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

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    "# 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"
    ]
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   "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"
   ]
  }
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