labs/Lab-2/Lab-2-Practical/04_NumPy_Security_Metrics_Lab.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2444933d",
   "metadata": {},
   "source": [
    "# Module 2 — Lab 4: NumPy for Security Metrics\n",
    "\n",
    "**Topics:** array, vectorization, statistics, boolean mask\n",
    "\n",
    "\n",
    "> **Instructions:** Run the cells in order. Complete every `TODO`, then run the **Self-check** cell. Do not open the Solution notebook before attempting the tasks yourself.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f43f1b4",
   "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": "0b917960",
   "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": "ca8c8c19",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "source": [
    "## 1. Unit conversion and descriptive statistics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01b704ef",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "outputs": [],
   "source": [
    "# TODO 1: convert packet_sizes from bytes to KB with a vectorized operation\n",
    "packet_kb = None\n",
    "\n",
    "# TODO 2: calculate the mean and 95th percentile of latency_ms\n",
    "mean_latency = None\n",
    "p95_latency = None\n",
    "\n",
    "print(\"Packet sizes (KB):\", packet_kb)\n",
    "print(\"Mean latency:\", mean_latency)\n",
    "print(\"P95 latency:\", p95_latency)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34dfe8cd",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "source": [
    "## 2. Filter with a Boolean mask\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "070dc425",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "outputs": [],
   "source": [
    "# TODO 3: True when size > 1400 OR latency > 150 OR failures >= 8\n",
    "suspicious_mask = None\n",
    "\n",
    "# TODO 4: obtain the indices matching the mask with np.where\n",
    "suspicious_indices = None\n",
    "\n",
    "print(\"Mask:\", suspicious_mask)\n",
    "print(\"Suspicious indices:\", suspicious_indices)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5e6d529",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "source": [
    "## 3. Normalize values and create a risk score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f126015",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "outputs": [],
   "source": [
    "def min_max(values):\n",
    "    values = np.asarray(values, dtype=float)\n",
    "    # TODO 5: return a zero array when max == min; otherwise apply min-max normalization\n",
    "    pass\n",
    "\n",
    "\n",
    "latency_score = min_max(latency_ms)\n",
    "failure_score = min_max(failed_attempts)\n",
    "\n",
    "# TODO 6: calculate 40% latency_score + 60% failure_score\n",
    "risk_score = None\n",
    "\n",
    "print(\"Risk scores:\", risk_score)\n",
    "print(\"Highest-risk index:\", None if risk_score is None else int(np.argmax(risk_score)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4dcff69",
   "metadata": {},
   "source": [
    "## Self-check\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a329c7f0",
   "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": "915b1cef",
   "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
}