{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "721d8cda",
   "metadata": {},
   "source": [
    "**Author:** Dr. Mallarapu  \n",
    "**Created:** 2026-07-27  \n",
    "**Course:** SEAS 8414 \u2014 Security Analytics\n",
    "\n",
    "---\n",
    "\n",
    "### Goal of this notebook\n",
    "\n",
    "Train and audit detectors on CIC-ToN-IoT network flows re-featured by CICFlowMeter.\n",
    "\n",
    "### What you will learn\n",
    "\n",
    "1. Read a majority-class baseline before trusting any accuracy figure.\n",
    "2. Find the strongest single feature, then test it by dropping it and refitting.\n",
    "3. Tell duplicate inflation apart from genuine signal.\n",
    "4. Report per-group recall, because the rare classes carry the risk.\n",
    "\n",
    "### Where this connects to the course text\n",
    "\n",
    "The text builds a defence pipeline; this notebook trains a classifier and audits it. The links below are to specific chapter objectives that share an *analytic move*, not to matching subject matter.\n",
    "\n",
    "- **Chapter 2: Service Enrichment and Device Fingerprinting** \u2014 Learning objective 3 (section 2.1) compares evidence sources by strength and **spoofability**. The features here are CICFlowMeter flow statistics - durations, inter-arrival times, packet-size aggregates - not protocol fields. An attacker moves them indirectly, by pacing and padding traffic. That is a weaker form of the same spoofability concern.\n",
    "- **Chapter 9: Supply-Chain Integrity and Counterfeit Detection** \u2014 Learning objectives 2 and 3 (section 9.1) separate documentation gaps from tamper evidence, and warn that screening signals are **not independent**. Both apply to artifact-derived features.\n",
    "- **Chapter 11: Formal Protocol Verification** \u2014 Section **11.1.2**, titled *Proved, tested, and hoped*, asks you to separate exactly those three. (Chapter 11 lists its objectives in \u00a711.0, not \u00a711.1 as the other chapters do.) The ablation does that job here: it tests whether the headline survives.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65a8460b",
   "metadata": {},
   "source": [
    "# IoT/IIoT Network-Flow Intrusion Detection: CIC-ToN-IoT\n",
    "### Model comparison + validity audit on CIC-ToN-IoT **network flows** (\u22651M records, via Kaggle)\n",
    "\n",
    "**Abstract:** CIC-ToN-IoT has **4,847,499** labeled flow records. It is a CICFlowMeter reprocessing of the UNSW ToN_IoT **network-traffic** captures \u2014 the pcap side of ToN_IoT, not its sensor-telemetry tables (Moustafa, 2021). **Every feature used below is a CICFlowMeter flow statistic.** None of ToN_IoT's seven sensor-telemetry tables (fridge, GPS tracker, motion light, garage door, Modbus, thermostat, weather) and none of its OS/audit logs are read here. So nothing in this notebook is evidence about *telemetry-based* detection. We compare four learners and audit the near-balanced, near-perfect scores."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1c3ec4f",
   "metadata": {},
   "source": [
    "## 1. Research problem\n",
    "\n",
    "**Task:** Classify IoT/IIoT **network flows** as benign or attack. The families that reach the per-family recall breakdown in \u00a79 include `mitm`, `ddos`, `dos`, `ransomware`, `backdoor` and `scanning`.\n",
    "\n",
    "**Scope, stated once so it is not assumed away:** ToN_IoT is a multi-view corpus \u2014 sensor telemetry, OS/audit logs, and raw network captures from an IIoT testbed. This notebook reads **only** the CICFlowMeter re-featurisation of the network captures. Telemetry is named here to mark what we are *not* using: a flow-only detector says nothing about whether device telemetry would have caught the same attacks."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d254ce9",
   "metadata": {},
   "source": [
    "## 2. Literature review\n",
    "\n",
    "- **Moustafa (2021)** \u2014 ToN_IoT: a new generation of IoT/IIoT datasets for the whole telemetry, OS-log and network stack.\n",
    "- **Booij, Chiscop, Meeuwissen, Moustafa & den Hartog (2022)** \u2014 *ToN_IoT: The Role of Heterogeneity and the Need for Standardization of Features and Attack Types in IoT Network Intrusion Data Sets* (IEEE Internet of Things Journal).\n",
    "- **Sommer & Paxson (2010)** \u2014 the closed-world critique.\n",
    "\n",
    "**Related approaches and their known caveats** \u2014 drawn from the wider literature; these are **not** measurements reproduced on this exact corpus:\n",
    "\n",
    "| Reported approach | Known caveat |\n",
    "|---|---|\n",
    "| Moustafa (2021) \u2014 ML baselines | in-distribution testbed |\n",
    "| Various IoT-NIDS papers | flow features from one testbed |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bbb911d",
   "metadata": {},
   "source": [
    "## 3. Dataset provenance & honesty caveats\n",
    "\n",
    "| Property | Value |\n",
    "|---|---|\n",
    "| Source | Kaggle `dhoogla/cictoniot` (CIC-ToN-IoT-V2) |\n",
    "| View | network flows only \u2014 no telemetry table, no OS/audit log |\n",
    "| Rows | 4,847,499 flow records |\n",
    "| Features | 68 numeric flow statistics after constant-column removal (printed below) |\n",
    "| Label | `Label` 0/1; family `Attack` |\n",
    "| Access | Kaggle API token required |\n",
    "\n",
    "**Honestly:** `Attack` (the family name) is dropped from features to prevent leakage; one testbed, so cross-distribution generalization is untested.\n",
    "\n",
    "**Preprocessing caveat that \u00a710 and \u00a711 depend on \u2014 read it before quoting any duplicate rate.** The loader ends with `X.clip(-1e15, 1e15)`, a blunt guard against float overflow, and then deletes any column left with a single distinct value. That clip is not free. Every value beyond the bound is mapped onto the *same* number. So (i) two flows that differed only above the bound become byte-identical rows. And (ii) a column lying entirely above the bound becomes constant and is dropped. In this corpus the clip does bite. The CICFlowMeter `Idle Mean` / `Idle Std` / `Idle Max` / `Idle Min` fields are recorded on a scale that runs past the 1e15 bound. That is a property of the source parquet, not something a cell below prints. `Idle Max` is the one column the clip alone removes. Consequence: the duplicate and overlap rates printed in \u00a710 are measured on this post-clip matrix, and part of what they count is duplication *we* manufactured.\n",
    "\n",
    "### Before you run this: getting the data\n",
    "\n",
    "This notebook downloads its own data on the first run, then caches it. You do not fetch anything by hand.\n",
    "\n",
    "**Dataset:** Kaggle `dhoogla/cictoniot` -> `/tmp/kg_cictoniot`. It is about **420 MB** on disk.\n",
    "\n",
    "**One-time setup.** Sign in at kaggle.com, open **Settings**, and under **API** choose **Create New Token**. Kaggle hands you a `kaggle.json` file. This notebook does *not* read that file. It reads a plain key file, so convert it once:\n",
    "\n",
    "```bash\n",
    "mkdir -p ~/.kaggle\n",
    "python3 -c \"import json;print(json.load(open('kaggle.json'))['key'],end='')\" > ~/.kaggle/access_token\n",
    "chmod 600 ~/.kaggle/access_token\n",
    "```\n",
    "\n",
    "Never paste the token into a cell, a commit, or a screenshot. If it leaks, revoke it from the same Settings page.\n",
    "\n",
    "**If the loader fails:**\n",
    "\n",
    "- `FileNotFoundError: ~/.kaggle/access_token` - you created `kaggle.json` but not the key file. Run the command above.\n",
    "- `401 Unauthorized` - the key is wrong, or a trailing newline crept in.\n",
    "- `403 Forbidden` - open the dataset page on Kaggle while signed in, accept its terms, then re-run the cell.\n",
    "\n",
    "The cache sits under `/tmp`, which macOS clears on reboot. To keep it, move the folder somewhere durable and symlink it back. Do **not** edit the path in the code cell below: that changes a code cell and invalidates the stored outputs you are reviewing."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "671f92f2",
   "metadata": {},
   "source": [
    "## 4. Solution design\n",
    "\n",
    "The methodology is deliberately two-track. We *earn* a headline score with standard modelling, then *interrogate* it with a validity audit. Only a verdict that survives both is reported. The diagram below is the shape of every notebook in this series."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "108f35fd",
   "metadata": {},
   "source": [
    "**Figure 4.1 \u2014 Solution design (methodology).**\n",
    "\n",
    "<img 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\" style=\"max-width:100%;height:auto;\" alt=\"Figure 4.1 \u2014 Solution design (methodology).\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36bf2ce9",
   "metadata": {},
   "source": [
    "## 5. Implementation architecture\n",
    "\n",
    "Five stages \u2014 ingestion, preprocessing, modelling, evaluation, and a parallel validity-audit path \u2014 feed a single graded results ledger. Leakage defences (dropping label-derived and identifier columns) live in preprocessing, before any model sees the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cc41fc8",
   "metadata": {},
   "source": [
    "**Figure 5.1 \u2014 Implementation architecture.**\n",
    "\n",
    "<img 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GTOW6TGiTWUfRZw4caRJk3fNcvr0SdMbpWfP9jJlynx3dY72N9E+NuGFnxo6MnRf2ig4PO33oiFDggSJ5FFoFYw20U2bNoN5fOzYEdN7Rc+zXLkXH2VXpl+OhjiuHjNabXI/kZ19admyP8y11H4y2nD4SdLrePPmTfnww253PXdn4AYAAJ4uQhkAABzi1q1/wwJ//4OmP0vOnHnN44sXA0xI4gpktFJDe6nosJZHoVUW2pxWZ/7Rhr4ZM2YxlSzBwUER+rA8jA6hCQsLjbAuV678poGwNqp1zUC0bZufCVgKFy4hj2L58sVy8uRxUxWk9P3fPv807m3CD+2633npsfW1KVOWda/Tapv/Sq+dSpUq7RPdr9LruGXLRsmWLbcJ1AAAgH0IZQAAcAidYUdnLNLGtD/88I2ZRrpq1dt9TLJmzSmbNq2TP/74zQwR0imftRfM4cO3p4e+35TTGpDosCdt9KuzGcWI4WV6k+i02K7eLzrltPZqSZYspaRNm1727dstixf/JgMHfmP62tyLr292E7hoNYseX6tXatduZJoGd+/eTurVa2qGRulU1Dqrk/a1eRANhbQ3i75Gb3/7baapQKlTp7F5XvuraL+WhQvnmD46+r618a3SfjWucOpe56XHX7NmhbkGly9fNEO4YsWKLTt3bjWVPK4pxR+F7lPNnDnJzPy0ceM/5jz0M9GhZtmz55LHpTNw6XXs27eT+Wy0ambRol/NbE9vvtlKAABA1CGUAQDAIXQWpRkzJpoqmQwZfE0oEidOXPOcTrkcGHhdxo0bYYIVDRu0iuSrr76QI0cOmcDlXrRipXPnz03IMWJEPzMESvuqNGnSwr2Nzu6kFSUaMGjPEh0yVK1a7bv6w4Sn01IPH/65dO3a1gpukljHL2TCkuHDJ8iXX/Y1AZMOWypYsJiZhtrVrPd+NDzSWZr0mKlSpTFTamsg43qdBlV9+nwp48ePlM8+62BCmr59R1rhyzrZtWurtUWT+56XHv+bb4aanjR6PXRqbB0GNGnS12Z2p5gxY8qjKl78BWnXrrNpHrxw4a+SL18h67OZJRMnfm169/yXUEY/c72O2uOnd++OJozSYVU6NTYAAIhaHgIAiBKzZ5c9Xrly/zTx4z+8nwSenu8/C5Wqb2eS2PFjiFNs27ZJOnZsIUOGjDVf7gHc9uuIg9LwYw+Jm1AAAM+5gID9smJF37116qzIIdHIo9fTAgAAAAAA4D8jlAEAAAAAALABPWUAAHjOZc6cVQYN+sbcAgAAIPoglAEA4DmnU1I/ynTUAAAAiBoMXwIAAAAAALABoQwAAAAAAIANCGUAAAAAAABsQCgDAAAAAABgA0IZAAAAAAAAGxDKAABgo+vXr0vbtk3lwoUAAZ6UoKAgmTdvlhw4sFcAAED0RSgDAICNxo//SpIlSyGJEycxj1evXiZDhvSSqPL77z/LkSOH5L9avXqZbNmyQRA97Nq1VUaOHCDffjtM7PSkfr4AAHheEcoAAGCTrVv9ZOHCOfLeex+512mwsWHDaokqEyd+bc7hv1qyZL58992XgofTKpanHVTkypVfmjd/X958s5U8qjNnTsnFixfkSXhSP18AADyvCGUAALDJpElfS5kylSVNmnQC5xg2rI/07t1RniYfHx9p0KCZ5M1b8JFed+zYEXnjjZqyadNaAQAATx+hDAAANtAeMtu3b5YiRUoKAAAAnMlLAABAlNuxY7O5zZEjz0O3vXr1ihkGsnnzejl79rRkypRFPvigm2TJkt29zdSp483zBw7sMVUSRYqUkubNP3D3qlGXLl2UMWOGyMaN/0i8ePGlTp3X73m8P/74zQw52b9/t6RPn0lat/4kQsVFaGioOZ9Vq5bKlSuXpEKFahIcHPSwtyEvvVRcOnfuK4cO7ZP583+RJk1ayKuvNrIe75cpU8ZaIdUms+98+QrLhx92lwQJEprXtWzZQHx9s0uXLn3N4wULW60y+QAAEABJREFU5siXX/aVt95qI40bv2PWffllP9m3b5eMHv3jPY+9f/8ec4xt2/wkVqzYUrBgMWu/HcwxwsLCZMaMSeb9+PsfkBQpUsvrrzeXihWru1+v12358j9k+vQ/3Ot69frYfB6uY27btkk6dmwhQ4aMlQkTRpnPIkMGX2nRor3kz1/YDAvSKhSXatWKSq5c+axzn3DPc+7cubWkTp1OQkKCZdOmdXLt2lUpWrSUfPJJH4kZM2aEY44bN8t6f+Nk5colMnLkZMmcOau53k2btrSu87tmW33+77//tN7bu9bPyzg5efKYdV5F5f33u1jvOZWp4Fm0aK7ZdsCAT83St+9XUqxYabl165b5zNetWymnTp2QPHkKWMftLUmSJHWfb2R/vgAAwL+olAEAwAYaRCj90v0w/fp1kb/+WiDVq78q7dt/Kp6envLRR83l3Lkz7m00qNFwREMP/SLu57fWBAPhDRz4qaxd+7c0bPi2NGvWxty/fPlShG3Wr18tQ4f2Fg8PD+tYPSRlyjTSrVtbEyi4/PTTRLPol3UNT9TatSslMqZP/94EUu+/39UERxo0dO3aRo4d8zch0htvvCc7d24xx9SwROXMmVcOHvx3FqG9e3dK7NhxTOjhos/rdveioZbuT0OI1q07ymuvvWm2v379mnleA5kJE0ZbwVMhE3hoUKaBxD//LJfH0bv3x/LiizXlu+9mmtBHHwcHB0uiRElk0KBvpFCh4pI8eUpz33X97mfx4t8kMPC6DB78nfToMciEM999N/yu7fRnJFasWNKhQw8TpN3P0aOH5YcfvjFB0bBh35sQatSogeY5vS6ffNLb3NdQSs8vT57bYdykSWPMdcqWLZc55wsXzlshWWv3Z6Qi8/MFAAAiolIGAAAbaIWJVmxoVcuD7Nq1zQQsXbv2t0KXqmZdyZLlpFGjKvLzzz+6mwSXLl0hwuuOHz8qS5cudD8+eHCfbNy4xlRF1KxZ372fBg0qR3jdzJk/mOqHoUPHmccVK1aTVq0aydy5M+Tddz8wFROzZ0+V8uWrWOs/Ntu88EJFOXz4gAlYHiYg4JwMHz7BClVim8ca7ugX99Gjp0jSpMnNugwZMkunTq1MKKLvK2fOfKZ6JyQkRLy8vExFTKVKL1nXZY3ZXqtrNKB5+eV69zymzgCkVRx6DA1DVO3aDc2tNt2dPn2CeW3Llu3NujJlKsnp0ydNZU2pUuXlUWnwU7lyDXNfg7QNG/4x1SXp02eUAgWKmkofrbDR+w+TJk16K1D6QmLEiGF6D5UrV8UEdHrtvb293dvp+9LA7mH0Gg4Y8LWZ8Uvpz8Dffy8x9zXM8fC4/fc6/Qxc53fjxg359dfp5jPv2LGXWVegQBFT9bNu3SopUaJMpH++AABARFTKAABgA1fA8DD6hV5pdYWLBhrZs+cxPWlczp8/a6o7GjeubobFaGBz/fq/IcnWrRvNbfggQCsrYsaMFeGcdDutYHHRihmtHNm9e5t57O9/0AQc+fMXiXCe8eMnkMjQUMEVyCj9Iq9hgCuQUTp8Sbnenw7x0YoMHU6l56jDn156qY6cPHncVMFoIKTrdbt7cR3DFciEp6GXVszcGZDocKN9+3ZLYGCgPCod/uTi43N7mNGNG4++H5UkSTITyLj4+mYz56tVP+FVqfJKpPanVVauQMZ1fjdv3njga/Sz12Am/M+Ffl4pU6aWPXu2m8eR+fkCAAB3o1IGAAAbaJVMZL6oX7162dxqj47w9PGxY4fNff2S3qHDO2YolPZd0SEnkyd/K3PmTAu3nyvmNk6cePc9lg6T0fBj8eJ5ZgkvVao0EfYTN+799/MgiRMnjfBYK4bu3JcGB/r+zp07bR5nzOhreqi4hivp9tmy5TSBhQ5l0mFcceLENdUd96LHuF9opM+59hlevHi3t9dzeNBwoKjmOk8NxsK787o+Sa7PXHvO6BKea1hbZH6+AADA3QhlAACwgYYcWt1x/vw5SZo02X23c1U1XLlyWRIlSuxer1+CEyRIZO4vW/aHGW6j/WS0Aeu9uBr+avXM/Y6nwYVWNxQvXsY9BMXFVfHx736uyZOg1Ss6tCc8HY6kQ6Fc709Dmty5C5ghMnrNdDiT0v4mGtRoKPOghsl6De88RvjjK1eo4OIKw1znEF24erQ8zRDmTq5rpI2Vc+fOH+E5DcZun8/Df74AAMDdCGUAALCBK1g4fHj/A7/Euhqt6vCQcuVeNPd1SM3evTvMEB6lTVdVqlRp3a/ToT7h6XAndeDAXnflh/aH0ZDjzuPpUKj79TvRahytYtGAJDztzfI4cuXKb4Zo6RThri/2OkOSVuwULlzCvZ028dXZpbS6SMMYlTVrDtNfRvvUuK7TvWigo0OY9H2FHyalMmbMYqpPdOYn7ZnisnWrn9l/woS3QxkNpe68Vnrcx6HD1kJDb0Vq21u3Ih5TmyTrObkql54015A6/dlw0Wukn7nOsHW/n4vI/nwBAICI6CkDAIANsmfPZaYh1umDw9MZerQqRhuoajChlQk6y9HIkV/Izz9PMVUxOluRiIfUr/+GeY0rpJg5c5J5nU5LrP1YtA/I3r27/r9NTjOttW6jM/DcvHlThg//3ApTbkY4vk5TvWPHFvnuuy9ly5YNpqls27ZNzH2lX9pr1WpgncciM1OTVrVoE+AtW9bL46hdu5GZoah793ayZMl8+fXXn2TAgO7mPYVvsquhjPaS2bNnh3Xtcpt1WbPmNFNdazDzoEoZbeqrx/j00w/MMebMmW6aF2v1jFYGNWz4lsybN0vGjfvKTIs9ePBnJgTTWaxctJeLfi567bRaRWcj0kDtcWTOnM30w9Fj6ed5/fr1+26rU16PHz/SXP/582fLihV/mqmmI9OP6HFoVZFeqzVrVphjapNpvUYNGjSTWbMmm3PQ9bNmaZPphnLx4gXzusj+fAEAgIgIZQAAsEmjRu+YWYXCfyl/8cWXzTCiHj0+NI1m1aefDpISJcqaL8VffNHNCgUuSv/+o9xDm4oXf0HatetsBTIrrfVd5cSJo1bAMMvsa9Omte59d+8+0PRKad26sXXsqlKwYDErIMga4Zx0+JPuW2c20hBDp0/Wqp706f/t16JhRdmylc251KxZyhyvRo268jjixIljZmOKHz+hjB49SL7+erBkyOArn38+wjQZdtFz0JBJv/CHD2WOHz9i1ms1zP2PEVeGDh1vKmK++uoL0wRZ37uGD0pDGR2as2rVX9ZxO5lZn3Ta5/ChkE43Xq9eU9O7p3Hjamb4Vt26TeRxvPLKa1KtWi0ZNKiHdV69ZNeurffdVoMOrQ7SKa8nT/7GCmT0s3tbnhYNe7p3H2Cus86ApTNTKb1Gr7/+rgld9GdzyZLfrfdQO0Ivnsj8fAEAgIg8BAAQJWbPLnu8cuX+aeLHTyuwz/efhUrVtzNJ7PgxxG46vKNlywZmSuGWLTsIEF7nzq3N7cCBYwRPx68jDkrDjz0kbkIBADznAgL2y4oVfffWqbMih0QjVMoAAGATneq4bdtOZsiONvwFAACAs9DoFwAAG2kz2wEDxjBjDQAAgAMRygAAYLN8+QoJcKcWLdoLAAB4vhHKAAAAREM6JTcAAHi+0VMGAAAAAADABoQyAAAAAAAANiCUAQAAAAAAsAGhDAAAAAAAgA0IZQAAAAAAAGxAKAMAAHAf58+fldmzp1m35+RpCgsLkw8/fEuOHTsiAADAOQhlAAAA7uP333+Wb74ZKvPn/yJPk4eHhxQsWExGjOgrAADAOQhlAACAba5evSKnTp2Q6OBe51K9+qvSpMm78tJLr8rT1rhxc1Mp89tvMwUAADgDoQwAALBNy5YNZPr0CRId3OtcUqRIJW++2UqSJUshT1usWLGkVq0GMnnytxISEiIAAOD5RygDAAAQTRQtWlouXboo27b5CQAAeP4RygAAgKdi6tTx0qlTK6lXr6I0blxNhgzpJRcuBJjnFi+eJ9WqFTWNdBcsmG3uf//9qPvu66WXisuyZX/IhAmj5bXXKsucOdPN+tatG0v//l0jbFu3bgX57rvh7sdTpoyTVq0ayYoVf5rb2rXLSI8e7eXMmVMPPZdt2zaZx3rr0rlzaxk9epD88MM31vuqLvXrVzKPdX/6fnX/HTq8I4cPH4hwXgEB52XQoJ7SoMGL5np8+WW/uypismTJLl5eXrJ9+2YBAADPP0IZAADwVGTKlEUqVKhmhRh9pWnTluLnt9YKVW6HHYULl7QCim8kYcJEUqJEWXO/Zs36D9zf9Onfy44dm+X997tKkSKl5FEcPXrYhCgtWrSXYcO+F3//AzJq1MDHPpdFi36Vgwf3yeDB30nlyjVk7twZ0q1bOylVqrx8/fU0uXbtqnWcPhFe07v3x7Jq1VIrtGkkDRu+LStXLpFvvx0WYRtPT09JlSqtHDlyUAAAwPPPSwAAAJ6C0qUrRHh8/PhRWbp0obmfNGkys3h5eUuSJMmkQIGiD91fQMA5GT58gsSOHVselVakDBjwtbs3TMmS5eTvv5c89rmkSJFaPv10oKlqefnleqZyp3z5qlKnTmPzvIYzs2ZNNlNd68xKW7ZslN27t1uBUhd34KPHGjy4pzRr1lrixYvv3nf8+Ank4sULAgAAnn+EMgAA4KnQ4UBjx46wAokNJlBR2sz2cZUrV+WxAhmlFSjhm/X6+MSUmzdvyONKmjS5CWRU3Li3A5XkyVO6n9eQRYOgW7dume22bFlv1mvPGJecOfNKUFCQ7N+/20yH7eLt7SPBwUGCp08/n48+GiIZsySR7NkzSrZsmawlg8SJ83g/ZwAAPCpCGQAA8MRdv37N9FVJnTqddOnSV/LkKWhmFZozZ5o8rsSJk8qzSoczqWbNat31nKu3jYteu0SJEguevhgxYkjz5q/J0ZMHZO9ef5k/f4Xs23fE+llLYIUzGc2SPfvtoCZ9+tQCAMCTRigDAACeOG3Ke/r0SdNPJk+eAuJ0riqdPn2+vKtaKF26TBEenz17ygoBcgmiRp48WaR46SwR1h0/ftoKZ/zNsmDBCvnqqyPW5xJwV1CTNWtGiRcvjgAA8LgIZQAAwBN34cJ5c6tNa110mM6ddGhPWFioPC4dhhR+BqOrV69IUNBNeRz/9VweJG/eQuY2ZsyYD+xZozM06ZTYOXLkEdgnbdqUZqlQobh73Y0bN91Bzd69h2XhwpXWz7S/JEgQL1xYk9EENRkzphEAACKDUAYAADxxrkqPmTMnmdmNNm78x0zzfOPGDesL7S7ry+vt5319s8u2bX6yefN6uXz5kpQr96I8isyZs4mf3xq5efOmCYK+/nqQ6R/zOP7ruTxIrlz5pFix0jJ8+Ofy3nsfSZw4ceWff5abWaH69/93KvB161aacOjOJsmwX6xYMSVfvuxmCe/EiTPusGbRotUyevQ0OXXqnAlpsmbN8P+qmtuBTbx4cQUAgPAIZQAAwBNXvPgL0q5dZ5k9e6osXPir9SC7smIAABAASURBVEW2kIwbN0smTvxaNm1a6w5l2rT5xAQVXbu2lUSJkpiKkiRJIt875u2328qlSxekfv2KpkFuq1Yfm54sj+Ne5/IkffrpIPnqq/4ycuQXEhgYKFmyZJd69ZpG2Gbu3J/M7Ew6PTeeDWnSpDBL+fL/Nmu+eTPIhDT79x8xVTV//vmP6VmjQ5102JMrqNElU6a0AgBwLg8BAESJ2bPLHq9cuX+a+PH5BdxO338WKlXfziSx48cQIDpZtGiujBkzxAqufqXRbxT5dcRBafixh8RNKFHi1KmzppGwBjWu6prjx8+YoEZnfro9A9TtRYdFAQCenICA/bJiRd+9deqsyCHRCJUyAAAANgsLC7PCmNHy1lttCGSeY6lSJTdL2bJF3OuCg4NNULNv32FTTbN06VpzGzt2TPcU3a7AJnPmdAIAeL4QygAAANjMw8NDevQYbHrPwFm8vb0ld+4sZgnv9OnzJqjRwGb58nUyduxMOXr0ZISgJmfOTOLrm94K8hIIAODZRCgDAAAQDeTOnV8Al5Qpk5qlTJl/q2pCQm65gxq93bZtr6xbt80KdrzC9anJYG6zZMkgAIDoj1AGAAAAeAZ4ecWQXLmymCW8s2cD3H1q/v7bT77/frYcOnTMHdTkz59d0qVLZQKbxImjqIEOACBSCGUAAACAZ1jy5EnM8sILhd3rQkNDTVCjM0CdOXNeFi9ebSpsYsTw/H9Yc3sWqKxZb0/dDQCwB6EMAAAA8Jzx9PSUnDl9zaLeeaeeuT137sL/q2qOmKqaCRNmy8GDx0wj4Zw5s4ivb7r/zwKVSRIlii8AgKeLUAYAAABwiGTJEpuldOlC7nW3q2r8rXDmiOzefcgKazaanjU+Pj5m+JOGNFpVo0umTGkFAPDkEMoAAAAADna7qiazWWrUKO9er8OetE/N7am618m3386Q48fP/L+SJqMUKJBT0qdPZe7HixdHAACPjlAGAAAAwF1SpEhqlvC9aoKDg01Io2HNqVNnZe7cv8z9BAni/b+aJuP/e9ZkknTpUgoA4MEIZQAAAABEire3t+TJk9UsqmXLBub2xIkzpleNBja//77CCmp+kICAyxGGPml/m6xZ05t9AABuI5QBAAAA8J+kSZPCLBUqFHevu3490IQ0Gtbs2LFfNm/eLYsWrZK0aVNIjhyZ3GGNLtrnBgCciFAGAAAAwBMXJ05sKVgwp1lcevduJ4cPH5c9ew6bsGbatPnmVpsNuwIaV2Dj65teAOB5RygDAAAAIMroDE66VKv2gntdQMCl/w9/Omym6h4//hc5cuSElChRQJInT2wFNZn/v2SSWLFiCgA8LwhlAAAAANgqSZKEUrJkAbO4hITcMk2E9+w5ZJaFC/82FTYpUiRx96hxVdYw/AnAs4pQBgAAAEC04+UVQ3Ll8jVLeEeOnDQVNbt3H5SffppvghrX8CetpsmbN5v4+qYz1TgAEN0RygAAAAB4ZmTIkNosL75Yyr3ONfxJK2o2btwho0dPlVOnzpkqGg1qcubM7B4C5eHhIQAQXRDKAAAAAHim3Wv4040bN00VjQY1W7bslRkzFpn72bJlvCOoyWSaEgOAHQhlAAAAADx3tCFwgQI5zBKeq6Jm9+5D8uef/5jgplixvOLt7WWGSmmvGg1qEidOKADwtBHKAAAAAHAM19Tbr7xS0b3u2LFTJqTZteugTJ481wQ1Pj5e7oBGb3VJmTKpAMCTRCgDAAAAwNHSpUtllvB9ak6fPm+aCWtA8+uvf8nAgePk5s1gM+zpdkiT2VTW6OsA4HERygAAHGfXmgviHdNTADhbcFCY9V+avuLetCpGl/Lli7nXXbx42VTUaFizZMka+fnnP0x1jSuocQ1/YuYnAJFFKAMAcJRCFTwk+OZleVYcOHBEVqzYILVrV5IkSRIJgCdH/3/gE4tgBpGXKFGCuxoKX7163fSo0XDm77/9ZOzYWabKxlVJ4wprfH3TCwDciVAGAOAohSo+G1++tL/B559/I0mTJpShE1oxMwgARFPx4sWRIkXymMUlMPCGu0fN2rVbZeLEOeb/6xrOFC+ezwx5ypUri2TJQlADOB2hDAAA0cxXX/1oyuJ79GglRYvmFQDAsyV27FhSqFAus7gEBQWbkObw4eOyfv12+eGHX+Xo0VOSO3cWE9BoYKP3M2dOJwCcg1AGAIBo4u+/N8pnn42SZs1qy6+/jhIAwPPDx8fbPUW3DklVGtTs3HnACmsOmIqaCRNmy8mTZ90BjS70qAGeb4QyAADY7MyZ82aoUubMaWX27K8kYcL4AgB4/mlQU7BgTrO43Lhx01TUaFjj6lFz5kyAFCuWVzJmTCN58mQ1S+rUyQXAs49QBgAAG02dOk8mT/7NDFUqXbqQAACcLVasmHcNfbp+PdAKag7Jjh37ZPHi1fLllz9Y4U2QqaTJkyeLCWly584qSZIkFADPFkIZAABssHHjDunVa7Q0bPiSLFjwrQAAcD/a7L1Ikdxmcblw4ZKpptmx44DMnPmHdf9r8fHxCRfS6G0267WxBED0RSgDAEAUGzFisvVL9H759ttekiZNCgEA4FElTpxQXnihsFlcTp06a0Ia/Tdm/Phf5Nq1QDMTVN682awlq7nVHjUAog9CGQAAosiSJf9It25fSt++H8iHH74hAAA8SalSJTdL5col3esOHTom27fvs5b9Mnv2Etm/399U0mhAo5U0GtakTZtSANiDUAYAgKcsODjECmOGi4eHp6xaNVW8vGIIAABRQafY1uWVVyqaxyEht0wljQY1y5evk9Gjp5qKGg1pChXKITlzZjH348WLIwCePkIZAACeoj/+WCWzZv0hjRrVkEqVSggAAHbSPwy4puZ2uXjxsglp9uw5LJMnzzX3U6RIKvnyZZP8+XOYkCZr1gwC4MkjlAEA4Cnp2nW4uf3uu94CAEB0lShRAilTpohZmjevZ9YdPHhUtm3bJ1u37pFp036X48fPWAFNdhPQaKCjtwkTxhcA/w2hDAAAT9jatVvlww+/kD592knVqi8IAADPGl/f9GapXbuSeawNg7du3WuqaH76aaH06DHSCnPiS8GCOSVfvuwmqNHtATwaQhkAAJ4gnVkpIOCS/P33D+Lt7S0AADwPYseOJSVK5DeLi7//CRPSbNq0S6ZO/V3Ong2wwpmcJqhxDZGKEYM+asCDEMoAAPAEXL58VVq37i3VqpVhZiUAgCNkzJjGLC+/XN48vnLlmmzZsls2b94tY8ZMt+7vkVy5fP8f0OSUQoVySpIkiQTAvwhlAAD4j/7+e6P07DnS+gX0M8mZM7MAAOBE8ePHdfemcdFKGg1nFi1aKb/9ttRU1xQunNu9pE6dXAAnI5QBAOA/GDt2phw4cFSWLp0oAAAgIm0IrEuTJjXN4yNHToqf307Tf02racLCxApncpmAplChXJIpU1oBnIRQBgCAx/Txx4MkW7aMMmDARwIAAB4uQ4bUZnn11crm8alTZ62QZpcJan788TczHLhQodxStGgeKVYsr2TOnE6A5xmhDAAAj6FNmz7SoEF1qVChuAAAgMeTKlVyqVFDl3LmsTbL37Rpp2zYsENmzFho+tQULZpXihfPa27Tpk0pwPOEUAYAgEdw8eIVqVKlucybN0ZSpkwqAADgyUmSJKFUrlzKLOrcuQtWQLNd1q3bLuPH/yJhYWGmgqZYsXzmNlmyxAI8ywhlAACIpIMHj0qLFj1l7drp4unpKQAA4OnS0KV69bJmUSdOnJH167fLqlV+pnmwPi5ZsoCUKFHAus3PFNx45hDKAAAQCdeuXZePPx4sS5ZMEAAAYI80aVJI7dqVzKK02f6aNVvkp5/mS+fOQ01T4TJlCkvp0gXF1ze9ANEdoQwAAA+hv/B16jREZs/+SgAAQPSRJUt6s7hmd1q/fpusXOlnBTTD5Pr1G1KxYjEpVaqQvPBCIQGiI0IZAAAeQBsOtmrVSxYvHi8AACB6u91rJp906NDMzOy0evVmU0XTocMAqVq1tBQvnl/Kly8qCRPGFyA68BAAQJSYPbvs8YwZy6WJGZNfAp4VoaFh8vbbs2TSpNcEAAA8u/Tf9M2bT8rGjcfFz++4pEmTQMqUySj586eWpEnjCJ5/168HyLFja/bWqbMih0QjhDIAEEVmzCjcLixMkgmeGaNGSZuXX5YZmTPLOQEAAM+N7dsl/eHDkmH/fikWK5Zc9fUN21KggOxOmdLjiuB5drxhQ7+xEo0QygAAcA9FihQZERoaun/Tpk0jBQAAPLcKFixYzMPD4yVPT88WYWFhh6z7P1y9evWnPXv2ENDgqSOUAQDgDoULF9ZugS39/PxqCQAAcAzrjzJlrGCmnhXMvGM9XGrdH2/9PvCbAE8JoQwAAHewfiE7eOnSpVz79++/KQAAwJEKFSpUywpnaljL61Y4M+7WrVvfbNmyZa8ATxChDAAA4ViBTJ/Q0NCgTZs29RUAAOB4uXPnjhcrVqx3rbslrCW19XvCl5s3b54jwBNAKAMAwP/pL12xY8c+sXHjxgQCAABwh0KFCpXz8PB4S3vQhIWFDfPz8xsswH/gKQAAwPDx8fn41q1bHwkAAMA9bNq0aYUVxLxz8+bNgtbDZEWKFPEvXLhwP+u+lwCPgUoZAAD+z/rF6nJgYGCanTt3XhUAAIBIsH5/6GrdNLOWmRs3buwhwCOIIQAAQH+hei0sLMxn69atUwUAACCSTp48udJaRqVOnbp0mjRpFqVMmfLYqVOntggQCQxfAgDgtoo6q4IAAAA8Bj8/v34bN26M7+npmblw4cJb8lkEeAhCGQAAbnv72rVrqwUAAODx3bLCmV4eHh5Nvb29+1nhTHsBHoBQBgDgeNYvTCXDwsI27d+//6YAAAD8Rxs3btxmhTO1rLsh1u8Z0wS4D0IZAIDjWX/NKmzdzBQAAIAnyApmRt26dWu4DmcqUqRIHAHuQCgDAHC8sLCw0lYwc1oAAACesC1btqy7fPlycev3jWW5c+f2ESAcQhkAgONZgUyukJCQXQIAAPAU6BBpPz+/4rFixZovfA9HOPwwAAAcz/rL1SXrr1jbBQAA4CkKDg5uR48ZhEcoAwBwtKxZsyb4f0+ZYAEAAHiKtm3bttv6Y9DaQoUKfSSAEMoAABwuYcKEya1fjs4KAABAFNi0adMw6w9Czay7XgLHI5QBADhaSEhIUutmgwAAAESdKYUKFWohcDxCGQCAo3l6eurwpaQCAAAQRUJDQ/+wbkoIHI9QBgDgaFYgEyssLOyGAAAARJHNFut3kNwCxyOUAQA4mvWXKi8rlDkjAAAAUetCjhw5MgscjVAGAOBonp6eMa2/VMUVAACAKGT9/nEyTpw4DKF2OEIZAICjhYWFeVq/FIUKAABAFNLh09bvILEFjkYoAwBwNE9PTw/rl6IwAQAAiELa1y4kJMRH4GjMiw4AcDQrj/EQAACAKGb9DpLSy8vLW+BoVMoAAAAAAADYgFAGAACep/s/AAAQAElEQVQAAADABgxfAgAAAAAg6l0KCgq6JXA0QhkAAAAAAKJeQh8fnxgCR2P4EgAAAAAAgA2olAEAAAAAIOr5h4SEBAkcjVAGAAAAAICol9HLy8tH4GgMXwIAAAAAALABoQwAAAAAAFEsNDT0mHXD8CWHY/gSAAAAAABRzNPTM511w/Alh6NSBgAAAAAAwAaEMgAAR7t161agdXNcAAAAgCjG8CUAgKPFiBEjtnWTVgAAAKLWUabEBqEMAAAAAABRLz1TYoPhSwAAAAAAADYglAEAAAAAALABoQwAAAAAAFHMw8PjTEhISLDA0egpAwAAAABAFAsLC0vh5eXlLXA0KmUAAAAAAABsQCgDAAAAAABgA4YvAQAAAAAQxUJDQw9aS5DA0QhlAAAAAACIYp6enr7W4iNwNIYvAQAAAAAA2IBKGTjWjBlFWoeFhaUUAI62cmVorpMnPdJ17ly4lwAAHMnT0+PKa69tHCoAEMUIZeBY3t5xumTIUCZDzJgJBYBzXbrkL2FhJyVPnpKlBQDgOCEhN+TAgT8uWXcJZQBEOUIZOFqWLFUlYcKMAsC5jhz5W/z9/axQpoEAAJznxo0LGsoIYINTYWFhwQJHI5QBADhajBieEi9eHAEAAIhiqTw8PLwFjkajXwCAo926FSpXr14XRE+7dx+Un3/+Q0JCQgQAAOB5Q6UMAMDRPK0/T8SKxWyU0dWwYZPEz2+nZMqUVooUySMAAADPE0IZAICjhYZqP4GgCOvWrt0qM2cuko0bd0j27Jmkbt0XpVq1MvK4Dh06Jq+91sH9OFmyxJI1awZ59916UrBgLsH9tW7dSLZt2ysFCuQQAACA5w3DlwAACGfFig3Stu3nki5dSunZs7WkTp1cuncfIXPn/iX/1Ztv1pYxY3rKW2+9KseOnbZCmZ4m+MH9FSqUy1w3Ly/+jgQAeO4ctJYggaMRygAAHM3Hx1uSJ0/iflyqVAEZPbqHtG//plSsWEJ69WormTOns0KZpfJfZcyYRooVyyeNGtWQKVMGStKkiaxjTRNEja1b98jYsTMFAIBowtdaGEPtcPzZCQDgaEFBwXL2bID7sbe3t5QokT/CNmnTppCTJ8/JkxQ3bhwpXDi3rFzpZx5v2rRLWrToKbNmfSnjxs2SJUvWyOTJA0wg9PXX0812J06cMcN4evduZwIdl+LFG0q7dq/L+vXbreBhr8SOHVNq165khv646D7//PMf+fTTVjJy5BTZt89f/vprgoSFhcmkSXNk6dJ1cuDAUVMZ1Lx5XalevWyE89XXzpr1h2zfvs9UEWlg1aJFffH09JTz5y/KiBGTZfXqzWY2q3Llikrnzs3d1S3+/idk4MBxsnevv2nYmy1bRmncuIZUqlTSuv5B1vsZI1u27JYLFy6b3jFVqpQy1TG6bz3v776bKevW/RTh/Xbv/p78889mM9QsYcL40rRpTalfv5p7m9Onz1kBzCxZtmy9XLx42cywpUPRmGkLAABEJ1TKAAAcLswECQ9y+PAJ8fVNJ0+aHldDkfC6dBkmsWLFlB49WpuAYsyY6SY0yZXL1woiWpoApHXrPne9TrfLly+b/PLLCHnnnboyfvzPsnjx6gjbnDt30exfq4E++6yNWaf71modHSbUp087yZMnqxXcfCXLl693v27z5l3mdXHjxjbbVKhQ3IRIodqQx/Lxx4NMqNOo0Uvy9tt1TKCkDXpdBg/+3gq1zkrbto2lW7eWkiJFUitQ2WKe+/HHedZr10rDhi9Jv34fSpEiuU2QcuvWLXkQDXlSpUom06YNlhdfLCkDBoyTXbsOmOc0hHnzza5miNiPPw6QUaM+lThxYlvblbLOq7MAAABEF1TKAAAczsNMi30/+kX/2LFTVijx7n23OX78tAksEiVKIJGloYpWjuTMmTnC+pQpk5lqFnXjxk2ZPn2BVKlS2gyjUjoDUc2abWTVKj8pU6aI+3WvvFJRWrZsYO43aFBdZsxYaIZc6WtdNKx4//0m0qzZq+axVqlMmDBH6tWrYoZrKa1e0QBFq0zKly9m1o0f/4sZejVkyCfi4eFhtnHRnjhaPaPXx1Wpoo2Me/YcaSp14sePa13Dg1KuXBGpU+dF83zVqi+4X69TXmvVzxtv1DKPNfCJjBo1yrnPWV87ceIcc5xcubLIX3+tNeGV9u9JlSq5WTS40eDqtdeqmfcAAAAQHVApAwDAfWglyJAhE81wnJIlC9xzGw1satduJ7VqtYvUPjWM0SCiW7cv5eDBo6bpb3ivvFLBfV9nHdJgRitbXLT/jQ4x2r59f4TXpUiRJMJjnd1pz55Ddx1fhzX9u/99cu3adSlaNG+EbXRYlZ5jYOANU7GiQ4Q0DLpXmKFDplTp0oXc6/LmzWqGhek+VPnyRWXRolXy9dfTrCDqcITX67XVEKhHj69kzZot7uqbh9EqGRetLFKBgTfNrWsfceLEcm+jw5auXQt8aAUOAABAVKJSBgDgaF5eMe5b4fLVVz/K/v1H5Kefhtz39YkTJzCv1z4rD/P552PMojQk6NChWYRqFxW+V8yVK9fMbZ8+Y8wS3qlTD+5xo/sPCLgUYZ32aAn/Xi9duuLeNrwECeKa29Onz0uSJAlNyOFad6erV6+b21q12t71nOscO3Z8W9KkSWHCnQkTZku+fNmlU6d3JGdOX3n55fKmUmnZsnXWdoOt84svH3zQNEI1zaMqU6awGa6k1TNdurSwrsNFUzVUtmwRZnECAEQn/iEhIcy+5HD8ZgIAcLSQkFtmWM+dFi78W3788TfTg0SHv9yPNuz988/xEhnavLZ06YKm2kWbBz8sINChTKpNm8aSP3/2CM/pEKEHuXz5aoRZpR60f1f48+9rbz/WgCRBgngSM6aPO3y5k6tC58svu7grVly0J47SgOTdd+ubRatitD9N+/YDZP78b0xQpNU7uly/HihDh040VURa6ePrm14eh35eGvr06jXaNCdWBQrklK5dWwgAANFIRut3AWZfcjhCGQCAo3l7xzDhQ3gbNmy3vtB/bfqk6PCaJ0X7stw5VOhBsmRJb3qy6FCgh71OwyUXHaLj57dLChXK+dD9a5WMNu0N33vGz2+n5MiR2V1Vo02AN2zYcc996HNKg5vIvDcdelWt2gumEbD2fQkfHGl4o/1wfv31L9mz5/BjhzJq6tTf5b33GkiLFq8JAABAdEUoAwBwtOBgrZS54n68f7+/dOgw0MwCpJUeGtC46HTUOmV2VNHKk2bNapumuylTJpX06VPJ7t2H5Lfflsk33/SUxIkTuredPn2+JEuWyJyzzoSk1T9NmtR86P61p41Oua33dfYmnflIm/cOHdrJvZ1Ofd28eQ8zvKhGjbKmoe7mzbtNI10diqT9ZD7//Bv56KNmpuHx8uUb5PDh42bWIx069O67PU24VbBgTjNcTKtXdFpsrfZp06aPuS1ePJ95jxqmaDij1/q/OHMmwHofOyVXro3mvWn4o9dPK3MAAACiC0IZAIDjhf+ivmPHAdPgVvuf6BJ+m3/+mSpR7a236ojOfj1p0q9y9myAZMiQWmrXrnhXH5jq1cuYMGTEiB9NXxqdralgwVyR2r+aM+cvmTx5rglVund/zz3zktKhPzo8adSoqWa6bK340RmbYsSIYZ4fNOhj6d//O/nii7Gm2W727BmladNXzHNJkiSyApsPZObMRdKv37cmINHeLhr0aONgPc8pU+bJDz/MlXPnLphwZvz4z00Pmv9CZ5nScwofqumQqG+/7SUJE8YXAACA6IA5IeFYs2eX9a9YsU+GhAkzCgBnqVq1uZw/f+mez+nsSH5+P8uzpHjxhtKy5WumZwvuFhISYqbtbtu2r5mWWxsPA4DLjRsXZP789y/Vq7c6kQBRqHDhwgusmxF+fn4LBY5FDS8AwHHq1q1iKl+0UiP8ooFMsWL5BM+2vn2/kV9+Wex+rA2VtWoobdqUd81IBQCAXazfO45bfzgIFjgaw5cAAI7TqNHLsnjxP+LvfyLCeu3R0rjxS4Jnm7e3lxkOpTNDuWaE0kbFBw8elXfeqSMAAEQH1h+E0lp/OIi6ZnWIlghlAACOo7MtvfhiKfn++19MdYyLNp+tUKGEPGu+/rrHf+7B8jz54IOmZprvTp2GmpmrtJFw9uyZZPjwLqafDQAAQHRBKAMAcKRGjWrIn3/+Wy2jzV8bN64hz6JHmWbbCWLHjiV9+34oAAAA0R09ZQAAjpQ4cQKpUqW06SWjfH3TRZhxCAAAAHjaCGUAAI6llTHp0qWSRIkSuKdwBgAAAKIKw5cAONb5k2FyYIvA0eJJxQJvy9GjpyR2YFFZtzBM4FxJUotkLeAhAAAAUYVQBoBjBZwU2bsphqTPGV/gXAUKVLAWkcBAgYNdPBskAacCrVBGAACIEmFhYYc8PDyCBI5GKAPA0RIl95a85ZIIAGfz33FVzhwkmQMARB0rkMls3fgIHI2eMgAAAAAAADYglAEAAAAAALABoQwAAAAAAIANCGUAAAAAAABsQCgDAAAAAABgA2ZfAgAAAAAgioWFhZ0NsQgcjVAGAAAAAIAo5uHhkdzb25vv5A7H8CUAAAAAAAAbEMoAAAAAAADYgFAGAAAAAADABoQyAAAAAAAANiCUAQAAAAAAsAGhDAAAAAAAgA2YfgsAAAAAgKh3JCQkJEjgaIQyAAAAAABEvQxeXl4+Akdj+BIAAAAAAIANCGUAAHiCzp8/K7NnT7Nuz8nzZtWqpbJ//x4BAADAk0EoAwAO5u9/UAYM+FROnz75SK/7++8lsn37ZvmvZsyYJD/9NFGeJ7///rN8881QmT//F3ne6PvasmWDAAAA4MkglAEABzt69LAsXbpQbt688Uiv69+/6xP5cq7HPnDg2a28uHr1ipw6dSLCuurVX5UmTd6Vl156VaKb5csXS/v2b0vjxtXk668Hy40bkf/cT548LmfOnJL8+YsIAAAAngxCGQAAHlPLlg1k+vQJEdalSJFK3nyzlSRLlkKikz/++M2EaUWLlpZWrTqKn99a6dTpvUi/XkO4+PETSJYs2QUAAABPBrMvAQDwfzt3bjVhRdOmLeR5M2fONHnxxZfd7y1VqjTywQfNzHvOnTv/Q1+/bZuf5M1bSDw9+XsOAADAk0IoAwDPgc6dW0vGjL6SLVsumTZtvGTOnE169BgkAQHnZdy4EbJhw2qJESOGlChRTtq16yxeXvf/3//UqeNl8+b1ZliRj4+PFClSSpo3/0ASJ04SYbvg4CDTj2bt2r8lXrz40qjR2/Lyy/UibKPVGQsXzpH9+3dL+vSZpHXrT6wv9gXve+yQkBAzpCpTpizi4eFx3/eq+0qQIKEsXjxPrl27KhUrVpf33vvInG9kj3327GmZMmWsrF69TC5duihx48YzVSBx4sR76HXQ4w4Z0stst2DBbLM0bPiWvPNOOyu82CQdO7awnh9rwo6GDatIoULFpXv3Ae5jb93qJ5980lJ6ZOevlwAAEABJREFU9hwsL7xQ8bE+p0ehQ6wOHNgrdeq87l6nPytx4sSVf/5ZHqlQZvfu7VKlSk0BAABPhvW7ztVbFoGj8ecuAHhObN26USZO/Fpef/1dqVWroVnXu/fHZsac2rUbWeHA27Jy5RL59tthD9yPBiIVKlSzwo++0rRpS1M5MmHCqLu2+/nnH01A0bFjLxMCffXVFxH6zKxfv1qGDu1twpX27XtIypRppFu3tqYvyf0MH/65tGrVyNr3lAee46JFv8revTvl889HWPv+1DTVnTt3RqSPrSHMBx+8KSdOHJNRo36U/v1HSezYcaRs2RetazbsodehcOGSMmjQN5IwYSIrQClr7tesWf+u89SARUOXjRvXSPjfudatWykxY8aU4sXLmMeP+jlNmDBaqlUretei6+/F1cg5efKU7nVa8aKPT58+IQ+j1+v48SOSK1c+AQAAT0ZYWFg869/jGAJHo1IGAJ4Thw7tlxEjJkrOnHnN4y1bNprqhvff7+IODJIkSSaDB/eUZs1am+qWeyldukKEx8ePHzUNee9UuXINU52iSpUqL02a1DDBSIECRc26mTN/sI6X1ApHxpnHFStWM4GLbvPuux/c89gZMmQ2t1rZ8iAasvTqNcxUkmh4MnXqONm3b5f7+Ycde+XKv0x1yoABY0wPGF3KlXtRJk0aI6+88poJcx50HZImTWYWLy9vc01d7/leSpeuKAsX/irbt29yb6cVMRrIeHt7P9bnVK1abSsYKnHX+uTJU93zHC5eDDC3WhkTnu77woUAeRgduqTnmjMnoQwAAMCTRCgDAM8JDSdcgYzasmW9udXGri76fFBQkBnSU7BgsXvu5/z5szJ27AhT9RIQcM6sixUr1l3bhQ8AtOpCh8BouKB0GJJW7mgPExcNOnLkyGNts03uR4cA6fIwSZMmjzC0J2bMWO4ZpCJz7LCwUHOr1TEuOmzp+vVrpqJF9x3Z6/AwRYuWMsdZu3alCWV02JQGaA0aNDPPP87nlCZNOrNElvWXuAc9Kw+joYyek1b3AAAA4MkhlAGA50SiRBF7vmivFdWsWa27tr3fECINJTp0eEdSp04nXbr0lTx5Csrkyd+aJrEPozPznDt3xtwPDLxuggDtvaJLeNpg9mmKzLG1SkWDkp9+mmgqVLRa5I8/5pqhSBrI/JfrcCcdwqSVRDpkqWXL9qYHjx5Dj6Ue53PSYUp3zvqktK/P22+3vWu9qx+Q61guOqW39iJ6GA259D0AAADgySKUAYDnlGtK5j59vryrwiNdukz3fM2yZX+Y/iPaRyVPngLyKPQLvvZYURrQ6DE1/Liz14qPz9OttojMsXW4Utu2nUyz3nnzZpl1+n7ff7+ruf9frsO9lC1bWf76a4Gpklm3bpVp/KuNhdXjfE6POnzJFdidPHncOvbtdaGhodbjY/etmHLRfjJa2dOq1ccCAACAJ4tQBgCeUzp9sdIhJw/qeRLehQvnzW2qVGnd63QIzb2EhASHu397yFC+fIXd67S6RIcARfbYrv08bPalyIjMsWfPnipvvPHePae/jux10IoX11CoBylW7AXzOSxfvlg2bvzH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style=\"max-width:100%;height:auto;\" alt=\"Figure 5.1 \u2014 Implementation architecture.\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebd54c2b",
   "metadata": {},
   "source": [
    "## 6. Data acquisition & preparation\n",
    "\n",
    "Every line below is commented so a student can re-run and modify each step. The cell ends by producing the standard analysis variables: `df`, `X` (clean numeric features), `y` (binary label), `feat` (feature names), and `family`. `family` is the per-group label used for the recall breakdown. It is an attack family on the intrusion corpora, but a transaction type, merchant category or malware category on the fraud/malware ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1b82e1b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import time, warnings; warnings.filterwarnings('ignore')   # keep output clean\n",
    "import numpy as np, pandas as pd                            # numerics + dataframes\n",
    "import matplotlib.pyplot as plt                             # static plots (embed in HTML+PDF)\n",
    "plt.rcParams['figure.dpi'] = 120                            # crisp figures\n",
    "RANDOM_STATE = 0                                            # single seed used everywhere\n",
    "np.random.seed(RANDOM_STATE)                                # reproducible sampling\n",
    "NEG_WORD, POS_WORD = 'benign', 'attack'                      # class names (overridden by some loaders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bba3a93a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded 4,847,499 rows x 68 features; positive rate 0.5619\n"
     ]
    }
   ],
   "source": [
    "import os, glob\n",
    "# Kaggle auth: token read from ~/.kaggle/access_token (students supply their own).\n",
    "os.environ.setdefault('KAGGLE_KEY', open(os.path.expanduser('~/.kaggle/access_token')).read().strip())\n",
    "import kaggle; kaggle.api.authenticate()\n",
    "REF = 'dhoogla/cictoniot'; DEST = '/tmp/kg_' + REF.split('/')[-1]\n",
    "if not os.path.exists(DEST):                                   # download + unzip once (cached)\n",
    "    kaggle.api.dataset_download_files(REF, path=DEST, unzip=True, quiet=True)\n",
    "NROWS = 1_500_000                                              # per-file read cap (memory bound)\n",
    "files = sorted(glob.glob(DEST + '/**/*.parquet', recursive=True))\n",
    "df = pd.concat([pd.read_parquet(f) for f in files], ignore_index=True)  # combine day/part files\n",
    "df.columns = [str(c).strip() for c in df.columns]             # strip header whitespace\n",
    "LABEL = 'Label'; FAMILY = 'Attack'\n",
    "df['y'] = (df[LABEL].astype(str).str.strip().str.lower() != '0').astype(int)  # benign=0\n",
    "df['family'] = df[FAMILY].astype(str).str.strip()             # descriptive attack family\n",
    "df = df.reset_index(drop=True)\n",
    "assert len(df) >= 1_000_000, f'floor not met: {len(df):,}'    # honesty gate: >= 1M rows\n",
    "DROP = list({LABEL, FAMILY, 'y', 'family'} | set([]))  # never leak label cols\n",
    "feat = [c for c in df.columns if c not in DROP]\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "X = df[feat].copy()\n",
    "idlike = [c for c in X.select_dtypes(include='object').columns if X[c].nunique() > 0.5*len(X)]\n",
    "X = X.drop(columns=idlike)                                     # drop ID/timestamp-like leaky columns\n",
    "for c in X.select_dtypes(include='object').columns:           # encode remaining categoricals\n",
    "    X[c] = LabelEncoder().fit_transform(X[c].astype(str))\n",
    "X = X.apply(pd.to_numeric, errors='coerce').replace([np.inf,-np.inf],np.nan).fillna(0.0)\n",
    "X = X.clip(-1e15, 1e15)                                        # clip huge NetFlow counts (float32-safe)\n",
    "X = X.loc[:, X.nunique() > 1]                                  # drop constants\n",
    "import re                                                      # LightGBM rejects special chars in names\n",
    "_seen, _cols = {}, []\n",
    "for _c in X.columns:                                           # sanitize to unique, safe names\n",
    "    _c = re.sub(r'[^0-9A-Za-z_]+', '_', str(_c)).strip('_') or 'f'\n",
    "    _seen[_c] = _seen.get(_c, -1) + 1\n",
    "    _cols.append(_c if _seen[_c] == 0 else f'{_c}_{_seen[_c]}')\n",
    "X.columns = _cols; feat = list(X.columns)\n",
    "y = df['y'].to_numpy()                                         # STANDARD CONTRACT\n",
    "NEG_WORD, POS_WORD = 'benign', 'attack'               # class names for plots\n",
    "print(f'loaded {len(df):,} rows x {len(feat)} features; positive rate {y.mean():.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21c40acb",
   "metadata": {},
   "source": [
    "## 7. Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bb5432a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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pzz77rPuD74033rD69evb5s2bLU+ePP5133vvPfeHXrt27eyJJ56wVatWWd++fe3s2bMuUJSUj1N/9CmQ1aVLF/fH3M8//2xjxoyxRYsWuT8AFeAKVKVKFReMDKTAR0K60mP1vPPOO5YtWzb/z+nSpYuwTlJ4TeNyrKNHj3bv4UB79uyx559/3po2bRph/SZNmth9990XZlnVqlUtIR09etReeOEF9z6sXLmyrVixIsaPvXz5srVs2dJ+/PFHe+qppyxv3rw2btw4F8T57rvvwgRYv/jiC2vTpo27TwFkvedffPFFO3z4sHtvJOXj1Hvv2LFj1r59e3dMuvih39fPPvvM/b9UsGDBMOsXLlzYXnnllTDL9PueUOJyrB49vkSJEmGW6QJQoMR+TeN6rM8995wLGgc6c+aM+z8o2O9rYv4//Oqrr9qaNWvce7BSpUou8Kv3oIKC69atswoVKqSI39No+QCkGJ988km06zz99NMJsi8AACDpW79+vU9/EowaNcq/7Ny5c76SJUv6atWqFeVjX331VffYDRs2+Jdt27bNly5dOt/AgQP9y86ePevLkyePr2XLlmEe37lzZ1/WrFl9x44d8yXl41y+fHmEZVOnTnXbmzBhQpjlxYoVi3CcCS0uxzpkyBD32CNHjkS5XlJ4TeN6rMEMHz7cbW/NmjVhlmtZr169fInt/PnzvoMHD7rvN27c6PZr8uTJMXrs7Nmz3fpz5szxLzt8+LAvV65cvo4dO4ZZt1y5cr7KlSv7/vvvP/+y5557zpcmTRr3O56Uj3PlypW+S5cuRVimbegYAtWvX99Xvnx5X2KKy7FqPa2vx0UnsV/TuB5rMNOnT3fbmDlzZpL6f1j/f1y4cCHMst9++82XKVMm939kSvk9jQ5l1EAK0qFDB1u8eHGk9+vKz2uvvZag+wQgYVy6dMll2ejK6fDhw92V48CblgFAeMoIU9aasvc8Ksd68MEHbe3atbZ3794oH1u9enV385QtW9YaN25sH330kX+ZMsz+/vtv69mzZ5jH9+rVy2WmfP7550n6OJU1Et6dd97pvm7bti3oY/799193bIkhLsfqUXzt1KlT7mswSeE1DdWxBpo1a5bLEKtdu3bQ+8+dO2fnz5+3xJIpU6YImXmxOVcFChSwtm3b+pepTFPlnZ9++qkrR5etW7e6m85p+vT/Vwip11rvB20nKR9nvXr1LG3atBGWqWw3st9XlZ2Gz3JNKHE51kD//POPGwsGkxRe01Aea+Dvq8qM1b4jKf0/rP8/MmbMGGaZMhJVVh3ZezA5/p5Gh2AjkIKotEP/MalXSfh07M6dO9uECRNs7NixibZ/AOLHpk2bXE+iO+64w5WwqV+L+m6FvwFAeD/88IMrLVO/xUA1atRwX1V2GIzGFj/99JPddNNNEe7TY3///Xf3x6/3HBJ+3RtvvNEFBbz7k+JxRsbrh6YSt/C+/vpr1ytOZcjqHfbmm29aQgrFseozJWfOnJY9e3ZXPv7XX39FeI7Efk1D/bpqWwoEdOrUKej9Ks9WYENl8+XKlXOBjuREx6cyzvCBOJ0rlb57/eQie21VWqyS44R6bUNJgUTdgv2+6rj1uuq9rkDYoEGD7L///rPkRO0s9Dug/3datWplO3bsCHN/SnxNjxw54nqzqoxYr19S+384PAUA9f9osPdgSv09pWcjkIKMHz/eXe3Q1R31cKhbt667oqN+Efp52rRpLugIIGXRVUxlW8yfP9/93ofv0wMAkdGkCddcc02E5d6yAwcOBH2ceqJpzBHdYzWZip5D2Wf58+cPs54yP9TXMbLnSArHGVVPLh3TXXfdFWa5+nOpT6COW5l/ClCpl6C2r8ckhLgc69VXX+0mNqhVq5bLQlIfRl2o3rBhg7uw5QX1ksJrGurXdebMme5rsLGyMpWUWaSsR21T50TrqV9pjx49LDnQuVKGX1TnqmLFiv6JVCI7rwn12oaS+nPqbyJN9BSoZMmSLlCn41YGnLLB1PNOAZ3Zs2dbUqdgmnqWesFG9fR7/fXX3ftV/WSLFCni1kuJr6leH2WkBvt9TQr/Dwf7/0UTqqnaKLX8nhJsBFKYyZMnu8G/GsvqA3PkyJGuQe2cOXMiTTEHkLwpu+ill15ymY0AEBu6UKGgUnje7MO6P7LHSUweq6/hS8oC143sOZLCcQajjDbNwvz0009HmLVYM4MGuv/++61FixYuANCnTx+XcZKUj/Wxxx4L87Mmf1FGjf6g1yQFzzzzTJJ5TUP5uipT98MPP3QTvmh27fA0lg6k2Y6VxamJkRTsCT9JUFIU03MV3e+2yuuTk2+++caGDRvmgsWNGjUKc59+jwPde++9rixV1WCPP/643XzzzZaU6Zh08yjLr1mzZi5YpXHhu+++myJfU+//YZUXa+Km8JLC/8OBtm/f7lpM6CJO165dLbX8nlJGDaQwSrnWlRP1S9J/qroSrb45BBqBlEuDpsj6agFAVBQk8XpABfL60kUWRPGWx+Sx+qqsomC0bkIEaq70OMNTpp/6AeoPev0xH500adK4oIUycK5kltnEPFaPyopVXvrVV1+FeY7Efk1DeawrV650WUcxrQBSoFUZoCdOnHDZZMlBTM9VdL/bySGwGhjkUX9Vzf77/vvvx+gx3gzGge/35EQZfTVr1ozw+5pSXlPRDOPqyapM1cB+hUnp/+HAlhtKAlJbCq/HbGr5PSWzEUjGdHUmMvqQUe/G5s2bu341Xs8a7z9bACnHgAED3ORPuhofvm8VAERF5VYKsoTnlWip/1MwmmxBGRXeelE9Vs+hiQsOHz4cpuxWwSqVuEX2HEnhOAP9+OOPrh+aAhf6ozEmf+SKV8qo0vOEEIpjDXYMgfufFF7TUB6rLtTrgn3Hjh1j/NwJ/bqG4lzF9PfVW+4dY+C6Xj/MpE6TAzVt2tQFeTSBnnoypsTXNbJj+PXXX/0/p5TX1OP1S41Ne7DEeF1Pnjzpkn90UUIXqmLy/1FK+j0l2AgkY/379492HQ2GA2ejItgIpDyahEENsEuVKuVmpdegI/yVU373AQRTpUoVN7OwSq4CL1asX7/ef38wCsyob5T6+IWnx2qCEe+Pe28bWve2227zr6efVb4a2XMkheP0aMIbXcBVYE2BC/2fG5ssHFHJX0KI67GGp8z53bt3uxLjwOdI7Nc0VMeqzKCPP/7YzToemyBpQr+ucaVzoYCHXp/AySd0rtT7TxPteOt5r2VgwEI94Pbt2xdm5u+kSgFvBRr12ir5Ilhfu5TyukZ2DIH7nxJe0/DBRvXbjE2Ze0K/rufPn3ftjdT/U1mmmlQq1f2e+gAkW7t3776iG4CUJU2aNNHe0qZNm9i7CSAJWrdunXow+EaNGuVfdv78eV+pUqV8NWvW9C/bs2ePb9u2bWEeO2LECPfYjRs3+pdt377dly5dOt+AAQP8y86ePevLnTu37/bbbw/z+C5duviyZMni+/vvv31J+TgPHjzou+6663yFChXy/fHHH5E+h47j4sWLYZb9+++/vltuucWXMWNGt52EEJdjPXz4cITtjR071m3v9ddfT1KvaVyP1TNv3jy3jYkTJwa9P9g5OXXqlK9kyZK+vHnz+i5cuOBLDPq9035Pnjw5wn0HDhxwx6v3n+fDDz9068+ZM8e/7MiRI75cuXL57rnnnjCPL1u2rK9y5cph3s/PP/+8G09s3brVl5SP8/Tp074aNWr4smfP7tu0aVOk2z158qR7rwS6fPmyOxd6vu+++86X0GJ7rMHem59//rnbRt++fZPsa3olx+r5/vvv3eMGDRqUZP8f1vO3atXKlz59evd6RCYl/Z4GQ2YjkIwVK1YssXcBQBLwxx9/JPYuAEim1Halffv2NnDgQFcSqwzpqVOnuky2wMkT7rvvPtfXLrA/bM+ePd1ECupHpWqLDBkyuBYvBQoU8Pc9E/WOGj58uGuQr+dSv0NlbsyYMcP1PVRJdlI+TmU0KitGE8KsXr3a3Tw6Vm+CAk1KoJlsNUO1Zi1WuZ4ycH755Rd7+eWXXd/DhBCXY9XYUn3QlLWqSQZ0rJo4RVk0jzzySJJ6TeN6rIEl1GoJoMlwgtHM0/Pnz3dZSkWLFnUlipMmTbI///zTpk+fHulEOfFlzJgxrizTm2124cKFLpNJNPmFyoZ1PnQeND4oXry4u0/vS2WCabKMrVu3Wt68ed2kPyqH1wQqgUaNGuVaBig7UBUTeg/rebt37x50Ap2kdJwqrVXPek3is23bNnfzKCNZk6iIZmtW2bxuet9owo1PPvnETQakrLBq1aolyHHG5Vg167Qyjm+66Sa3jo5J701VuGjyoqT2msblWGMya3xS+X9Yn38LFixw/2fo+fX/YqAuXbq4rynh9zRKiR3tBBA6u3bt8i1YsCDS+3VfVFfkAQBA6nPu3Dlf//79fQULFvRlypTJV716dd/ixYvDrFO/fn2XbRHe3r17fXfddZcvR44cvmzZsrlMtx07dgR9nvHjx/vKlCnjskuUFfbGG2+4TKKkfpz6ObKb1vcoi+qOO+7wXXvtte4YdT7q1Knj++ijj3wJ7UqPtXv37r5y5cq5rLAMGTK4DEFlqSqTLym+pnF9/yq7LXPmzL62bdtGuv0vv/zS16RJE7d9nRNlGDVt2tS3bNkyX2IoVqxYpO9Hb5zftWvXMD97jh075nvwwQd9efLkcRmoOi+BmcmBPvnkE1+VKlXcOS1cuLDLmAqWaZbUjjOqx+m+wL+b2rdv7ytevLh7D+h83Hjjjb533303wd/DV3qszz33nHuNcubM6d6bRYsW9fXo0cN36NChJPmaxvX9e+nSJff/a7Vq1SLdflL4f9j7/8YiuXlSwu9pVNLon8QOeAIIDV2RVc+apUuXBr1fV+Zz5crlrlADAAAAAACEGmXUQAqydu1a69evX6T3N27c2EaPHp2g+wQg/qlMRBPAREb3qRyucOHC1rBhQ1cKd/XVV/PSAAAAAAi5/5veBkCyd/z4cf/Mj8GoT4lmZwOQstSvX9/9fqtHlf4PUP8e3fS9luk+zYKnflbq4aNeXPR5BAAAABAfCDYCKYiaVqupcWTUuFuZTQBSFjU7379/v2t+/+OPP9rHH3/sbvp++fLl7r5u3brZDz/8YF9//bW7MKGm1AAAAAAQagQbgRREs6l98MEH9tZbb9nly5f9yzV71ZtvvmmzZ8+2Tp06Jeo+Agi9wYMHuxn86tatGzTrUbOFerMSNmjQwJVRf/XVV7wUAAAAAEKOno1ACqJMpdWrV7u+jS+99JKVKVPGLf/111/tyJEjLsjw3HPPJfZuAgixHTt2RNmDMXfu3G4dzw033GBnzpzhdQAAAAAQcmQ2AilIpkyZ7Msvv7SJEydajRo17OjRo+6m7ydNmuQymbQOgJTluuuus6lTp9q5c+ci3Hf27FmbPHmym0TGc+DAAcuXL18C7yUAAACA1CCNz+fzJfZOAACAKzdnzhzr0KGD68natWtXK1mypFu+c+dOmzZtmuvZqBYL7du3d20VypYtazfddJNbBgAAAAChRLARAIAU4PPPP3etFH755ZcwyytUqGAvv/yy3X777e7nixcvuuCjyq5z5MiRSHsLAAAAIKUi2AikMIcOHXJl1N9//72dPHkyzEQxkiZNGlu2bFmi7R+A+HXw4EHbs2eP+75YsWJ2zTXXcMoBAAAAJBh6NgIpyE8//WTlypWzF1980X7//Xdbvny5mxhGE0OsWLHC9u7da3ROAFI2BRdvvvlmdyPQCAAAAKQsp0+ftiFDhljz5s3dRJBKKJoyZUqct6s5Hho1amQ5c+a07Nmz24033mizZ8++om0xGzWQgjzzzDOWLVs227x5s2XJksXy589vb775pvsPQz3devToYTNnzkzs3QQQR+rDKPfee68bXHg/R+e+++7j3AMAAADJ2NGjR+2FF16wokWLWuXKlV1iUVxpQskHH3zQmjRp4lowpUuXzn799VeXsHQlKKMGUhBdgXj66aftueees2PHjlnevHnd7NS33nqru/+xxx5zgciVK1cm9q4CiIO0adO6IKNmn86YMaP7OTpaX5PDAAAAAEi+Lly4YMePH7eCBQvapk2brHr16i5Y2K1btyva3u7du12F5EMPPeSSlUKBzEYgBVF/xgIFCrjvc+XK5a5GKOjoqVixouvnCCB5++OPP9xXBRoDfwYAAACQsmXKlMkFGmPiiy++cJmKmtNBCQr16tWzkSNHWvny5f3rvPvuuy4pQdmSXpl21qxZXbLClaJnI5CClChRwh900H8k+ll9FzzffvutC0ICSN408Ytu4X+O7gYAAAAgdZg+fbq1bNnStVp79dVXbdCgQbZ161arU6eOy2b0KGZQtmxZW7RokRUuXNj1a8yTJ49bP/yEszFFZiOQgjRt2tT1ZnzppZfcz+rR+OSTT9quXbvcxDDq5aCfAQAAAABAynT69Gnr27evde/e3caPH+9f3rVrVytTpozLdvSWa0JZVUXef//9ri2b+kDOmzfPTTx78eJFe+WVV2L9/AQbgRREvRo7duxo//33n2XIkMH69etnZ86csY8//tj956ErE88++2xi7yaAeLBkyRLXJkEXF9TDJfzM8yqD0Cz1AAAAAFK2pUuX2okTJ1x8QBPKeBQXqFmzpi1fvjxMYFIZjCNGjLABAwa4Ze3atXMt2dTDUTEEZTvGBsFGIAW5+uqr3fT0gcGF559/3t0ApFyjRo1ys9GrZ2uNGjVcf1YAAAAAqdOOHTvc10aNGgW9P0eOHP7vr7rqKpekpMBkIP28ePFi++GHH1yvx9gg2AikIPqPRNmNjRs3Dnq/rl4MHz7cvv766wTfNwDxR1cc9fuvPivKagYAAACQel3+/3stqm9jsMlk0qf/v3BgoUKFXHDSm2zWkz9/fvdVVVOxRbARSEHUk1E9GSJz+PBhW7lyZYLuE4D4pwHAXXfdRaARAAAAgJUsWdIfMLz11lujPCOqjlSwcf/+/Xbdddf5lx84cMB9zZcvX6zPKLNRAylMVNPT79y5M9a9FgAkfSqd/vXXXxN7NwAAAAAkAc2aNXOl0poIRnM6hHfkyBH/9/fcc4/7qv7vgZmRkydPtty5c4dp1RZTZDYCydzUqVPdzaMZoyZMmBBhPTWH/emnn+y2225L4D0EEN/GjRtnLVq0sJtuusk6derECQcAAABSsDFjxri/8b3sw4ULF9q+ffvc93369LGcOXPaO++8Y/fee69Vq1bNOnTo4DIU//zzT/v888/tlltucduQ1q1bu1ZsmnVak8loNur58+fb6tWr7b333rNMmTLFev/S+MJPVwkgWdF/IAo0yJYtW6xw4cLuP5bw2Y5Zs2Z1VyQGDx7s770AIGWoVKmSmy3u4MGDli1bNvf/gGaaC///wI8//pho+wgAAAAgNIoXL2579uwJet8ff/zh7vdarWmW6XXr1tmFCxfs2muvtbp161rv3r3DZCxqRmpNLDt79mz3d0WZMmXczNSdO3e+ov0j2AikICVKlHATRbRq1SqxdwVAAmrQoEGULRQCJ4kCAAAAgPhEsBFIQb755hu74YYbIm3gqpTorVu3xnraegAAAAAAgJhgghggBWnYsKEtXbo00vuXLVvm1gEAAAAAAIgPBBuBFCS6Fqzq0RC+jxuAlOHUqVOuH4tmnqtatapt2LDBLVfPlddff93NRg8AAAAA8Y3ZqIFkTrNJ7d692//z9u3bXTl1eJqpSjNJFStWLIH3EEB808xz9evXt71791rp0qXd/wNq8iy5c+d2v/tqIK2ergAAAAAQnwg2Asnc5MmTbdiwYW5yCN1eeukldwuW9aisRgUdAKQsTz31lP3zzz+2efNmN9t8+Bnn27RpY5999lmi7R8AAACA1INgI5DM3X333VahQgUXTNT3ffv2dVPZB1IQMmvWrFalShUrUKBAou0rgPjx5Zdf2uOPP27lypWzv//+O8L91113nct6BAAAAID4RrARSOY0+7RuXpajSimLFy+e2LsFIAGdO3cu0lnoRVmPAAAAAJAQmCAGSEG6du1KoBFIhZTRGKxXq2f+/Plu0hgAAAAAiG9kNgIpzPnz5+3jjz+277//3k6ePGmXL1+OUFI9ceLERNs/AKHXr18/d7GhUqVK1r59e7dMv/uagVo9XdeuXev+XwAAAACA+JbGp0ZvAFIEzTbbsGFDNzt1rly5XLBRM9FqJupLly5Z3rx5LVu2bLZr167E3lUAIaaJoYYOHer6tyrQmDZtWve9vr744os2YMAAzjkAAACAeEewEUhBNEHMsmXL7IsvvnATQmhG2q+++spuueUWe+utt2zMmDHu59KlSyf2rgKIB3/++afNmzfPduzY4QKOJUuWtLZt27r/DwAAAAAgIdCzEUhBvv76a+vZs6fVqFHDZTOJMpsyZcpkTz31lDVu3NiVWwJImU6dOmX//vuv+71XywQFHM+ePZvYuwUAAAAgFaFnI5CCKKjgzUSdI0cOF2xQKbWnVq1a1r9//0TcQwDx4cKFC/bII4/Y9OnT/aXTomDjwIEDrXPnzvb+++9bxowZeQEAAAAAxCsyG4EUpGjRorZv3z73ffr06e3aa6+1devW+e/funWrZc6cORH3EEB8UD/GadOmWY8ePWzbtm1uoigFIPX9o48+ajNmzLCnn36akw8AAAAg3tGzEUhBVEKt4KJmopbBgwfbK6+8Yt26dXMZTsp6uu+++1yGE4CUQ5M/tWzZ0qZOnRr0/nvvvdf1cj169GiC7xsAAACA1IUyaiAFeeaZZ2zjxo0uo0l9Gp999lk7cOCAzZ0719KlS2edOnWy119/PbF3E0CI/ffff3bzzTdHen/t2rVt4cKFnHcAAAAA8Y7MRgAAUsBM9JoYZv78+UHvb926tbsA8dFHHyX4vgEAAABIXQg2AgCQzBw7dizMz0eOHHEBx5IlS1qvXr2sVKlSbvmOHTts7Nix9scff9js2bOtTJkyibTHAAAAAFILgo0AACQzmm1as80H0izUEtlyPebixYsJuJcAAAAAUiN6NgIAkMxo8qfwQUUAAAAASArIbAQAAAAAAAAQEmlDsxkAAAAAAAAAqR3BRgAAAAAAAAAhQbARAAAAAAAAQEgQbAQAAAAAAAAQEgQbAQAAAAAAAIQEwUYAAAAAAAAAIUGwEQAAAAAAAEBIEGwEAAAAAAAAEBIEGwEAAAAAAACEBMFGAEghli9fbmnSpLFPPvkkwn2zZs1y961duzZR9g0AAAAAkDqk8fl8vsTeCQBA3Om/82LFilmNGjVs7ty5Ye5r2bKl/frrr7Zz505ONQAAAAAg3pDZCAAphDIXu3TpYp999pmdPHnSv/zIkSP25ZdfuvsAAAAAAIhPBBsBIAW577777MKFC2EyG2fPnm0XL14k2AgAAAAAiHeUUQNACqMy6mzZstnXX3/tfq5Vq5b7Sr9GAAAAAEB8I7MRAFJgduPKlStt37599vvvv9u6devIagQAAAAAJAgyGwEghTl69KgVKlTIXnrpJTt37py9+OKLduDAAcubN29i7xoAAAAAIIUj2AgAKVDr1q1t9+7ddv78eStTpowtWLAgsXcJAAAAAJAKUEYNACm0lPqnn36y3377jRJqAAAAAECCIbMRAFKgf//91woWLGiXL1+2Q4cOWebMmRN7lwAAAAAAqUD6xN4BAEDopU2b1tKnT2933HEHgUYAAAAAQIKhjBoAUqD58+fbkSNHXDk1AAAAAAAJhTJqAEhB1q9f73o1Dh8+3M0+/f333yf2LgEAAAAAUhEyGwEgBXnnnXesR48elj9/fps2bVpi7w4AAAAAIJUhsxEAAAAAAABASJDZCAAAAAAAACAkCDYCAAAAAAAACAmCjQAAAAAAAABCgmAjAAAAAAAAgJAg2AgAAAAAAAAgJAg2AgAAAAAAAAgJgo0AAAAAAAAAQoJgIwAAAAAAAICQINgIAAAAAAAAICQINgIAAAAAAAAICYKNAAAAAAAAACwU/j/WTDXcbpFKXgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1320x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- EDA 1: class balance and the attack-family mix ---\n",
    "fig, ax = plt.subplots(1, 2, figsize=(11, 4))\n",
    "df['y'].map({0:NEG_WORD,1:POS_WORD}).value_counts().plot.bar(               # counts per class\n",
    "    ax=ax[0], color=['#2a9d8f','#e76f51']); ax[0].set_yscale('log')\n",
    "ax[0].set_title(f'Class balance ({NEG_WORD} vs {POS_WORD})'); ax[0].set_ylabel('records (log)')\n",
    "df.loc[df.y==1,'family'].value_counts().head(8).plot.barh(                  # top attack families\n",
    "    ax=ax[1], color='#e76f51'); ax[1].invert_yaxis(); ax[1].set_title('Top attack families')\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ba8fa890",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- EDA 2: feature correlation + a 2-D PCA projection ---\n",
    "from sklearn.preprocessing import StandardScaler                 # scale before PCA\n",
    "from sklearn.decomposition import PCA\n",
    "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n",
    "topv = X[feat].var().sort_values().tail(12).index                # 12 highest-variance features\n",
    "im = ax[0].imshow(X[topv].corr(), cmap='coolwarm', vmin=-1, vmax=1)  # correlation heatmap\n",
    "ax[0].set_xticks(range(len(topv))); ax[0].set_xticklabels(topv, rotation=90, fontsize=7)\n",
    "ax[0].set_yticks(range(len(topv))); ax[0].set_yticklabels(topv, fontsize=7)\n",
    "ax[0].set_title('Feature correlation (top-variance)'); fig.colorbar(im, ax=ax[0], shrink=0.7)\n",
    "samp = X.sample(min(5000, len(X)), random_state=RANDOM_STATE)     # subsample for a fast PCA\n",
    "pc = PCA(n_components=2).fit_transform(StandardScaler().fit_transform(samp))\n",
    "ys = y[samp.index]                                               # aligned labels for coloring\n",
    "for lab,c in [(0,'#2a9d8f'),(1,'#e76f51')]:\n",
    "    ax[1].scatter(pc[ys==lab,0], pc[ys==lab,1], s=4, alpha=0.4, color=c,\n",
    "                  label={0:NEG_WORD,1:POS_WORD}[lab])\n",
    "ax[1].set_title('PCA projection (2 components)'); ax[1].legend(); ax[1].set_xlabel('PC1'); ax[1].set_ylabel('PC2')\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b85e0c54",
   "metadata": {},
   "source": [
    "## 8. Model comparison\n",
    "\n",
    "Four diverse learners share one held-out split, ranked by ROC-AUC.\n",
    "\n",
    "**Two honesty guards print with the table:**\n",
    "\n",
    "1. The models train on a *stratified subsample* of at most 120,000 rows. The full row count is printed above. So every score here is a subsample number, not a full-corpus claim.\n",
    "2. The **majority-class baseline accuracy** appears *inside* the ranking table. On imbalanced data, 0.99 accuracy can be worse than always guessing the majority class. Judge each model against that baseline, not against 0.5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6d4d9307",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "materialized 4,847,499 rows | trained on 120,000 (stratified subsample) | held-out 1,211,875\n",
      "MAJORITY-CLASS BASELINE accuracy = 0.5619  (any model must beat THIS, not 0.5, to be interesting)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best model: LightGBM\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>roc_auc</th>\n",
       "      <th>train_s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LightGBM</td>\n",
       "      <td>0.991056</td>\n",
       "      <td>0.996751</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.991390</td>\n",
       "      <td>0.996673</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>0.990370</td>\n",
       "      <td>0.993853</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.901802</td>\n",
       "      <td>0.940597</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MajorityBaseline</td>\n",
       "      <td>0.561900</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                model  accuracy   roc_auc  train_s\n",
       "0            LightGBM  0.991056  0.996751      1.5\n",
       "1             XGBoost  0.991390  0.996673      0.9\n",
       "2        RandomForest  0.990370  0.993853      2.5\n",
       "3  LogisticRegression  0.901802  0.940597      1.3\n",
       "4    MajorityBaseline  0.561900  0.500000      0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --- Model comparison: four learners on the same held-out split ---\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score\n",
    "import xgboost as xgb, lightgbm as lgb\n",
    "\n",
    "# Stratified split keeps the class ratio in both halves.\n",
    "Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.25, random_state=RANDOM_STATE, stratify=y)\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "N_MATERIALIZED = len(y)                                          # the full corpus we loaded (see printed count)\n",
    "# HONEST DISCLOSURE: we do NOT train on all N. We fit on a STRATIFIED subsample (<=120k) because\n",
    "# these learners saturate long before then on this data. Every headline below is a SUBSAMPLE\n",
    "# number, not a full-corpus number \u2014 saying otherwise would be the fabrication this course forbids.\n",
    "if len(Xtr) > 120_000:\n",
    "    Xtr, _, ytr, _ = train_test_split(Xtr, ytr, train_size=120_000, random_state=RANDOM_STATE,\n",
    "                                      stratify=ytr)               # genuinely stratified, not random\n",
    "MAJORITY_BASELINE = max(np.mean(yte), 1 - np.mean(yte))          # accuracy of 'always predict majority'\n",
    "print(f'materialized {N_MATERIALIZED:,} rows | trained on {len(Xtr):,} (stratified subsample) | '\n",
    "      f'held-out {len(yte):,}')\n",
    "print(f'MAJORITY-CLASS BASELINE accuracy = {MAJORITY_BASELINE:.4f}  '\n",
    "      f'(any model must beat THIS, not 0.5, to be interesting)')\n",
    "\n",
    "models = {                                                        # four standard, diverse learners\n",
    "    'LogisticRegression': make_pipeline(StandardScaler(), LogisticRegression(max_iter=300)),  # scaled!\n",
    "    'RandomForest': RandomForestClassifier(n_estimators=60, n_jobs=-1, random_state=RANDOM_STATE),\n",
    "    'XGBoost': xgb.XGBClassifier(n_estimators=80, max_depth=6, tree_method='hist', n_jobs=-1,\n",
    "                                 eval_metric='logloss', random_state=RANDOM_STATE),\n",
    "    'LightGBM': lgb.LGBMClassifier(n_estimators=80, n_jobs=-1, verbose=-1, random_state=RANDOM_STATE),\n",
    "}\n",
    "rows, fitted = [], {}\n",
    "for name, m in models.items():                                    # fit + score each model\n",
    "    t = time.perf_counter(); m.fit(Xtr, ytr); fitted[name] = m\n",
    "    p = m.predict_proba(Xte)[:, 1]                                # positive-class probability on held-out\n",
    "    rows.append({'model': name, 'accuracy': round(accuracy_score(yte, (p>0.5).astype(int)), 6),\n",
    "                 'roc_auc': round(roc_auc_score(yte, p), 6),      # 6 dp: a 1.000000 is a red flag, not a win\n",
    "                 'train_s': round(time.perf_counter()-t, 1)})\n",
    "rows.append({'model': 'MajorityBaseline', 'accuracy': round(MAJORITY_BASELINE, 4),\n",
    "             'roc_auc': 0.5, 'train_s': 0.0})            # show the baseline IN the ranking table\n",
    "comparison = pd.DataFrame(rows).sort_values('roc_auc', ascending=False).reset_index(drop=True)\n",
    "_ranked = comparison[comparison.model != 'MajorityBaseline']\n",
    "best_name = _ranked.iloc[0]['model']; best = fitted[best_name]  # winner by ROC-AUC (excl. baseline)\n",
    "print('best model:', best_name); comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88cc8a9b",
   "metadata": {},
   "source": [
    "## 9. Results\n",
    "\n",
    "Diagnostics for the winning model, including **per-group recall**.\n",
    "\n",
    "The grouping comes from whatever the loader put in `family`. It is *not* always an attack taxonomy. On the intrusion corpora it is the attack family. On the fraud and malware corpora it is a transaction type, a merchant category or a malware category. On binary corpora it collapses to the positive class.\n",
    "\n",
    "Read it accordingly. Where the groups are genuinely rare classes, they reveal whether detection is real. The dominant flood classes do not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "21a6971e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1560x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "operational FALSE-POSITIVE RATE @0.5 = 0.0183  (9,723 benign flagged of 530,967)\n",
      "worst per-family recalls: {'mitm': 0.602, 'ddos': 0.833, 'ransomware': 0.844, 'dos': 0.9, 'backdoor': 0.995, 'scanning': 0.998}\n"
     ]
    }
   ],
   "source": [
    "# --- Results for the best model: confusion, ROC, PR, importances, per-family recall ---\n",
    "from sklearn.metrics import confusion_matrix, roc_curve, precision_recall_curve, recall_score\n",
    "pb = best.predict_proba(Xte)[:, 1]; pred = (pb > 0.5).astype(int)\n",
    "fig, ax = plt.subplots(1, 3, figsize=(15, 4))\n",
    "# (1) confusion matrix\n",
    "cm = confusion_matrix(yte, pred); ax[0].imshow(cm, cmap='Blues')\n",
    "ax[0].set_title(f'{best_name}: confusion'); ax[0].set_xticks([0,1]); ax[0].set_yticks([0,1])\n",
    "ax[0].set_xticklabels([NEG_WORD,POS_WORD]); ax[0].set_yticklabels([NEG_WORD,POS_WORD])\n",
    "for (i,j),v in np.ndenumerate(cm): ax[0].text(j,i,f'{v:,}',ha='center',va='center')\n",
    "# (2) ROC and PR curves\n",
    "fpr,tpr,_ = roc_curve(yte, pb); prec,rec,_ = precision_recall_curve(yte, pb)\n",
    "ax[1].plot(fpr,tpr,color='#264653'); ax[1].plot([0,1],[0,1],'--',c='grey')\n",
    "ax[1].set_title(f'ROC (AUC={roc_auc_score(yte,pb):.4f})'); ax[1].set_xlabel('FPR'); ax[1].set_ylabel('TPR')\n",
    "ax[2].plot(rec,prec,color='#e76f51'); ax[2].set_title('Precision-Recall'); ax[2].set_xlabel('recall'); ax[2].set_ylabel('precision')\n",
    "plt.tight_layout(); plt.show()\n",
    "\n",
    "# (3) feature importances + (4) per-attack-family recall\n",
    "fig, ax = plt.subplots(1, 2, figsize=(13, 5))\n",
    "imp, names = None, feat                                           # importances, robust to the scaled-LR pipeline\n",
    "if hasattr(best, 'feature_importances_'):                          # tree models\n",
    "    imp = best.feature_importances_; names = list(getattr(best, 'feature_names_in_', feat))[:len(imp)]\n",
    "elif hasattr(best, 'named_steps') and 'logisticregression' in getattr(best, 'named_steps', {}):\n",
    "    imp = np.abs(best.named_steps['logisticregression'].coef_[0]); names = feat  # LR pipeline\n",
    "elif hasattr(best, 'coef_'):\n",
    "    imp = np.abs(best.coef_[0]); names = feat\n",
    "if imp is not None:\n",
    "    pd.Series(imp, index=names[:len(imp)]).sort_values().tail(12).plot.barh(ax=ax[0], color='#264653')\n",
    "ax[0].set_title(f'{best_name}: top importances / |coef|')\n",
    "# Per-family recall, WORST-first so rare, hard classes are visible, not just the dominant floods.\n",
    "fam_te = df.loc[Xte.index, 'family']\n",
    "fr = {}\n",
    "for fam, cnt in fam_te[yte==1].value_counts().items():\n",
    "    if cnt < 5: continue                                          # need a few positives for a meaningful recall\n",
    "    mask = (fam_te==fam).to_numpy(); fr[fam] = recall_score(yte[mask], pred[mask], zero_division=0)\n",
    "srt = pd.Series(fr).sort_values()\n",
    "show = pd.concat([srt.head(9), srt.tail(3)]) if len(srt) > 12 else srt   # worst 9 + best 3\n",
    "show = show[~show.index.duplicated()]\n",
    "show.plot.barh(ax=ax[1], color=['#e76f51' if v < 0.5 else '#2a9d8f' for v in show]); ax[1].set_xlim(0,1)\n",
    "ax[1].set_title('Per-family recall (worst first; red < 0.5)')\n",
    "plt.tight_layout(); plt.show()\n",
    "# Operational numbers, not just figures: false-positive rate and the worst per-family recalls.\n",
    "tn, fp = int(cm[0,0]), int(cm[0,1])\n",
    "fpr_op = fp/(fp+tn) if (fp+tn) > 0 else float('nan')             # benign wrongly flagged @0.5\n",
    "print(f'operational FALSE-POSITIVE RATE @0.5 = {fpr_op:.4f}  ({fp:,} benign flagged of {fp+tn:,})')\n",
    "print('worst per-family recalls:', {k: round(v, 3) for k, v in srt.head(6).items()})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49f3da00",
   "metadata": {},
   "source": [
    "## 10. Validity audit \u2014 is the score real?\n",
    "\n",
    "Three diagnostics. **(a)** How well can the *single best feature*, alone, separate the classes? A near-1.0 single-feature AUC means that feature is *near-sufficient* \u2014 a shortcut (which may be legitimate signal or an artifact), not the same as target leakage. **(b)** The exact-duplicate row rate. **(c)** The **train/test exact-row contamination** \u2014 the fraction of held-out rows that are duplicates of training rows, which is what actually inflates a held-out score. The trust grade is the *worse* of the single-feature and contamination concerns.\n",
    "\n",
    "**What these numbers are measured on \u2014 read this before quoting them:** All three diagnostics run on `X`, the matrix *after* preprocessing, never on the raw parquet. Because of the \u00b11e15 clip described in \u00a73, rows that differed only in the out-of-range `Idle` fields arrive at this cell already identical. So **(b)** and **(c)** are **upper bounds** on duplication and split leakage in the source capture. Some part of each is an artifact of our own pipeline, not of CIC-ToN-IoT. This notebook does not measure the pre-clip rates. So it quotes no split between manufactured and native duplication. Reading the printed value as a property of the dataset would be wrong. The same clip constrains **(a)** in two ways. A column the clip drives to a single value is dropped before this cell runs, and cannot be ranked at all. A column it only partially flattens *is* still ranked \u2014 but on degraded values. So \u201cno single feature dominates\u201d is a claim about the post-clip feature set, not about everything CICFlowMeter measured."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2b1c9efa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best single-feature AUC = 0.8132  (feature: Bwd_IAT_Min)\n",
      "   note: a near-1.0 single-feature AUC means this feature is *near-sufficient* (a shortcut),\n",
      "   which may be legitimate signal OR an artifact \u2014 it is NOT the same as target leakage.\n",
      "exact-duplicate row rate (whole corpus) = 0.307\n",
      "TRAIN/TEST exact-row contamination       = 0.114  (single-feat grade A, contam grade B)\n",
      "==> data trust grade: B   (worse of the two; F = shortcut and/or heavy contamination)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 960x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- Validity audit: is the score real detection, or a data shortcut? ---\n",
    "from sklearn.metrics import roc_auc_score\n",
    "samp = X.sample(min(60_000, len(X)), random_state=1); ysamp = y[samp.index]\n",
    "aucs = {}\n",
    "for c in feat:                                                    # AUC of EACH feature alone\n",
    "    col = samp[c].to_numpy(float)\n",
    "    if col.std()==0: continue\n",
    "    a = roc_auc_score(ysamp, col); aucs[c] = max(a, 1-a)          # direction-agnostic\n",
    "best_auc = max(aucs.values()); best_col = max(aucs, key=aucs.get)\n",
    "dup_rate = 1 - X.drop_duplicates().shape[0]/len(X)               # exact-duplicate feature rows (whole set)\n",
    "# The statistic that actually inflates a held-out score is TRAIN/TEST CONTAMINATION: how many test\n",
    "# rows are exact duplicates of a training row. Measure it directly on the split used above.\n",
    "_trkeys = set(map(tuple, np.round(Xtr.to_numpy(), 6)))\n",
    "_te = np.round(Xte.to_numpy(), 6)[:50_000]\n",
    "contam = float(np.mean([tuple(r) in _trkeys for r in _te]))      # fraction of test rows seen in train\n",
    "# Trust grade reflects BOTH failure modes and takes the WORSE of the two: a near-perfect single\n",
    "# feature (shortcut) OR heavy train/test contamination each independently invalidate the headline.\n",
    "_ga = 'F' if best_auc>=0.999 else 'D' if best_auc>=0.99 else 'C' if best_auc>=0.95 else 'B' if best_auc>=0.85 else 'A'\n",
    "_gc = 'F' if contam>=0.5 else 'D' if contam>=0.3 else 'C' if contam>=0.15 else 'B' if contam>=0.05 else 'A'\n",
    "grade = max(_ga, _gc)                                            # 'max' letter = worse grade (A best, F worst)\n",
    "print(f'best single-feature AUC = {best_auc:.4f}  (feature: {best_col})')\n",
    "print(f'   note: a near-1.0 single-feature AUC means this feature is *near-sufficient* (a shortcut),\\n'\n",
    "      f'   which may be legitimate signal OR an artifact \u2014 it is NOT the same as target leakage.')\n",
    "print(f'exact-duplicate row rate (whole corpus) = {dup_rate:.3f}')\n",
    "print(f'TRAIN/TEST exact-row contamination       = {contam:.3f}  (single-feat grade {_ga}, contam grade {_gc})')\n",
    "print(f'==> data trust grade: {grade}   (worse of the two; F = shortcut and/or heavy contamination)')\n",
    "s = pd.Series(aucs).sort_values().tail(15)\n",
    "fig, ax = plt.subplots(figsize=(8,5))\n",
    "s.plot.barh(ax=ax, color=['#e76f51' if v>=0.99 else '#457b9d' for v in s]); ax.axvline(0.5,ls='--',c='grey')\n",
    "ax.set_xlim(0.5,1.0); ax.set_title('Single-feature ROC-AUC (red = near-perfect shortcut)'); ax.set_xlabel('AUC alone')\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e05794c",
   "metadata": {},
   "source": [
    "## 11. Ablation \u2014 does the headline survive removing the artifacts?\n",
    "\n",
    "Narrating a shortcut is not enough. We *retrain the winning model* after (1) de-duplicating the corpus (removing the train/test contamination) and (2) dropping the single strongest feature. We report the held-out AUC each time. **Read the result honestly, both ways:** if the AUC **collapses**, the headline was a contamination/shortcut artifact. If it **barely moves** \u2014 common on *simulated* corpora \u2014 that is **not vindication**. It means the classes are separable by *many* redundant features, because the attack and benign distributions barely overlap. That is its own generation artifact. The numbers below decide which story is true here, not the prose.\n",
    "\n",
    "**Caveat carried down from \u00a73:** the de-duplication variant drops every row that is a duplicate *after* clipping, which includes rows the clip itself made identical. It therefore removes more than the capture's own duplication, and the \u201crows removed\u201d percentage in the table should be read as an upper bound too. That makes the de-duplicated AUC a *conservative* check \u2014 it strips out more data than a clean pipeline would."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1172dc2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ablation \u2014 how much of the headline survives once each artifact is removed:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>setting</th>\n",
       "      <th>held_out_auc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>headline (as-is)</td>\n",
       "      <td>0.996751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>de-duplicated (31% rows removed)</td>\n",
       "      <td>0.995080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>shortcut feature dropped (Bwd_IAT_Min)</td>\n",
       "      <td>0.996740</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  setting  held_out_auc\n",
       "0                        headline (as-is)      0.996751\n",
       "1        de-duplicated (31% rows removed)      0.995080\n",
       "2  shortcut feature dropped (Bwd_IAT_Min)      0.996740"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --- Ablation: SHOW the inflation empirically, don't just narrate it ---\n",
    "from sklearn.base import clone\n",
    "def _retrain_auc(Xa, ya):                                        # re-split, stratified-subsample, refit best family\n",
    "    xtr, xte, ytr2, yte2 = train_test_split(Xa, ya, test_size=0.25, random_state=RANDOM_STATE, stratify=ya)\n",
    "    if len(xtr) > 120_000:\n",
    "        xtr, _, ytr2, _ = train_test_split(xtr, ytr2, train_size=120_000, random_state=RANDOM_STATE, stratify=ytr2)\n",
    "    m = clone(best); m.fit(xtr, ytr2)\n",
    "    return roc_auc_score(yte2, m.predict_proba(xte)[:, 1])\n",
    "base_auc = roc_auc_score(yte, best.predict_proba(Xte)[:, 1])     # (0) the headline held-out AUC\n",
    "Xdd = X.drop_duplicates(); ydd = y[Xdd.index]                    # (1) de-duplicated corpus\n",
    "auc_dedup = _retrain_auc(Xdd, ydd)\n",
    "auc_noshort = _retrain_auc(X.drop(columns=[best_col]), y) if best_col in X.columns else base_auc  # (2) drop shortcut\n",
    "ablation = pd.DataFrame([\n",
    "    {'setting': 'headline (as-is)',              'held_out_auc': round(base_auc, 6)},\n",
    "    {'setting': f'de-duplicated ({1-len(Xdd)/len(X):.0%} rows removed)', 'held_out_auc': round(auc_dedup, 6)},\n",
    "    {'setting': f'shortcut feature dropped ({best_col})', 'held_out_auc': round(auc_noshort, 6)},\n",
    "])\n",
    "print('Ablation \u2014 how much of the headline survives once each artifact is removed:')\n",
    "ablation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d032d858",
   "metadata": {},
   "source": [
    "## 12. Reproducibility & robustness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e4306c25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seed=0 | numpy 2.3.5 | sklearn 1.9.0 | xgboost 1.6.2 | lightgbm 4.7.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LightGBM 3-fold CV ROC-AUC = 0.9962 +/- 0.0002  (mean +/- std across 3 stratified folds; a small std means a stable estimate on this split)\n"
     ]
    }
   ],
   "source": [
    "# --- Reproducibility & robustness ---\n",
    "import sklearn\n",
    "from sklearn.model_selection import StratifiedKFold, cross_val_score\n",
    "print(f'seed={RANDOM_STATE} | numpy {np.__version__} | sklearn {sklearn.__version__} | '\n",
    "      f'xgboost {xgb.__version__} | lightgbm {lgb.__version__}')\n",
    "# 3-fold cross-validated ROC-AUC of the winning model (fresh clone, bounded subsample) -> mean +/- std.\n",
    "from sklearn.base import clone\n",
    "cvX, cvy = Xtr.iloc[:40_000], ytr[:40_000]\n",
    "def _auc_scorer(est, Xv, yv):                                   # robust to xgboost's 2-col predict_proba\n",
    "    p = est.predict_proba(Xv)\n",
    "    p = p[:, 1] if getattr(p, 'ndim', 1) == 2 else p\n",
    "    return roc_auc_score(yv, p)\n",
    "try:\n",
    "    cv = cross_val_score(clone(best), cvX, cvy,\n",
    "                         cv=StratifiedKFold(3, shuffle=True, random_state=RANDOM_STATE),\n",
    "                         scoring=_auc_scorer, error_score='raise')\n",
    "    assert np.all(np.isfinite(cv)), 'non-finite CV folds'   # FAIL CLOSED: never narrate a NaN as evidence\n",
    "    print(f'{best_name} 3-fold CV ROC-AUC = {cv.mean():.4f} +/- {cv.std():.4f}  '\n",
    "          f'(mean +/- std across 3 stratified folds; a small std means a stable estimate on this split)')\n",
    "except Exception as e:\n",
    "    print(f'CV UNAVAILABLE ({type(e).__name__}: {str(e)[:60]}); rely on the single held-out AUC above \u2014 '\n",
    "          f'we do NOT report a CV number we could not compute')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb542e9c",
   "metadata": {},
   "source": [
    "## 13. Scientific conclusion\n",
    "\n",
    "The four learners reach high AUC on the **1,211,875**-row held-out split drawn from **4,847,499** near-balanced **network flows** (no telemetry view is used). The audit below reports the strongest single feature and the duplicate/overlap rates. The ablation decides whether either actually accounts for the score. Per-family recall shows which of the many ToN_IoT attack types are genuinely separated versus carried by easy majority classes.\n",
    "\n",
    "**Validity ledger \u2014 read the headline against these printed numbers:** Majority-class baseline **accuracy**: **0.5619**. The accuracy column must clear that bar to mean anything. For ROC-AUC the trivial baseline is 0.5, not that figure. Winning learner: **LightGBM** (3-fold CV ROC-AUC **0.9962**). Strongest *single* feature **among the 68 that survive preprocessing**: `Bwd_IAT_Min` at AUC **0.8132**. The ablation refutes a single-feature story. Dropping that feature barely moves the AUC: **0.996751 \u2192 0.996740**. So the separability is **multi-feature**. That reflects how this corpus was generated, not one leaky column. De-duplication lowers the AUC only slightly, to **0.995080**. Repeated rows account for a negligible part of the headline. That verdict is *conservative*, because the de-duplication removes more rows than the capture's own duplication warrants (see the clip caveat below). Data-trust grade: **B**. It is the worse of two independent sub-checks. Single-feature AUC 0.8132 scores **A**. Train/test exact-row overlap 0.114 scores **B**. The overlap check drives the grade, not the single-feature check. That says the split leaks *on the matrix this pipeline built*, not that features are clean; the single-feature check separately scores A. Operational false-positive rate at threshold 0.5: **0.0183**. Worst per-group recalls, exactly as printed: {`mitm`: 0.602, `ddos`: 0.833, `ransomware`: 0.844, `dos`: 0.9, `backdoor`: 0.995, `scanning`: 0.998}. The weakest group sits at **0.602**, which is where detection is thinnest. **Disclosed limitation:** categorical columns are integer-encoded before the split. The encoder therefore sees the test set's category values. On an all-numeric corpus that step is a no-op. The mapping never consults the label, so no *label* information leaks. It is still transductive. A deployed system would need an unseen-category bucket. **How the audit numbers are computed:** overlap is measured on the first 50,000 held-out rows, so read it as a sampled estimate. Each ablation re-splits and refits, so tiny differences are re-split noise. The de-duplication variant keeps the first label when a feature vector appears twice. **All of it is computed on the preprocessed matrix `X`, never on the raw parquet, and that matters here.** The loader ends with `X.clip(-1e15, 1e15)`, which maps every out-of-range value onto one number. CIC-ToN-IoT's `Idle Mean` / `Idle Std` / `Idle Max` / `Idle Min` fields sit partly or wholly above that bound. That is a property of the source file, not something a cell prints. So `Idle Max` is flattened to a constant and dropped, and rows differing only in those fields reach the audit already identical. The exact-duplicate rate **0.307**, the **31%** of rows the de-duplication drops, and the **0.114** train/test overlap are therefore **upper bounds** that include duplication the pipeline manufactured. Grade **B** grades *this pipeline's* split, not CIC-ToN-IoT itself. No decomposition into manufactured versus native duplication is quoted, because no cell here measures the pre-clip rate. **Known defect, disclosed rather than papered over:** a flat \u00b11e15 clip is the wrong guard for a corpus with fields on that scale. Per-column scaling or a log transform would preserve them. Re-running \u00a710 with the clip removed is the exercise. The code above is left exactly as it ran, so every printed number remains the number this pipeline actually produced. **Scope:** the split is random, not temporal or entity-grouped. Every number above therefore measures in-distribution separability only."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9db76404",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "1. Moustafa, N. (2021). A new distributed architecture for evaluating AI-based security systems at the edge: Network TON_IoT datasets. *Sustainable Cities and Society*, 72, 102994.\n",
    "2. Booij, T.M., Chiscop, I., Meeuwissen, E., Moustafa, N. & den Hartog, F.T.H. (2022). ToN_IoT: The Role of Heterogeneity and the Need for Standardization of Features and Attack Types in IoT Network Intrusion Data Sets. *IEEE Internet of Things Journal*, 9(1), 485\u2013496. \n",
    "3. Sommer, R. & Paxson, V. (2010). Outside the Closed World: On Using Machine Learning for Network Intrusion Detection. *IEEE S&P*."
   ]
  }
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