COMP_480_Project.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMHtAhw3Pzmjx9E2TNrrdyv",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gavin2059/AttackSketch/blob/main/COMP_480_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install bitarray"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oxJs7K_J4hvH",
        "outputId": "b9a9834f-73f0-4922-c14b-e2ad061627dd"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting bitarray\n",
            "  Downloading bitarray-2.6.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (241 kB)\n",
            "\u001b[K     |████████████████████████████████| 241 kB 6.8 MB/s \n",
            "\u001b[?25hInstalling collected packages: bitarray\n",
            "Successfully installed bitarray-2.6.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "JwKeBxjc4Xru"
      },
      "outputs": [],
      "source": [
        "import random\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pylab as plt\n",
        "from sklearn.utils import murmurhash3_32\n",
        "from bitarray import bitarray\n",
        "import math\n",
        "import csv\n",
        "import pandas as pd\n",
        "import string\n",
        "from tqdm import tqdm"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def hashfunc(m):\n",
        "    p = 1048573\n",
        "    a = random.randint(1, p)\n",
        "    b = random.randint(1, p)\n",
        "    c = random.randint(1, p)\n",
        "    def three_universal_hash(x):\n",
        "        val = 0\n",
        "        for char in x:\n",
        "            val += a * ord(char)**2 + b * ord(char) + c\n",
        "        return val % m\n",
        "    return three_universal_hash\n",
        "\n",
        "# def hashfunc(m):\n",
        "#     rand = random.getrandbits(31);\n",
        "#     return lambda x : (murmurhash3_32(key=x, seed=rand) % m)"
      ],
      "metadata": {
        "id": "Sp1O1Ga14utp"
      },
      "execution_count": 345,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"user-ct-test-collection-01.txt\", sep=\"\\t\")\n",
        "urllist = data.ClickURL.dropna().unique()"
      ],
      "metadata": {
        "id": "w9Hu8b-t452o"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(urllist)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cXvSlkGW46aQ",
        "outputId": "059214d7-c23b-496a-e4bd-fb6f4e30f9d2"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "377870"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class BloomFilter ():\n",
        "  def __init__(self, n, fp_rate, buffer_size):\n",
        "      # Compute R value (number of bits in the bitmap)\n",
        "      R = math.ceil(math.log(fp_rate) / math.log(0.618) * n)\n",
        "      # Round R to the next highest power of 2\n",
        "      R = 1 << math.ceil(math.log2(R - 1))\n",
        "      # Compute the number of hash functions\n",
        "      self.k = round(math.log(2) * R / n)\n",
        "      self.bitmap = bitarray(R)\n",
        "      self.bitmap[:] = 0\n",
        "      self.hashfun_list = [hashfunc(R) for i in range(self.k)]\n",
        "      self.buffer_size = buffer_size\n",
        "      self.false_positive_buffer = set()\n",
        "      self.buffer_url_tracker = []\n",
        "      self.R = R\n",
        "\n",
        "  def insert(self, key):\n",
        "    for i in range(self.k):\n",
        "      self.bitmap[self.hashfun_list[i](key)] = 1\n",
        "\n",
        "  def test(self, key):\n",
        "    if key in self.false_positive_buffer:\n",
        "        return False\n",
        "    res = True\n",
        "    for i in range(self.k):\n",
        "      if self.bitmap[self.hashfun_list[i](key)] != 1:\n",
        "        res = False\n",
        "        break\n",
        "    if res:\n",
        "        self.false_positive_buffer.add(key)\n",
        "        self.buffer_url_tracker.append(key)\n",
        "        if len(self.false_positive_buffer) > self.buffer_size:\n",
        "            self.false_positive_buffer.remove(self.buffer_url_tracker.pop(0))\n",
        "    return res"
      ],
      "metadata": {
        "id": "xHov0-_k4yRk"
      },
      "execution_count": 184,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Attacher ():\n",
        "    def __init__(self, bloom_filter : BloomFilter, epsilon, attack_threshold):\n",
        "        self.bloom_filter = bloom_filter\n",
        "        self.epsilon = epsilon\n",
        "        self.attack_threshold = attack_threshold\n",
        "        self.buffer = set()\n",
        "        self.positive_queue = []\n",
        "        self.num_positive_in_queue = 0\n",
        "        self.records = list()\n",
        "        self.total_episodes = 0\n",
        "        self.threshold_reached = False\n",
        "\n",
        "    def make_url(self):\n",
        "        if len(self.buffer) < self.attack_threshold or random.random() < self.epsilon:\n",
        "            return 'http://'.join(random.choices(string.punctuation + string.digits + string.ascii_lowercase, k=random.randint(8, 43))) \n",
        "        else:\n",
        "            if not self.threshold_reached:\n",
        "                print(self.total_episodes)\n",
        "                self.threshold_reached = True\n",
        "            return random.sample(self.buffer, 1)[0]\n",
        "\n",
        "    def attack(self, num_episodes, plot_interval=100, plot=True):\n",
        "        for i in range(num_episodes):\n",
        "            url = self.make_url()\n",
        "            res = self.bloom_filter.test(url)\n",
        "            if res:\n",
        "                self.buffer.add(url)\n",
        "                self.num_positive_in_queue += 1\n",
        "                self.positive_queue.append(1)\n",
        "            else:\n",
        "                self.positive_queue.append(0)\n",
        "            if len(self.positive_queue) > plot_interval:\n",
        "                self.num_positive_in_queue -= self.positive_queue.pop(0)\n",
        "            if i % plot_interval == 0:\n",
        "                self.records.append(self.num_positive_in_queue)\n",
        "            self.total_episodes += 1\n",
        "        if plot:\n",
        "            plt.plot(range(0, self.total_episodes, plot_interval), self.records)"
      ],
      "metadata": {
        "id": "mwpweYip42qq"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "malicious_url = random.sample(set(urllist), 10000)\n",
        "bloom_filter = BloomFilter(10000, 0.01, 20)"
      ],
      "metadata": {
        "id": "BGSjgGIl4_z_"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(malicious_url)):\n",
        "    bloom_filter.insert(malicious_url[i])"
      ],
      "metadata": {
        "id": "KtU8MFOa5ZGE"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Adjust attack threshold"
      ],
      "metadata": {
        "id": "CvgkNavQNIzc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "attacker = Attacher(bloom_filter, 0.01, 60)"
      ],
      "metadata": {
        "id": "wr52NuI93158"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "attacker.attack(500000, plot_interval=1000)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "rZAn-c6F5a2M",
        "outputId": "bc453dd7-82e3-4a9c-d8cc-faae790f315f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "attacker1 = Attacher(bloom_filter, 0.01, 80)"
      ],
      "metadata": {
        "id": "0s6fNFgIxS2n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "attacker1.attack(2500000, plot_interval=1000)"
      ],
      "metadata": {
        "id": "tZ-B-KS1yCrU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "636a5345-4397-42bc-8f61-2b285276110c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1915417\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "attacker2 = Attacher(bloom_filter, 0.01, 40)"
      ],
      "metadata": {
        "id": "YFifchtd_mQ3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "attacker2.attack(1000000, plot_interval=1000)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "VyFJbTPK_q5q",
        "outputId": "93247196-592e-4c8b-b6c3-c3ef9f9ae43c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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78ZVLRrF1zyGuO+c4Zq94L3C9U4f0YcJJJbzwVvPDkzx8Q/ylp/7zFGcd1y/2qMFFX58Y90G78a4p7DtUx5R7F/Gzq8ZRUVnNwdr6lM6xBH0ARP8WP50buYG/W2E+44b1jTyA5KITycszTiiOfOBcMmZg7EEk2eq5K7mL+LRUIVj9vcs45TtzAbjv2jM4Z0TTum7UQe/JPNGrZ7p3KfDmg5O9c3B2aX+WbtxNXjPdxxs+WBrYPnnsIJ5ZuT3pB1M0mZgZ//vpsxg3tG9sPuobl4/mixeP5OCReso37mFY/+6svfPywCtPehQV8KvrzqSstF+TZWWl/Zk8dhBnDm+6DKC4ZxGzbjobaP45qxNPGtAkuT/5uXObvUrGb8KoklhyH9a/e5OnMvXpVshL37gIgPEBNfpkzIyzS/tz/XlNT15H6/rFvYr4wsQT+Y9HyznNO9bDj+nOqu9dRo8u+bGx9LNVc1dyFwmQ+Pi4kl5FVB2ojbvM8d9Oa/566OkXjGD+2krOOi6S4G46v5RG57j+3NLA9c87oZjJYwcHPq4tqiDPOLZP8GV493zyDL77kSOBPc8Vd1waV++9LKD2DZCXZ/QoKqBHUQGTx0Z6wM0l0ug6USOKezD19Mhx+dV1ZyXdzm/SyQP53t/XMPX0Y3lqeXwvPqjEkc7JzFsmnsjP570dm08skSVzTI8ugeU2vyd8JSq/myecSNcu+Vx11lAK8vN4+baL4r7d9PTeQ0UFeXQtzEvpIeKtoeQukoLnvvwh9h+uS2ub804ojiubFBXkByaM7089hbFD+jDIu/okKJn+8lNn0KMon4tGNz3BF9WlII8BvYIv0evTPf2rblqjuZPEyQzr3z12nBKTu1+6l5ICTb4BpVpuW/btS9J+rahuXfL5/ISjf+dk18QX9yxi7Z1tO0HcHCV3EZ9knag+3QuzliCT9eT9gi5p7CiP3PAB3tye+vj1AN/58BgG9W752vBbJ42kT7fC2AnlaC/91kkjufG8lm9Aakkq9fSwUHIXCfA+ygFpmzh6ABNHp3fN/o0p3BkKcOuk+LtDTx3Sh4ofXhF4lUyqfvLx096XT6ZSchfxcS2eUpX21pbEDnD1B4Zx9QfafhfsP754PpUHatu8n/ai5C4SoLmO+5C+3bhwVEm7xSKZl+7gXhC5Jv6ULMSSLUruIj6pXLjw8m0XZT8QyZo5t17A4CRXHIWJkrtIANXcw6ulu47DQsMPiPio4i5hoeQuEiDxJiaRXKPkLuKTbNAnkVyj5C4SRB13yXFK7iIiIaTkLuKjqoyEhZK7SABVZSTXKbmLiISQkrtIgPfT6IESTkruIj6quUtYKLmLBFC/XXKdkruIj4b8lbBIObmbWb6ZvW5mT3vzI8xsiZlVmNnjZtbFay/y5iu85aXZCV0ke1Ryl1yXTs/9S8CbvvkfA3c7504E9gDTvfbpwB6v/W5vPZGcoJq7hEVKyd3MhgJTgAe9eQMuAp70VpkFXOlNT/Xm8ZZfbLr0QHKM3rGS61Ltud8DfB1o9OaPAfY65+q9+S3AEG96CLAZwFu+z1s/jpnNMLNyMyuvqqpqZfgimaWOu4RFi8ndzP4NqHTOLcvkCzvnZjrnypxzZSUlemSZiEgmpfIkpg8CHzGzyUBXoDfwP0BfMyvweudDga3e+luBYcAWMysA+gC7Mh65SBZpPHfJdS323J1z33TODXXOlQLXAM87564DFgCf8FabBjzlTc/25vGWP+80SLbkCL1VJSzacp37N4CvmFkFkZr6Q177Q8AxXvtXgNvaFqJI+9MJVcl1aT0g2zn3AvCCN70BODtgncPAVRmITaTdqd8uYaE7VEVEQkjJXcRHJXcJCyV3kQC6705ynZK7SBx13SUclNxFAqjfLrlOyV3ERzV3CQsldxGREFJyFwmg86mS65TcRXxUlZGwUHIXCaCBwyTXKbmL+OiEqoSFkrtIANXcJdcpuYv4OFXdJSSU3EUCqOMuuU7JXcRHNXcJCyV3kQCquUuuU3IX8VHPXcJCyV1EJISU3EUCqS4juU3JXcRHl0JKWCi5iwTQCVXJdUruIj46oSphoeQuEkAdd8l1Su4iIiGk5C4SwFR0lxyn5C7io5q7hIWSu0gA9dsl17WY3M2sq5ktNbMVZrbazL7ntY8wsyVmVmFmj5tZF6+9yJuv8JaXZvdXEMkcXecuYZFKz70WuMg5Nw44HbjczMYDPwbuds6dCOwBpnvrTwf2eO13e+uJiEg7ajG5u4hqb7bQ+3HARcCTXvss4Epveqo3j7f8YtPZKckR0Zq73rGS61KquZtZvpktByqBecB6YK9zrt5bZQswxJseAmwG8JbvA44J2OcMMys3s/Kqqqq2/RYiIhInpeTunGtwzp0ODAXOBka39YWdczOdc2XOubKSkpK27k4ko9Rzl1yX1tUyzrm9wALgXKCvmRV4i4YCW73prcAwAG95H2BXRqIVyTKdTpWwSOVqmRIz6+tNdwMuAd4kkuQ/4a02DXjKm57tzeMtf945XT0sucV0MaTkuIKWV2EwMMvM8ol8GDzhnHvazNYAfzSzHwCvAw956z8E/M7MKoDdwDVZiFskK9QPkbBoMbk7594Azgho30Ck/p7Yfhi4KiPRiXQUddwlx+kOVREf9dslLJTcRQKo4y65TsldxEcldwkLJXcRkRBScheJE+m6a8QMyXVK7iIiIaTkLhJA/XbJdUruIj46oSphoeQuEkAld8l1Su4iPuq4S1gouYsE0MBhkuuU3EV8VHOXsFByFwmgmrvkOiV3ER8N+SthoeQuIhJCSu4iPtF+u6oykuuU3EVEQkjJXSSIuu6S45TcRXx0PlXCQsldJIBuYpJcp+Qu4uM0AIGEhJK7SADdxCS5TsldxE8ddwkJJXeRAOq4S65TchfxUcddwkLJXUQkhJTcRXyi17mbzqhKjmsxuZvZMDNbYGZrzGy1mX3Ja+9vZvPMbJ33bz+v3czsXjOrMLM3zOzMbP8SIiISL5Weez3wX865McB44BYzGwPcBsx3zo0E5nvzAFcAI72fGcADGY9aJEui17mr4y65rsXk7pzb5px7zZs+ALwJDAGmArO81WYBV3rTU4FHXcRioK+ZDc545CIiklRaNXczKwXOAJYAA51z27xF24GB3vQQYLNvsy1eW+K+ZphZuZmVV1VVpRm2SHap4y65LuXkbmY9gT8Dtzrn9vuXucjja9K6isw5N9M5V+acKyspKUlnU5Gs0cBhEhYpJXczKySS2H/vnPuL17wjWm7x/q302rcCw3ybD/XaRHKGau6S61K5WsaAh4A3nXO/8C2aDUzzpqcBT/nar/eumhkP7POVb0Q6NXXcJSwKUljng8CngZVmttxr+xZwF/CEmU0H3gWu9pY9A0wGKoAa4MaMRizSLtR1l9zWYnJ3zr1E8nf6xQHrO+CWNsYl0iGciu4SErpDVUQkhJTcRXyi/XadUJVcp+QuIhJCSu4iftGBwzo2CpE2U3IXEQkhJXeRABryV3KdkruIj9NtTBISSu4iAdRvl1yn5C7io3uYJCyU3EUCqOQuuU7JXcRHPXcJCyV3kQCmqrvkOCV3ER913CUslNxFREJIyV3EJzrkr06oSq5TchcRCSEldxGREFJyF/HRCVUJCyV3kQCquUuuU3IX8dFNTBIWSu4iAXQTk+Q6JXeROOq6SzgouYsEUM1dcp2Su4iPau4SFkruIiIhpOQu4hPtuKssI7lOyV1EJIRaTO5m9rCZVZrZKl9bfzObZ2brvH/7ee1mZveaWYWZvWFmZ2YzeJFMi9bcdSmk5LpUeu6/BS5PaLsNmO+cGwnM9+YBrgBGej8zgAcyE6aIiKSjxeTunFsI7E5ongrM8qZnAVf62h91EYuBvmY2OFPBirQX1dwl17W25j7QObfNm94ODPSmhwCbfett8dqaMLMZZlZuZuVVVVWtDEMks5xuYpKQaPMJVRd5ukHa/yOcczOdc2XOubKSkpK2hiGSUeq4S65rbXLfES23eP9Weu1bgWG+9YZ6bSI5QTcxSVi0NrnPBqZ509OAp3zt13tXzYwH9vnKNyI5QzV3yXUFLa1gZo8BE4BiM9sCfAe4C3jCzKYD7wJXe6s/A0wGKoAa4MYsxCySNeq4S1i0mNydc59KsujigHUdcEtbgxIRkbbRHaoiPi5WdFddRnKbkruISAgpuYsE0AlVyXVK7iIiIaTkLhJAHXfJdUruIj66iUnCQsldJICp6C45TsldxEcDh0lYKLmLBFC/XXKdkruIj2ruEhZK7iIiIaTkLuITe4aq6jKS45TcRURCSMldxOfosGHquktuU3IXEQkhJXeRAKq5S65TchfxcboWUkJCyV1EJISU3EV81G+XsFByFwmgmrvkOiV3ET913SUklNxFREJIyV3EJzrkr8Zzl1yn5C4iEkJK7iI+sYHDOjYMkTZTchcRCSEldxGf2MBh6rpLjstKcjezy83sLTOrMLPbsvEaIiKSXMaTu5nlA/cDVwBjgE+Z2ZhMv05rOeeo3H+4Sdvq9/bR0Hj0IufoOofrGthXU9dkG4DdB4+wt+YIBw7XxdoaGh1VB2qbjWHPwSPU1jdE9n3o6La7qmvZuvcQAJt21bBt36G4fSfGv/9wHTVH6pvEHFVb38D6qmrqGhpj8dbWN1B5oOnvArCzupaqA7Vs2VPTbPwAe2si+4q+rnOOfTV1bN4d2bbmSD3rq6qprW9g98EjsdePxpJMXUMju6prqdx/mAOH6zhYW5903Z3VtdQn7K+uoTH2em2hIX8l1xVkYZ9nAxXOuQ0AZvZHYCqwJtMv9MS/NvObRRvS2mZ9VTWNDo7t05UeRZFfv6KqOnYibeSAnuysrmVPTR09iwqo9iWXvt0LKelZBES+vldUVseWjRzQE4B1XtuI4h4U5DVNEInbBW3bq2sBBw7XN1nuX2dY/25s3n0otnzznhoO1zVS0quIvt0K49YFOHFAz7jXHd6/O0UFRz/bG5xjQ9XB2Pyg3l3p1TX52yO674G9i9ixv5bCfKOuIXIQ+3UvZE9N/IfSCSU9WO/t3//7JNuvX9D6/niDjs+JA3q2Kj37P2xFclk2kvsQYLNvfgtwTuJKZjYDmAEwfPjwVr1Q3+6FjByYPFEEGdSnK4vW7WTcsL6xuuqxfbvx4ttVlB3XjwG9ixhR3INn1+xg/PH9eWvHgVgSPbu0PwX5R1NGNFn26JIfi6NPt0LK393DyYN7JY2horKaUQN7crC2gW37DsW2jSam8ccfw7w1OwDoVVQQ9zsW9yzi1Q27GDukDzv219LPOwZD+3VjwVtVnDW8H3lezm50jvVVBxk7pA/D+nejorKaAb2KqDxQy6lDejeJy5/czxjet9m687rKao4v6cFJA3vxz1XbOe+EYl58uwqAD5T2Z8WWvezYXxt7vZMG9WJ91UHGDO5NaXH3pPstKsxj1db9AHQrzKe779gGxXv6sL4c27drrK0wP4812/YzKs33hd+AXl0Z0Kuo1duLdAbZSO4pcc7NBGYClJWVteqm70tPGcSlpwzKaFwiImGQjROqW4FhvvmhXpuIiLSTbCT3fwEjzWyEmXUBrgFmZ+F1REQkiYyXZZxz9Wb2n8BcIB942Dm3OtOvIyIiyWWl5u6cewZ4Jhv7FhGRlukOVRGREFJyFxEJISV3EZEQUnIXEQkhc67jHxppZlXAu63cvBjYmcFwMkVxpaezxgWdNzbFlZ4wxnWcc64kaEGnSO5tYWblzrmyjo4jkeJKT2eNCzpvbIorPe+3uFSWEREJISV3EZEQCkNyn9nRASShuNLTWeOCzhub4krP+yqunK+5i4hIU2HouYuISAIldxGREOrUyb2lB22bWZGZPe4tX2Jmpb5l3/Ta3zKzy9o5rq+Y2Roze8PM5pvZcb5lDWa23PvJ6FDIKcR1g5lV+V7/P3zLppnZOu9nWjvHdbcvprfNbK9vWTaP18NmVmlmq5IsNzO714v7DTM707csm8erpbiu8+JZaWavmNk437KNXvtyMytv57gmmNk+39/rDt+yZt8DWY7ra76YVnnvqf7esqwcLzMbZmYLvDyw2sy+FLBOdt9fzrlO+UNkuOD1wPFAF2AFMCZhnc8Dv/amr5pr4UEAAAVPSURBVAEe96bHeOsXASO8/eS3Y1wTge7e9M3RuLz56g48XjcA9wVs2x/Y4P3bz5vu115xJaz/BSLDRGf1eHn7vhA4E1iVZPlk4J+AAeOBJdk+XinGdV709Yg8iH6Jb9lGoLiDjtcE4Om2vgcyHVfCuh8Gns/28QIGA2d6072AtwP+P2b1/dWZe+6xB207544A0Qdt+00FZnnTTwIXm5l57X90ztU6594BKrz9tUtczrkFzrkab3YxkadRZVsqxyuZy4B5zrndzrk9wDzg8g6K61PAYxl67WY55xYCu5tZZSrwqItYDPQ1s8Fk93i1GJdz7hXvdaH93l+pHK9k2vLezHRc7fL+cs5tc8695k0fAN4k8nxpv6y+vzpzcg960HbiwYmt45yrB/YBx6S4bTbj8ptO5NM5qquZlZvZYjO7MkMxpRPXx72vgE+aWfRxiJ3ieHnlqxHA877mbB2vVCSLPZvHK12J7y8HPGtmyyzyEPr2dq6ZrTCzf5rZKV5bpzheZtadSJL8s68568fLIuXiM4AlCYuy+v7qsAdkvx+Y2b8DZcCHfM3HOee2mtnxwPNmttI5t76dQvo78JhzrtbMPkvkW89F7fTaqbgGeNI51+Br68jj1amZ2UQiyf18X/P53vEaAMwzs7Vez7Y9vEbk71VtZpOBvwEj2+m1U/Fh4GXnnL+Xn9XjZWY9iXyY3Oqc25+p/aaiM/fcU3nQdmwdMysA+gC7Utw2m3FhZpOA24GPOOdqo+3Oua3evxuAF4h8ordLXM65Xb5YHgTOSnXbbMblcw0JX5mzeLxSkSz2Dn8IvJmdRuRvONU5tyva7jtelcBfyVw5skXOuf3OuWpv+hmg0MyK6QTHy9Pc+yvjx8vMCokk9t875/4SsEp231+ZPpGQwRMSBUROJIzg6EmYUxLWuYX4E6pPeNOnEH9CdQOZO6GaSlxnEDmBNDKhvR9Q5E0XA+vI0ImlFOMa7Jv+KLDYHT2B844XXz9vun97xeWtN5rIyS1rj+Ple41Skp8gnEL8Ca+l2T5eKcY1nMh5pPMS2nsAvXzTrwCXt2Ncg6J/PyJJcpN37FJ6D2QrLm95HyJ1+R7tcby83/tR4J5m1snq+ytjBzcbP0TOJr9NJFHe7rV9n0hvGKAr8Cfvjb4UON637e3edm8BV7RzXM8BO4Dl3s9sr/08YKX35l4JTG/nuP4bWO29/gJgtG/bm7zjWAHc2J5xefPfBe5K2C7bx+sxYBtQR6SuOR34HPA5b7kB93txrwTK2ul4tRTXg8Ae3/ur3Gs/3jtWK7y/8+3tHNd/+t5fi/F9+AS9B9orLm+dG4hcZOHfLmvHi0ipzAFv+P5Ok9vz/aXhB0REQqgz19xFRKSVlNxFREJIyV1EJISU3EVEQkjJXUSknbU02FnA+lf7BiH7Q0rb6GoZEZH2ZWYXAtVExpY5tYV1RwJPABc55/aY2QAXuemqWeq5i4i0Mxcw2JmZnWBmc7xxbhaZ2Whv0WeA+503WFwqiR2U3EVEOouZwBecc2cBXwV+5bWPAkaZ2cveAHopjRCpgcNERDqYN8DYecCfIqOWA5HhUyCSp0cSGS9/KLDQzMY65/Ym7sdPyV1EpOPlAXudc6cHLNtC5EEedcA7ZvY2kWT/r5Z2KCIiHchFhgN+x8yugtgj+KKPT/wbkV473iibo4gMxNYsJXcRkXZmZo8BrwInmdkWM5sOXAdMN7PoQGbRp1XNBXaZ2RoiA/59zfmGeU76GroUUkQkfNRzFxEJISV3EZEQUnIXEQkhJXcRkRBSchcRCSEldxGREFJyFxEJof8DtlfC81ck7eIAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.style.use('seaborn-white')\n",
        "plt.xlabel(\"Training episodes\")\n",
        "plt.ylabel(\"Percentage of URL causing latencies\")\n",
        "plt.title(\"Bloom Filter Attack Simulation (varying attack threshold)\")\n",
        "plt.plot(range(0, 2000000, 1000), np.asarray(attacker.records)/10, label=\"threshold = 60\")\n",
        "plt.plot(range(0, 2000000, 1000), np.asarray(attacker1.records)[0:2000]/10, label=\"threshold = 80\")\n",
        "plt.plot(range(0, 2000000, 1000), np.asarray(attacker2.records)/10, label=\"threshold = 40\")\n",
        "plt.legend()\n",
        "plt.savefig(\"Bloom_Filter_Attack.png\", dpi=150)"
      ],
      "metadata": {
        "id": "0KhR9IANNUi0",
        "outputId": "b4a4827c-c623-4780-fedf-222bd12b486a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 748.8x514.8 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Adjust false positive rate"
      ],
      "metadata": {
        "id": "hpwPT3jJNN0t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bloom_filter1 = BloomFilter(10000, 0.1, 20)\n",
        "bloom_filter2 = BloomFilter(10000, 0.001, 20)\n",
        "for i in range(len(malicious_url)):\n",
        "    bloom_filter1.insert(malicious_url[i])\n",
        "    bloom_filter2.insert(malicious_url[i])\n",
        "\n",
        "attacker3 = Attacher(bloom_filter1, 0.01, 60)\n",
        "attacker4 = Attacher(bloom_filter2, 0.01, 60)"
      ],
      "metadata": {
        "id": "Xsvw3i5O37iQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "attacker3.attack(100000, plot_interval=1000)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "UQ-Zkl135ywt",
        "outputId": "115905e2-a844-407a-eb3d-cf4eea34399d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "40213\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "attacker4.attack(5000000, plot_interval=1000)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "zVMdR-z-5-F0",
        "outputId": "e9faca8c-afe4-4b76-9479-9dc4235b16b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3704818\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Using Neural Network"
      ],
      "metadata": {
        "id": "fZ1wG4v12ZIb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install torch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aBH46OM12foF",
        "outputId": "a4d21c11-bb94-4008-eb3d-d7b3e1df94b8"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.8/dist-packages (1.13.0+cu116)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch) (4.4.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as functional\n",
        "import torch.optim as optim"
      ],
      "metadata": {
        "id": "PtaHdDs12yGc"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Classifier(nn.Module):\n",
        "\n",
        "  def __init__(self, input_size=256, mlp_sizes=[256, 256]):\n",
        "    \"\"\"Initialize the Neural Network.\n",
        "    \n",
        "    Args:\n",
        "        mlp_sizes: An array of integers. The length of array equals the number\n",
        "            of hidden layers of the Multi-Layer Perceptron. Each element in the array\n",
        "            equals the number of nodes in the corresponding layer.\n",
        "    \"\"\"\n",
        "    super().__init__()    \n",
        "    # input layer\n",
        "    self.input_layer = nn.Linear(input_size, mlp_sizes[0])\n",
        "    \n",
        "    # hidden layers\n",
        "    self.hidden_layers = nn.ModuleList()\n",
        "    for k in range(len(mlp_sizes) - 1):\n",
        "        self.hidden_layers.append(nn.Linear(mlp_sizes[k], mlp_sizes[k+1]))\n",
        "\n",
        "    # output layer\n",
        "    self.output_layer = nn.Linear(mlp_sizes[-1], 1)\n",
        "\n",
        "  def forward(self, x):\n",
        "    \"\"\"Implements the forward pass of the Neural Network.\n",
        "    \n",
        "    Args:\n",
        "        x: Input to the Neural Network.\n",
        "    \n",
        "    Returns:\n",
        "        Output of the Neural Network.\n",
        "    \"\"\"\n",
        "    x = torch.relu(self.input_layer(x))\n",
        "    for hidden_layer in self.hidden_layers:\n",
        "        x = torch.relu(hidden_layer(x))\n",
        "    x = torch.sigmoid(self.output_layer(x))\n",
        "    return x"
      ],
      "metadata": {
        "id": "-dxAdcsW4BMr"
      },
      "execution_count": 272,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a training set\n",
        "urllist_50 = [x for x in urllist if len(x) <= 50]\n",
        "training_sample = random.sample(urllist_50, 100000)"
      ],
      "metadata": {
        "id": "24lT0xFI6t3I"
      },
      "execution_count": 346,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bloom_filter = BloomFilter(50000, 0.01, 20)\n",
        "for i in range(50000):\n",
        "    bloom_filter.insert(training_sample[i])"
      ],
      "metadata": {
        "id": "lh4utug2JTxN"
      },
      "execution_count": 347,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_set = [(training_sample[i], 1) if (i < len(training_sample)/2 or bloom_filter.test(training_sample[i])) else (training_sample[i], 0) for i in range(len(training_sample))]\n",
        "len(training_set)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vi9L07HGbj4c",
        "outputId": "a8fb38ce-317a-421e-dc76-6de54deead14"
      },
      "execution_count": 348,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100000"
            ]
          },
          "metadata": {},
          "execution_count": 348
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bloom_filter.R"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r1yf5yVLPLFF",
        "outputId": "cf56c017-babe-49e7-86f7-c270e69774d8"
      },
      "execution_count": 330,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1048576"
            ]
          },
          "metadata": {},
          "execution_count": 330
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Add to training set with random inputs\n",
        "for i in range(20000):\n",
        "    url = 'http://' + ''.join(random.choices(string.punctuation + string.digits + string.ascii_lowercase, k=random.randint(8, 43)))\n",
        "    if bloom_filter.test(url):\n",
        "        training_set.append((url, 1))\n",
        "    else:\n",
        "        training_set.append((url, 0))\n",
        "len(training_set)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "81ouVcUWup6w",
        "outputId": "edfc9bdc-c29d-4841-cefb-288526a52c03"
      },
      "execution_count": 352,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "120000"
            ]
          },
          "metadata": {},
          "execution_count": 352
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "character_set = string.punctuation + string.digits + string.ascii_lowercase\n",
        "character_dict = dict()\n",
        "for i in range(len(character_set)):\n",
        "    character_dict[character_set[i]] = i\n",
        "character_dict[' '] = len(character_set)\n",
        "len(character_dict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K5Alph7JSdPy",
        "outputId": "cb004cf5-c840-47e7-d92f-e44f90dfa6f4"
      },
      "execution_count": 331,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "69"
            ]
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      "cell_type": "code",
      "source": [
        "random.shuffle(training_set)"
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      "metadata": {
        "id": "EXlCzy0GNpN3"
      },
      "execution_count": 354,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_set[80000:]"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "SJOusYHHGSUh",
        "outputId": "5ed2ab49-eaa3-4f8f-a3fe-df413f9c938d"
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      "execution_count": 355,
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              " ...]"
            ]
          },
          "metadata": {},
          "execution_count": 355
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def toClassifierInput(url, input_size=256):\n",
        "    res = np.zeros(input_size)\n",
        "    for i in range(len(url)):\n",
        "        res[ord(url[i])] += 1\n",
        "    return res"
      ],
      "metadata": {
        "id": "PkRNH6GW0q7c"
      },
      "execution_count": 317,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class URLClassifier():\n",
        "    def __init__(self, learning_rate=0.001, hidden_layer_sizes=[256, 256]):\n",
        "        self.learning_rate = learning_rate\n",
        "        self.classifier = Classifier(mlp_sizes=hidden_layer_sizes)\n",
        "        self.classifier = self.classifier.float()\n",
        "        self.optimizer = optim.Adam(self.classifier.parameters(), lr=self.learning_rate)\n",
        "        self.loss_list = list()\n",
        "\n",
        "    def train(self, training_set):\n",
        "        for step in range(len(training_set)):\n",
        "            input = torch.from_numpy(toClassifierInput(training_set[step][0]))\n",
        "            output = self.classifier(input.float())\n",
        "\n",
        "            self.optimizer.zero_grad()\n",
        "            loss = (output - torch.tensor(training_set[step][1]))**2\n",
        "            loss.backward()\n",
        "            self.optimizer.step()\n",
        "\n",
        "            self.loss_list.append(loss.detach().numpy())\n",
        "\n",
        "            if step % 1000 == 0:\n",
        "                print(\"The average loss at step\", step, \"is\", np.mean(self.loss_list))\n",
        "                self.loss_list.clear()"
      ],
      "metadata": {
        "id": "rZQ0N0xF93Bm"
      },
      "execution_count": 318,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "url_classifier = URLClassifier(hidden_layer_sizes=[256, 256])\n",
        "url_classifier.train(training_set[0:10000])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h9nRghpOw0g8",
        "outputId": "c0203bcd-aa37-45b5-ee61-6300b63ba590"
      },
      "execution_count": 356,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The average loss at step 0 is 0.25171202\n",
            "The average loss at step 1000 is 0.21907654\n",
            "The average loss at step 2000 is 0.20903262\n",
            "The average loss at step 3000 is 0.21237522\n",
            "The average loss at step 4000 is 0.2090216\n",
            "The average loss at step 5000 is 0.21215014\n",
            "The average loss at step 6000 is 0.20675288\n",
            "The average loss at step 7000 is 0.20751278\n",
            "The average loss at step 8000 is 0.20775658\n",
            "The average loss at step 9000 is 0.20779894\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "url_classifier.classifier(torch.from_numpy(toClassifierInput(\"http://www.londonhotels.ndirect.co.uk\")).float())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e5_g6wFWGI_H",
        "outputId": "61be640a-278d-4b52-ca62-2ef465b9369d"
      },
      "execution_count": 364,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0.6220], grad_fn=<SigmoidBackward0>)"
            ]
          },
          "metadata": {},
          "execution_count": 364
        }
      ]
    }
  ]
}