phishing_detector.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNFyExuYjVCtLBkgrLl63bU",
      "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/rodgersxy/AirBnB_clone/blob/master/phishing_detector.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "import re\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Download necessary NLTK data\n",
        "nltk.download('punkt', quiet=True)\n",
        "nltk.download('stopwords', quiet=True)\n",
        "\n",
        "# Function to preprocess email content\n",
        "def preprocess_email(email):\n",
        "    email = email.lower()\n",
        "    email = re.sub('<[^<]+?>', '', email)\n",
        "    email = re.sub('[^a-zA-Z]', ' ', email)\n",
        "    words = word_tokenize(email)\n",
        "    stop_words = set(stopwords.words('english'))\n",
        "    words = [word for word in words if word not in stop_words]\n",
        "    return ' '.join(words)\n",
        "\n",
        "# dataset\n",
        "data = {\n",
        "    'email_content': [\n",
        "        'Urgent: Update your bank account details now!',\n",
        "        'Your monthly statement is ready for viewing',\n",
        "        'Win a free iPhone! Click here!',\n",
        "        'Payment received: KES 5000 credited to your account',\n",
        "        'Security alert: Unusual activity detected on your account',\n",
        "        \"Congratulations! You've won a cash prize. Click to claim\",\n",
        "        'Your loan application has been approved',\n",
        "        'Important: Verify your account to avoid suspension',\n",
        "        'New features available in your mobile banking app',\n",
        "        'Limited time offer: Increase your credit limit today!',\n",
        "        'Reminder: Your credit card payment is due soon',\n",
        "        'Exclusive investment opportunity. Act now!',\n",
        "        'Your account has been temporarily locked. Verify now',\n",
        "        'Welcome to our new secure banking portal',\n",
        "        'Fraud alert: Unauthorized transaction attempt'\n",
        "    ],\n",
        "    'label': [1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0]  # 1 for phishing, 0 for legitimate\n",
        "}\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# Preprocess the emails\n",
        "df['processed_email'] = df['email_content'].apply(preprocess_email)\n",
        "\n",
        "# Split the data\n",
        "X = df['processed_email']\n",
        "y = df['label']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)\n",
        "\n",
        "# Vectorize the text data\n",
        "vectorizer = CountVectorizer()\n",
        "X_train_vectorized = vectorizer.fit_transform(X_train)\n",
        "X_test_vectorized = vectorizer.transform(X_test)\n",
        "\n",
        "# Train a Random Forest classifier\n",
        "clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "clf.fit(X_train_vectorized, y_train)\n",
        "\n",
        "# Make predictions\n",
        "y_pred = clf.predict(X_test_vectorized)\n",
        "\n",
        "# Print the classification report\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "# Function to classify new emails\n",
        "def classify_email(email_content):\n",
        "    processed = preprocess_email(email_content)\n",
        "    vectorized = vectorizer.transform([processed])\n",
        "    prediction = clf.predict(vectorized)\n",
        "    return \"Phishing\" if prediction[0] == 1 else \"Legitimate\"\n",
        "\n",
        "# Demo\n",
        "print(\"\\nDemo Classifications:\")\n",
        "test_emails = [\n",
        "    \"Urgent: Your account will be closed. Click here to prevent this!\",\n",
        "    \"Your monthly bank statement is ready for download.\",\n",
        "    \"You've won a prize! Click here to claim it now!\",\n",
        "    \"Important notice: Update your account information immediately.\",\n",
        "    \"Thank you for your recent transaction. Here's your receipt.\"\n",
        "]\n",
        "\n",
        "for email in test_emails:\n",
        "    print(f\"Email: {email}\")\n",
        "    print(f\"Classification: {classify_email(email)}\\n\")\n",
        "\n",
        "# Visualizations\n",
        "plt.figure(figsize=(12, 8))\n",
        "\n",
        "# 1. Class distribution\n",
        "plt.subplot(2, 2, 1)\n",
        "sns.countplot(x='label', data=df)\n",
        "plt.title('Class Distribution')\n",
        "plt.xlabel('Email Type')\n",
        "plt.xticks([0, 1], ['Legitimate', 'Phishing'])\n",
        "\n",
        "# 2. Email length distribution\n",
        "df['email_length'] = df['email_content'].apply(len)\n",
        "plt.subplot(2, 2, 2)\n",
        "sns.boxplot(x='label', y='email_length', data=df)\n",
        "plt.title('Email Length Distribution')\n",
        "plt.xlabel('Email Type')\n",
        "plt.ylabel('Email Length')\n",
        "plt.xticks([0, 1], ['Legitimate', 'Phishing'])\n",
        "\n",
        "# 3. Confusion Matrix\n",
        "plt.subplot(2, 2, 3)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "\n",
        "# 4. Feature Importance\n",
        "feature_importance = pd.DataFrame({\n",
        "    'feature': vectorizer.get_feature_names_out(),\n",
        "    'importance': clf.feature_importances_\n",
        "}).sort_values('importance', ascending=False)\n",
        "\n",
        "plt.subplot(2, 2, 4)\n",
        "sns.barplot(x='importance', y='feature', data=feature_importance.head(10))\n",
        "plt.title('Top 10 Important Features')\n",
        "plt.xlabel('Importance')\n",
        "plt.ylabel('Feature')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "eLjq8orNxRJm",
        "outputId": "acaafd01-27a2-474b-832a-195d161c80b7"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00         3\n",
            "           1       0.40      1.00      0.57         2\n",
            "\n",
            "    accuracy                           0.40         5\n",
            "   macro avg       0.20      0.50      0.29         5\n",
            "weighted avg       0.16      0.40      0.23         5\n",
            "\n",
            "\n",
            "Demo Classifications:\n",
            "Email: Urgent: Your account will be closed. Click here to prevent this!\n",
            "Classification: Phishing\n",
            "\n",
            "Email: Your monthly bank statement is ready for download.\n",
            "Classification: Legitimate\n",
            "\n",
            "Email: You've won a prize! Click here to claim it now!\n",
            "Classification: Phishing\n",
            "\n",
            "Email: Important notice: Update your account information immediately.\n",
            "Classification: Phishing\n",
            "\n",
            "Email: Thank you for your recent transaction. Here's your receipt.\n",
            "Classification: Legitimate\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 5 Axes>"
            ],
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          "metadata": {}
        }
      ]
    }
  ]
}