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

Module-6/1.Botnet_Detection.ipynb

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+{
+ "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": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "collapsed_sections": [],
+   "name": "botnet detection with ML Udemy.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
+}