{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "OqblfyiOsa03"
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RCtgADNqtaVd"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.linear_model import *\n",
    "from sklearn.tree import *\n",
    "from sklearn.naive_bayes import *\n",
    "from sklearn.neighbors import *\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qg6BvpkHt2if"
   },
   "outputs": [],
   "source": [
    "dataset= pd.read_csv('datasets2/network-logs.csv')\n",
    "samples = dataset.iloc[:, [1,2]].values\n",
    "targets = dataset['ANOMALY'].values\n",
    "training_samples, testing_samples, training_targets, testing_targets = train_test_split(samples, targets, test_size = 0.3, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qOBEmoQuuohe",
    "outputId": "4dc3954f-2346-4b38-f2e2-14ccd7ab6c6a"
   },
   "outputs": [],
   "source": [
    "#K-Nearest neighbors model\n",
    "knc = KNeighborsClassifier(n_neighbors= 2)\n",
    "knc.fit(training_samples, training_targets)\n",
    "knc_prediction = knc.predict(testing_samples)\n",
    "knc_accuracy = 100.0 * accuracy_score(testing_targets, knc_prediction)\n",
    "print(\"K-Nearest Neighbours accuracy: \" + str(knc_accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report , confusion_matrix\n",
    "\n",
    "print(\"Confusion Matrix:\")\n",
    "print(confusion_matrix(testing_targets, knc_prediction))\n",
    "\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(testing_targets, knc_prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "40I7cKhEvJ5i",
    "outputId": "bf6f93c6-b3ee-467f-c6f0-41fc47dc9cbd"
   },
   "outputs": [],
   "source": [
    "# Decision tree model\n",
    "dtc = DecisionTreeClassifier(random_state=0)\n",
    "dtc.fit(training_samples, training_targets)\n",
    "dtc_prediction = dtc.predict(testing_samples)\n",
    "dtc_accuracy = 100.0 * accuracy_score(testing_targets, dtc_prediction)\n",
    "print(\"Decision Tree Accuracy: \" + str(dtc_accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Confusion Matrix:\")\n",
    "print(confusion_matrix(testing_targets, dtc_prediction))\n",
    "\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(testing_targets, dtc_prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_VSTLGPCvrla",
    "outputId": "fd0bb5c4-e8e4-42f2-b5c3-5541e64a31f4"
   },
   "outputs": [],
   "source": [
    "# Gaussian Naive Bayes Model\n",
    "gnb = GaussianNB()\n",
    "gnb.fit(training_samples, training_targets)\n",
    "gnb_prediction = gnb.predict(testing_samples)\n",
    "gnb_accuracy = 100 * accuracy_score(testing_targets, gnb_prediction)\n",
    "print(\"Gaussian Naive Bayes accuracy: \" + str(gnb_accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Confusion Matrix:\")\n",
    "print(confusion_matrix(testing_targets, gnb_prediction))\n",
    "\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(testing_targets, gnb_prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "name": "botnet detection with ML Udemy.ipynb",
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   "file_extension": ".py",
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