CSAI2026 labs: Lab-2..Lab-6 (Practical + Solution, data), Module-2..Module-6 notebooks and datasets

2026-10-12 19:16:14 +07:00
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+{
+ "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": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "name": "Udemy Malware detection with decision trees.ipynb",
+   "provenance": []
+  },
+  "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": "3.8.8"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}