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

Lab-2/Lab-2-Solution/04_NumPy_Security_Metrics_Solution.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "03d77301",
+   "metadata": {},
+   "source": [
+    "# Module 2 — Lab 4: NumPy for Security Metrics\n",
+    "\n",
+    "\n",
+    "**Topics:** array, vectorization, statistics, boolean mask\n",
+    "\n",
+    "\n",
+    "> **Solution version:** This notebook contains the complete solution. Run the cells in order and compare the results with your Lab notebook.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33cb0ba1",
+   "metadata": {},
+   "source": [
+    "## Scenario\n",
+    "\n",
+    "A network sensor reports packet sizes, latency, and failed authentication attempts for each time window. We use NumPy to calculate efficiently across the entire array.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d7d3de72",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "packet_sizes = np.array([64, 128, 1500, 512, 1514, 256, 1400, 80])\n",
+    "latency_ms = np.array([12.5, 18.0, 250.0, 33.5, 180.0, 25.0, 95.0, 10.0])\n",
+    "failed_attempts = np.array([0, 1, 8, 2, 10, 1, 6, 0])\n",
+    "\n",
+    "print(packet_sizes.shape, packet_sizes.dtype)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d225bd1",
+   "metadata": {
+    "tags": [
+     "exercise"
+    ]
+   },
+   "source": [
+    "## 1. Unit conversion and descriptive statistics\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3da57f45",
+   "metadata": {
+    "tags": [
+     "solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "packet_kb = packet_sizes / 1024\n",
+    "mean_latency = latency_ms.mean()\n",
+    "p95_latency = np.percentile(latency_ms, 95)\n",
+    "\n",
+    "print(\"Packet sizes (KB):\", np.round(packet_kb, 3))\n",
+    "print(f\"Mean latency: {mean_latency:.2f} ms\")\n",
+    "print(f\"P95 latency: {p95_latency:.2f} ms\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dddbd812",
+   "metadata": {
+    "tags": [
+     "exercise"
+    ]
+   },
+   "source": [
+    "## 2. Filter with a Boolean mask\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d24785c0",
+   "metadata": {
+    "tags": [
+     "solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "suspicious_mask = (packet_sizes > 1400) | (latency_ms > 150) | (failed_attempts >= 8)\n",
+    "suspicious_indices = np.where(suspicious_mask)[0]\n",
+    "\n",
+    "print(\"Mask:\", suspicious_mask)\n",
+    "print(\"Suspicious indices:\", suspicious_indices)\n",
+    "print(\"Suspicious packet sizes:\", packet_sizes[suspicious_mask])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce903e91",
+   "metadata": {
+    "tags": [
+     "exercise"
+    ]
+   },
+   "source": [
+    "## 3. Normalize values and create a risk score\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dcece1eb",
+   "metadata": {
+    "tags": [
+     "solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "def min_max(values):\n",
+    "    values = np.asarray(values, dtype=float)\n",
+    "    value_range = values.max() - values.min()\n",
+    "    if value_range == 0:\n",
+    "        return np.zeros_like(values)\n",
+    "    return (values - values.min()) / value_range\n",
+    "\n",
+    "\n",
+    "latency_score = min_max(latency_ms)\n",
+    "failure_score = min_max(failed_attempts)\n",
+    "risk_score = 0.4 * latency_score + 0.6 * failure_score\n",
+    "\n",
+    "print(\"Risk scores:\", np.round(risk_score, 3))\n",
+    "print(\"Highest-risk index:\", int(np.argmax(risk_score)))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65a088f0",
+   "metadata": {},
+   "source": [
+    "## Self-check\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a730cc89",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert packet_sizes.shape == (8,)\n",
+    "assert np.isclose(mean_latency, 78.0)\n",
+    "assert suspicious_indices.tolist() == [2, 4]\n",
+    "assert int(np.argmax(risk_score)) == 4\n",
+    "assert np.all((risk_score >= 0) & (risk_score <= 1))\n",
+    "print(\"✓ Lab 4 completed\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c85c6e5",
+   "metadata": {},
+   "source": [
+    "## Extension challenge\n",
+    "\n",
+    "Change the weights assigned to latency and failed attempts. Observe whether the highest-risk element changes and explain why.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}