances\n",
        "f, (ax1, ax2) = plt.subplots(1, 2, sharey=True, figsize=(25,10))\n",
        "f.suptitle(\"Accuracy-vs-K relations\" , fontsize=16)\n",
        "\n",
        "ax1.grid(visible=True)\n",
        "ax1.set_title(\"Mean accuracy from Classical Approach(\"+ str(nb_attempts) +\" different shuffles)\")\n",
        "ax1.set_ylabel('Accuracy')\n",
        "ax1.set_xlabel('K value')\n",
        "ax1.set_xticks(range(1,11))\n",
        "_ = ax1.plot(range(1,11),np.array(ACCURACY).mean(axis=1))\n",
        "_ = ax1.scatter(range(1,11), np.array(ACCURACY).mean(axis=1), s=90, facecolors='none', edgecolors='b')\n",
        "\n",
        "ax2.grid(visible=True)\n",
        "ax2.set_title(\"Classical Approach using old training set\")\n",
        "ax2.set_ylabel('Accuracy')\n",
        "ax2.set_xlabel('K value')\n",
        "ax2.set_xticks(range(1,11))\n",
        "ax2.yaxis.set_tick_params(labelbottom=True)\n",
        "_ = ax2.plot(range(1,11),ACCURACY_OLD_TRAIN)\n",
        "_ = ax2.scatter(range(1,11), ACCURACY_OLD_TRAIN, s=90, facecolors='none', edgecolors='b')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R5C8yTBrfk1Q"
      },
      "source": [
        "As expected, the perfomances dropped significantly."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rGyIRFmjsYR8"
      },
      "source": [
        "This deterioration in performance is because the news websites change frequently their homepage and consequently their size that should have changed the values of k-NN features."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oJUSyEru3EZ6"
      },
      "source": [
        "## Bonus Point"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1K3-iXJkdqgW"
      },
      "outputs": [],
      "source": [
        "# get_cumulative_function provides the cumulative trace of a bidirectional flow as described in the provided slide set\n",
        "def get_cumulative_function(direction, df_flow, df_reverse_flow):\n",
        "  c_funct = [0]\n",
        "\n",
        "  # add the field 'downlink' to all packets to store its direction\n",
        "  if direction == 'up':\n",
        "    df_flow['downlink']=False\n",
        "    df_reverse_flow['downlink']=True\n",
        "  else:\n",
        "    df_flow['downlink']=True\n",
        "    df_reverse_flow['downlink']=False\n",
        "  \n",
        "  # join direct and reverse packets and sort by the frame number\n",
        "  df_join = pd.concat([df_flow, df_reverse_flow]).sort_index()\n",
        "  # exclude the TCP ACKs that do not carry data by removing all packets that contain no data and just acknowledge data coming from the other side\n",
        "  df_join = df_join[df_join['tcp.len'] != 0]\n",
        "\n",
        "  #display(df_join)\n",
        "  # for each packet in the df_join\n",
        "  for index, pkt in df_join.iterrows():\n",
        "    # append its length with positive sign if incoming(downlink), negative sign otherwise (uplink)\n",
        "    if pkt['downlink']:\n",
        "      c_funct.append(c_funct[-1] + pkt['ip.len'])\n",
        "    else:\n",
        "      c_funct.append(c_funct[-1] - pkt['ip.len'])\n",
        "  return c_funct"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qyV-fikZ3ECP",
        "outputId": "145e01e5-d114-4d95-bd0a-4ac93b26181d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file   www.msn.com.csv\n",
            "Processing file   www.indiatimes.com.csv\n",
            "Processing file   www.theguardian.com.csv\n",
            "Processing file   www.dailymail.co.uk.csv\n",
            "Processing file   www.cnbc.com.csv\n",
            "Processing file   www.ndtv.com.csv\n",
            "Processing file   www.abc.net.au.csv\n",
            "Processing file   www.news.google.com.csv\n",
            "Processing file   www.express.co.uk.csv\n",
            "Processing file   www.cnn.com.csv\n",
            "Processing file   www.nypost.com.csv\n",
            "Processing file   www.washingtonpost.com.csv\n",
            "Processing file   www.foxnews.com.csv\n",
            "Processing file   www.finance.yahoo.com.csv\n",
            "Processing file   www.rt.com.csv\n",
            "Processing file   www.bbc.co.uk.csv\n",
            "Processing file   www.timesofindia.com.csv\n",
            "Processing file   www.news18.com.csv\n",
            "Processing file   www.news.yahoo.com.csv\n",
            "Processing file   www.nytimes.com.csv\n"
          ]
        }
      ],
      "source": [
        "# As done previously, scan all measurement files to obtain the df_flows_tcp dataframe and\n",
        "# compute the cumulative function for each flow/reverse flow using the function above\n",
        "pd.options.mode.chained_assignment = None  # default='warn'\n",
        "path = './26_03_22_captures/'\n",
        "tsharkfiles = [f for f in listdir(path) if isfile(join(path, f))]\n",
        "\n",
        "all_flows_fingerprints = pd.DataFrame(columns=['c_funct', 'website'])\n",
        "\n",
        "for f in tsharkfiles:\n",
        "  if (f.startswith('www') and f.endswith('.csv')):\n",
        "    print('Processing file  ', f)\n",
        "    df = pd.read_csv(join(path,f))\n",
        "    df_tcp = df[df[\"ip.proto\"] == 6]\n",
        "    df_flows_tcp = df_tcp.groupby([\"ip.src\",\"ip.dst\",\"tcp.srcport\",\"tcp.dstport\"])\n",
        "    to_ignore_flows = []\n",
        "    for flow,df_flow in df_flows_tcp:\n",
        "      if flow not in to_ignore_flows:\n",
        "        reverse_flow = (flow[1], flow[0], flow[3], flow[2])\n",
        "        if reverse_flow in df_flows_tcp.groups:\n",
        "          df_reverse_flow = df_flows_tcp.get_group(reverse_flow)\n",
        "          to_ignore_flows.append(reverse_flow)\n",
        "        else:\n",
        "          df_reverse_flow = pd.DataFrame()\n",
        "\n",
        "        c_funct = get_cumulative_function(get_direction(flow[0]), df_flow, df_reverse_flow)\n",
        "        all_flows_fingerprints.loc[len(all_flows_fingerprints.index)] = [c_funct, f[4:len(f)-4]]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "tT0KlutyV0TA",
        "outputId": "10e8073e-61bb-451f-98f4-b731baece597"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                               c_funct      website\n",
              "0    [0, -557, 3416, 3218, 3309, 3418, 3325, 3229, ...      msn.com\n",
              "1    [0, -557, 3416, 3218, 3309, 3418, 3325, 3229, ...      msn.com\n",
              "2    [0, -557, 3416, 3218, 3309, 3418, 3325, 3229, ...      msn.com\n",
              "3    [0, -557, 3416, 3218, 3309, 3418, 3325, 3229, ...      msn.com\n",
              "4    [0, -557, 3416, 3218, 3309, 3418, 3325, 3229, ...      msn.com\n",
              "..                                                 ...          ...\n",
              "196  [0, -569, 3167, 3022, 3125, 3020, 2912, 2818, ...  nytimes.com\n",
              "197  [0, -569, 3167, 3022, 3125, 3020, 2912, 2818, ...  nytimes.com\n",
              "198  [0, -569, 3167, 3022, 3125, 3020, 2912, 2818, ...  nytimes.com\n",
              "199  [0, -569, 3167, 3022, 3125, 3020, 2912, 2818, ...  nytimes.com\n",
              "200  [0, -569, 3167, 3022, 3125, 3020, 2912, 2818, ...  nytimes.com\n",
              "\n",
              "[201 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cf55bf52-6336-4074-8017-0bf75904c9cb\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>msn.com</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>[0, -557, 3416, 3218, 3309, 3418, 3325, 3229, ...</td>\n",
              "      <td>msn.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>[0, -557, 3416, 3218, 3309, 3418, 3325, 3229, ...</td>\n",
              "      <td>msn.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>[0, -557, 3416, 3218, 3309, 3418, 3325, 3229, ...</td>\n",
              "      <td>msn.com</td>\n",
              "    </tr>\n",
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              "      <td>[0, -569, 3167, 3022, 3125, 3020, 2912, 2818, ...</td>\n",
              "      <td>nytimes.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>[0, -569, 3167, 3022, 3125, 3020, 2912, 2818, ...</td>\n",
              "      <td>nytimes.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>[0, -569, 3167, 3022, 3125, 3020, 2912, 2818, ...</td>\n",
              "      <td>nytimes.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>[0, -569, 3167, 3022, 3125, 3020, 2912, 2818, ...</td>\n",
              "      <td>nytimes.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>[0, -569, 3167, 3022, 3125, 3020, 2912, 2818, ...</td>\n",
              "      <td>nytimes.com</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>201 rows × 2 columns</p>\n",
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          "metadata": {}
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      ],
      "source": [
        "display(all_flows_fingerprints)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ki140UGl-O6u"
      },
      "outputs": [],
      "source": [
        "# Number of samples of piecewise linear interpolant of the cumulative function\n",
        "M = 20"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "EUmDnfTuNYqB",
        "outputId": "c1fa29d6-8c8c-40f4-815d-525116456a4f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Example to see what I have done:\n",
        "# for each flow the algorithm will sample the piecewise linear interpolant of the cumulative function\n",
        "n_flow = 155 # flow index to visualize\n",
        "from_nth_packet = 0\n",
        "up_to_nth_packet = 15 # how many packets\n",
        "up_to_nth_packet = len(all_flows_fingerprints.loc[n_flow].c_funct) # comment this line if you want to analyze a limited nb. of pkts (up_to_nth_packet)\n",
        "\n",
        "fp = all_flows_fingerprints.loc[n_flow].c_funct[from_nth_packet:up_to_nth_packet]\n",
        "\n",
        "xp = range(0, up_to_nth_packet-from_nth_packet)\n",
        "x = np.linspace(0, up_to_nth_packet-from_nth_packet-1, num=M)\n",
        "fig, ax = plt.subplots(figsize=(10, 8))\n",
        "ax.plot(x, np.interp(x, xp, fp), 'bo', label='Samples of piecewise linear interpolant')\n",
        "ax.plot(xp, fp, label='Cumulative Trace')\n",
        "ax.legend()\n",
        "ax.set_title('Cumulative trace of flow '+str(n_flow)+' and sampled version with M='+str(M))\n",
        "ax.set_xlabel('Packet number')\n",
        "ax.set_ylabel('Cumulative Trace [Bytes]')\n",
        "ax.grid()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DbQcRUEMNsTl"
      },
      "outputs": [],
      "source": [
        "# Create X and Y arrays\n",
        "cols = []\n",
        "for i in range(0,M):\n",
        "  cols.append(\"Sample #\"+str(i))\n",
        "X = pd.DataFrame(columns=cols)\n",
        "\n",
        "for idx, flow in all_flows_fingerprints.iterrows():\n",
        "  fp = all_flows_fingerprints.loc[idx].c_funct\n",
        "  xp = range(0, len(fp))\n",
        "  x = np.linspace(0, len(fp)-1, num=M)\n",
        "  X = X.append(dict(zip(X.columns, np.interp(x, xp, fp).tolist())), ignore_index=True)\n",
        "\n",
        "Y = all_flows_fingerprints['website']\n",
        "\n",
        "# Normalization\n",
        "scaler = StandardScaler()\n",
        "X_norm = scaler.fit_transform(X)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "-lISLvhM9-FN",
        "outputId": "ec55093d-c4b5-419c-b611-074a336c816a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Since the accuracy depends on how the set is splitted, let's analyze it from \n",
        "# the statistical point of view by collecting data with different shuffles\n",
        "ACCURACY_TRACE = [[0 for x in range(nb_attempts)] for y in range(10)] \n",
        "\n",
        "# For each attempt get accuracy for each value of K in 1 to 10\n",
        "for attempt in range(nb_attempts):\n",
        "  for k in range(1,11):\n",
        "    knn = KNeighborsClassifier(n_neighbors=k, weights='distance')\n",
        "    # ReSplit X_norm in train and test set\n",
        "    X_train, X_test, Y_train, Y_test = train_test_split(X_norm, Y, test_size=0.3, shuffle=True)\n",
        "    knn.fit(X_train, Y_train)\n",
        "    knn_predict = knn.predict(X_test)\n",
        "    ACCURACY_TRACE[k-1][attempt] = accuracy_score(Y_test, knn_predict)\n",
        "\n",
        "# plot Accuracy over K comparing with the classical approach\n",
        "f, (ax1, ax2) = plt.subplots(1, 2, sharey=True, figsize=(25,10))\n",
        "f.suptitle(\"Accuracy-vs-K relation (\"+ str(nb_attempts) +\" attempts)\", fontsize=16)\n",
        "\n",
        "ax1.grid(visible=True)\n",
        "ax1.set_title(\"Trace Approach\")\n",
        "ax1.set_ylabel('Accuracy')\n",
        "ax1.set_xlabel('K value')\n",
        "ax1.set_xticks(range(1,11))\n",
        "_ = ax1.boxplot(ACCURACY_TRACE)\n",
        "\n",
        "ax2.grid(visible=True)\n",
        "ax2.set_title(\"Classical Approach\")\n",
        "ax2.set_ylabel('Accuracy')\n",
        "ax2.set_xlabel('K value')\n",
        "ax2.set_xticks(range(1,11))\n",
        "ax2.yaxis.set_tick_params(labelbottom=True)\n",
        "_ = ax2.boxplot(ACCURACY)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fAF9elWcHN9I"
      },
      "source": [
        "This method does performs more or less as the previous one. With respect to the classical approach it also consider the time evolution but both of them are based on the size of exchanged packets."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Homework_2_Network_Measurements_Lab.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.6+"
    }
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  "nbformat": 4,
  "nbformat_minor": 0
}