{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "epi_kAOf4BKs"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import *\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "CmlwL67D4OFb"
   },
   "outputs": [],
   "source": [
    "malware_dataset = pd.read_csv(\"MalwareArtifacts.csv\", delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "E9J1BzoQ4dIq"
   },
   "outputs": [],
   "source": [
    "#Extract artifacts samples fileds \"AddressOfEntryPoint\" and \"DLLCharacteristics\"\n",
    "samples = malware_dataset.iloc[:, [0, 4]].values\n",
    "targets = malware_dataset.iloc[:, 8].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gdXEI9GJ5HXD"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "training_samples, testing_samples, training_targets, testing_targets = train_test_split(samples, targets, test_size = 0.2, random_state = 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4h33N4dB5bg-",
    "outputId": "0f8f3fe6-20a3-416e-acc9-581c5722b70f"
   },
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "tree_classifer = tree.DecisionTreeClassifier()\n",
    "tree_classifer.fit(training_samples, training_targets)\n",
    "predictions = tree_classifer.predict(testing_samples)\n",
    "accuracy = 100* accuracy_score(testing_targets, predictions)\n",
    "print(\"Decision Tree accuracy: \" + str(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, predictions))\n",
    "\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(testing_targets, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "colab": {
   "name": "Udemy Malware detection with decision trees.ipynb",
   "provenance": []
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   "language": "python",
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    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "widgets": {
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