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": "BUTFBYKgAsSV"
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn.cluster import KMeans\n",
+    "from sklearn.metrics import silhouette_score\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ykmjXu5-A6FH"
+   },
+   "outputs": [],
+   "source": [
+    "malware_dataset = pd.read_csv(\"MalwareArtifacts.csv\", delimiter=',')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "-kbytUCHA8j6"
+   },
+   "outputs": [],
+   "source": [
+    "# Extracting artifacts smaples fields # MajourLinkerVersion, MajorImageVersion, MajorOperatingSystemVersion,DllCharacteristics\n",
+    "samples = malware_dataset.iloc[:, [1,2,3,4]].values\n",
+    "targets = malware_dataset.iloc[:, 8].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "jXBwP8eLBijT",
+    "outputId": "f03022bb-020b-4227-a407-c9199da4f747"
+   },
+   "outputs": [],
+   "source": [
+    "k_means = KMeans(n_clusters=2, max_iter=100)\n",
+    "k_means.fit(samples)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "nWi4BxSTDGDQ",
+    "outputId": "2485d298-eeb0-4906-c0ec-c6f91baec6f1"
+   },
+   "outputs": [],
+   "source": [
+    "print(\"K-Means labels: \" + str(k_means.labels_))\n",
+    "print(\"\\n K-means clustering reulsts \\n\\n\", pd.crosstab(targets, k_means.labels_, rownames = [\"Observerd\"], colnames=[\"Predicted\"]))\n",
+    "print (\"\\nSilhouette coefficient: %0.3f\" % silhouette_score(samples, k_means.labels_, metric='euclidean')) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "name": "Udemy Malware detection with K-Means.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
+}