{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cd33497a",
   "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 Edge-IIoTset, after excluding endpoint and payload columns that act as label proxies.\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",
    "5. Read the loader's defence against a label proxy made by text formatting alone. The same numeric value can be serialised as `0`, `0.0` or `0x00000000`. If a capture spells it one way per class, label-encoding turns formatting into a class-tracking category. The loader coerces numeric-looking strings to numbers *before* any encoding, so the spellings collapse first. The defence is **applied** here, not demonstrated: no cell runs the pipeline without it. Untested hypothesis you can check: delete the `pd.to_numeric` coercion loop in the loader cell and rerun. Then compare section 10's best single-feature AUC.\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**. Several features used here are protocol fields an attacker controls, which is the same concern.\n",
    "- **Chapter 9: Supply-Chain Integrity and Counterfeit Detection** \u2014 Learning objective 3 (section 9.1) is to build **multi-signal** counterfeit detection (MAC OUI, firmware hash, TCP stack fingerprint, entropy, timing) rather than trusting one indicator, and the chapter's guardrails add that a single anomaly rarely settles a case and that a documentation gap is not evidence of tampering. Both cautions apply to artifact-derived features here.\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": "6ab99040",
   "metadata": {},
   "source": [
    "# IoT/IIoT Intrusion Detection: the Edge-IIoTset Dataset\n",
    "### Model comparison + validity audit on Edge-IIoTset (2.2M records, via Kaggle)\n",
    "\n",
    "**Abstract:** Edge-IIoTset (Ferrag et al., 2022) is a large IoT/IIoT security dataset (~2M+ records, 14 attack types across MQTT/Modbus/HTTP). We compare four learners and audit the near-perfect scores."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02853cb3",
   "metadata": {},
   "source": [
    "## 1. Research problem\n",
    "\n",
    "**Task:** Detect attacks (DoS/DDoS, injection, MITM, reconnaissance, malware) against IoT/IIoT devices from flow/protocol features. IIoT devices are constrained and long-lived, so a detector must generalize \u2014 exactly what an in-distribution score cannot show."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e7e1f35",
   "metadata": {},
   "source": [
    "## 2. Literature review\n",
    "\n",
    "- **Ferrag et al. (2022)** \u2014 Edge-IIoTset: realistic IoT/IIoT cyber-security dataset for centralized and federated learning.\n",
    "- **Sommer & Paxson (2010)** \u2014 the closed-world critique.\n",
    "- **Apruzzese et al. (2023)** \u2014 *The Role of Machine Learning in Cybersecurity* (ACM DTRAP): a broad survey of where ML is (and is not) deployed in practice. We cite it for that framing, not as a study of dataset shortcuts.\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",
    "| Ferrag et al. (2022) \u2014 DNN / ML baselines | in-distribution; protocol fields can leak |\n",
    "| Federated-learning studies | non-IID splits, optimistic |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ee5602e",
   "metadata": {},
   "source": [
    "## 3. Dataset provenance & honesty caveats\n",
    "\n",
    "| Property | Value |\n",
    "|---|---|\n",
    "| Source | Kaggle `sibasispradhan/edge-iiotset-dataset` |\n",
    "| Rows | ~2M+ (DNN-EdgeIIoT-dataset.csv) |\n",
    "| Label | `Attack_label` 0/1; family `Attack_type` |\n",
    "| Access | Kaggle API token required |\n",
    "\n",
    "**Honestly:** `Attack_type` is dropped (it encodes the label). We also explicitly drop the **endpoint-identity and payload columns** \u2014 `ip.src_host`, `ip.dst_host`, the ARP IPv4 fields, and the HTTP/DNS/MQTT string payloads. In a testbed capture the attacker and victim addresses are fixed per role. So keeping those columns lets the model read *who* is talking instead of *what* the traffic does. The generic high-cardinality ID filter does not catch them, since testbed IPs take only a handful of values.\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 `sibasispradhan/edge-iiotset-dataset` -> `/tmp/kg_edge-iiotset-dataset`. It is about **1 GB** 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": "8426e50f",
   "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": "a1faa2d5",
   "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": "da4165f6",
   "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": "66b575ad",
   "metadata": {},
   "source": [
    "**Figure 5.1 \u2014 Implementation architecture.**\n",
    "\n",
    "<img 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0nMpUaK8dOzYWzw9H/z1u3v3dunRo7W1zXzrnk6Xdev+lIkTv7fu9atSo0Zxadq0jbz77gcyZEgP6/x3yE8//eHYd2BgoNSvX0mqV39b2rXrYXrZ6FCkzZvXyfnz/5l71aPHEEmWLLnjPTduXJcpU0bLtm3/SoIECaVOnXcEAAAAzkOlDAC4mD17dpiGtokTJ7G+tA+WXLnymXAhPLt2bTNf9N955wN5661GJlzp27e9nDlzUlq1+kjee+9D2bdvp/Tr10FsNluY965a9Zvcu3dXPv98qglzNJyZOnWcPA0NdooVKy3t2/eUTp36mMDls8/6mDAoND0HDV10u2rVapvmtXPnznC8fu3aVescOptAqU2brlKmTGWZPHmU/PPPn/IsInsPhwzpLuvXr5batRtLo0YtTOgSXvPeTz/tIz4+PtK160DJkCHzI6+XKVPJBCoHD+51rPPz22TdD39zLWrWrClWaDPLhGgakl27dkX69GkX5nMZOXKAbNr0jzmXZs3am5817AIAAIBzUCkDAC5mwYLZkixZChk0aIyjkiQoKEhmz576yLYaYkyY8K0JHdSPP35rvsRPnjxHkidPadZphUivXm3l33//ltKlKzremy5dBiusGW6OkS5deilfvqr89ddyadu2u3h5eUlkaEVM6KqY+PETWOfdTc6ePRUmvEidOp21frSpQNEA4/LlC/Lbbz9ZYVIrc3wNTDRQ0muxv08Dnvnzv5dy5V4L99jasDcwMCDMOq0uiew93Llzmxw4sMeESTVr1jfr9D2ff/6xFYi0c+xLpUyZWrp0GSAR0fuq16ZVMDlz5jXrtPpGq5Py5y9swplffpknFSpUNSGRKliwiBWa1bTes15KlCgrx44dlm3bNoY5n5Ily0vDhq8JAABwiv8CAwODBC6NShkAcDG7d/uZL/Khh/YkTJgo3G0zZ87mCGSUfqnXUMMeyCgdvqS0eiQ0DSBCHyNr1uxy9+4dOXfujERWQECAzJw5WVq2rCPVqxczgYzS/YSm52MfEqTy5CloKkt0GI/auXOLpEqVJkyQo+GGVp5omBIerTypV69SmMUuMvdQj6mKFi3tWKf3Uq/pyJEDYbatWrWWPE68ePHF17eEbNq0zrFOK3A0gHJzc7PCn90mmClSpJSEviepU6e1rnGPea5VT6pgwaKObbQ6J04cHwEAAE6RzvpFFYUSLo7/AADAxdy6ddP6kp8gUtsmSZLsoffeMNUqobm7u5uqD61OeRz7+zQsiazhw/uZAKN16y5W4FDSVJ7069fxie+zByRXr16WV17JYKpkLl48L9WqFX1k2ytXLpnw4mHabyZ05U9okbmHekzVrNlbj7ym5xJa0qTJ5UlKl64k48cPk+vXr8nNm9fNeZcqVdG8dvv2LfM4duxQs4R3LPs2kf3sAQAA8PIRygCAi9EKlnv37siz0GE29uoTO+3vogFEokRJHvtee++SyAQQ6syZU7Jhwxpp3ry9lC9fRZ6GPYCwhzN63jocqXPnfo9sG9H5pE37ilnCE5l7mCJFKvM4dOh4U5ESWvr0meVpaUD0xRefycaNa831afWMNk9Wen1K71WePAUeOVelzY3V3bu3JXnyFAIAAADnI5QBABejw3a0v0hoOktSZOTOXUC2bv3XNM61f8nXoTzaTFaH14QWHBx2WJDOHqSNcdOkSSeRcf36VfOo/WLsjh49GO62D5+/npMGMvYZkfS8tcdL9ux5rDAjnjyvyNzDfPkKm0edbSr0kKFnpfdOZ3nSXjIacJUoUc4xfCpTpmymWkl74ER0rBw5HvSiOXr0kGMYl87YFNHwLQAAALx89JQBABdTu3YjOXXquCxePM/0Idm3b5dpXBu59zY2zWX79+8of/65TH755UcZMaK/mfGnVKkKYbbV6Z5nzJhohSFbZdmyRWZ6a52COXTvl9B0eJO3t7c5n//+O2OCA60wWbFisezYscWcrzbmVQ/3rzl8eL9MnTre9LyZNGmkmVWpXr2mZmiV/Zrjxo0rw4b1MvvS3ixDh/aU7777yrEPHaqlw5K0Me7DM0k9yz3MnTu/mTlq3LhPTMWPHleno47M8KuIlC1b2VyjTkceemiV3qeGDZuZ+6P3Wu/5/Pmz5cMPG5nhTkobJufLV0h+/nmWnD59wlQO6bkFBNwXAAAAOAehDAC4mMKFi0vHjr1NiFC7dlkzY1DLlp0i9V6tMhk3bqYkTJjYTCn95ZefS8aMWeWTTyaYhrOhaQCgMxzpdM/ff/+VFcg0kcaNW0S4bw1QNEjRsEenjdbKEB36o/sYNKir/PHHEitUmSitWnWS/ft3hXlvxYrVzIxOOuXzb7/9LNWr15ZGjZqHOu/45rwDAwNlyJAeMn78JyZ4sU8nrapUedNU1+jU2YcPH5AXcQ8HDBglefMWkokTh8vgwd1Nfxz7zEfPomzZ10yTY73XxYuXDfOaXq9OXa6hi17Dn38uNdODh+4B1L//SEmQIJG0a6efxeum8iZLllcFAAAAzuEmgItatKjcyUqVhmZMnDiTwDUd9rPJQT8fKVUncsNpEHm9e7czjyNHThEgJji597ZcPHZJqjcXAC7G3/+aLFvW6Ua9ehuSCBCFfH19l1sPE/z8/FYIXBaVMgAAAAAAAE5AKAMAAAAAAOAEzL4EAHjhWrfuIgAAAAAej1AGAPDCvfpqTgEAAADweIQyAAAAAABEMZvNdinIInBphDIAAAAAAEQxNze3lF5eXnwnd3E0+gUAAAAAAHACQhkAAAAAAAAnIJQBAAAAAABwAkIZAAAAAAAAJyCUAQBECydPHpMRIwbIhQvnBE926NA+Wb16hQQHBwsAAABiJkIZAEC0cPr0CRMy3L/vL3iyfft2mRDr/v37AgAAgJiJUAYAAAAAAMAJCGUAAAAAAACcwFMAAHgBbDab/PDDN7J+/Wo5c+aEZMmSXd5+u4lUrPi6eV3XL1kyX/bv3y0JEyaSChVel5YtO4q7e8S/H5gyZbT8/fdKmTdvpWPd4MHd5dKlCzJ58mzzfPfu7dKjR2sZN+4bmTp1nBw7dkjSpk1vrRsiO3dulV9//VHu3r0jtWo1kGbN2jn2M2fOdPnnnz/knXc+kLlzp8u5c2ekQIGi0qlTH0mVKo3ZJiAgQMaMGSL79u2UGzeuSYYMmaV8+arSoMH7jz1vu7Vr/5BVq5bIyZNH5ebNG5I3b0Fp3ryDZM+ey7FN797tzH4TJUpstr1z57ZUqlRdPvywm3h7ezu209f0/p04cVQKFSomGTNmEQAAAMRsVMoAAF6IefNmynfffWUFG77Ss+dQExocOLDbvLZnzw755JNe4uMTV7p1+1iqVq0pixbNlenTJ8iLMnhwN3nrrYYyYcIsuXbtigwf3teELkOGjJPGjVtawcsM2bRpXZj3aB8bPefWrbvI2LHfmPBk0qSRjtcXLJgtGzasltq1G0mfPp9a11bEer4m0s11NRwqVqy0tG/f04Q9/v735LPP+khISEiY7X7//RfTuPeTTyZIly4DZNmyhVaY9JPjdb1/o0cPlsSJk1hh02DJlSufCWgAAAAQs1EpAwB4bkFBQfLzz99J9epvmwoPVbZsZcfrP/wwQ9KnzySDBo02z8uXryJubm7y44/fSqNGLUzY8Lz0uJUr1zA/ayXJtm0bTUCjVTkajmgApFU0JUqUDXPeI0Z8KSlSpDLPS5YsbwU5fzpeP3LkgCRNmlzq13/PPC9duqI8Da2ICV0VEz9+AusedJOzZ0+Z6hi71KnTWaHSWPH09JTMmbOZyp3Dh/c7XtdwKFmyFNZ7x4iHh4fj3GfPnioAAACIuaiUAQA8t717d5phN1pJ8jCtKvHz22SCktB028DAQDOc6UVIlSqt4+d48RJYS3wTyCgfHx+JEyeOqVQJTYcg2QMZ5e0dJ8zsTxrS6BTdI0cONCHPwxUuT6LDn2bOnCwtW9axAqtiJpBROpwqtOTJU5pAxi5OHJ8w57F7t5/kz1/YEcgo+7UBAICYyfoF1W3r3xaRK79FrEWlDADgud26dcM8hlfxogGEhhlaJRJaggQPQoXLly9IdFWlypsmVNqwYY0MHdpDEiVKIq1afeTok/Mkw4f3M9U2OjyqSJGScuDAHunXr6M8rVu3bpqgCQAAxB42my2B9QsiD4FLI5QBADw3e7WJhgcP04oOrVS5fftWmPW3bz/YVoOO6EqHWFWvXtssd+/ela+/HmOClixZXpVMmbI+9r1nzpwyYU7z5u3NcK3noUOX7t27IwAAAIhdGL4EAHhumTO/KgkSJDTDbMKTN28h2bNne5h1u3ZtM0N2ChYsGuF+dTiR9k4J7erVy+IM8eLFk1q1Gpqfjx49+MTtr1+/ah61X4xdZN4Xnpw588qxY4fDrAsKChQAAADEbFTKAACem1bCNGzYzPRP0X4oefIUkI0b15q+Ljrz0Lvvtpbu3T+QoUN7mma82nBXm/zqrEb2IU/aUFdt3rzeWpfUrM+aNbupvtFZknSdzth04sQRyZDh5U8HrVN89+3bwVSpFC5cXFKmTC0LF86VuHHjWddXUO7duyeDBnU1DYw/+qjvI+/XRr56X1asWGztI7mZynr+/O/NazqbkgYtkfVg9qf2snjxPNNMWe+fNv8FAABAzEalDADghWjUqLm8/35bWb/+Lxk1aqCcP39WypSpZF7Lm7egme753LkzMmJEf/nhh29MuKD9Wew0yNHtpk0bL6tWLTHrKlasJvXqNZWuXVtKkybVTH+aunXflaigQ5d0+ukkSZKamaWGDettmgWPHTtD0qRJJzduXJOdO7fKypW/hvt+DZWGDh1vmgtrePPHH0usfUy0rrmToRlD0wAAEABJREFU7N+/S56GhkIdO/Y2QUzt2mXNrEstW3YSAAAAxGxuArioRYvKnaxUaWjGxIkzCVzTYT+bHPTzkVJ10gnwLFq3biCpU6e1wpYvBDHbyb235eKxS1K9uQBwMf7+12TZsk436tXbEH2bnCFW8vX1XW49TPDz81shcFlUygAA8Ay0akeHVb3xRl0BAAAAngU9ZQAAeAZ79+6U5MlTSqlSFQQAAAB4FoQyAAA8g2LFSsucOcsEAAAAeFYMXwIAAAAAAHACQhkAAAAAAAAnYPgSAAAAAABR75i1BAhcGqEMAAAAAABRL6u1eAtcGsOXAAAAAAAAnIBQBgAAAAAAwAkIZQDAxS1dukCuXLkkT+vw4QPmvUFBQQIAAADg6RHKAIALO3furHzxxXC5evWKPK2pU8ea9+7du1MQNTZsWCOjRw8WAAAAxA40+gWAGKBJk2pSpEgp6dFjsHl+6tRxad26gfk5Tpw4kiJFKnn11Vzy3nttJUOGTJHe786dWyVhwkSSLVuOx2538eJ58faOI0mSJHWse//9dnLgwG7Jm7egONvt27fMkiZNOonN9PPaunWDAAAAIHagUgYAYrAGDd6XQYPGSP3675thRB9+2NBUU0TW7t1+ki9fYXF3j/ivgzNnTllhT03Zvn1TmPX58xc2x/f0dH6+36ZNQ5k3b6YAAAAAMQmhDADEYOnTZ5IiRUrKG2/UkY8//lwKFiwqX3zxmdy9ezdS79+1a5sVrvgKAAAAgKjH8CUAiEXefruJFc50kU2b1kqlStUfu+2VK5fNsKTcufNHuM3YsUPl999/NT+PGDHALMOGfSHFipWWOXOmy+zZU2X58s2O7WvUKC5duw6ULVvWW8sGMzSqadM2kipVGvn667Fy7twZKVGinHTu3F/ix0/geJ/2pfnxx5lWSOQnSZMmk7p135VatRo4Xt+4ca3MnTtDTp48Zt6XPXtuadasnRw9etDRY2X58kVmadSoubRs2VGOHTss8+Z9I6dPn5CzZ09JxoxZpX7996Rixdcd+9Vr+OefP+S99z6UGTMmWvfkonU/CkifPp+a8928eZ0kTpzE2l8nKVu2cpjr1GPs2LFF9u3bJT4+caV69drmnB7nyJGD1jGnmQolfU+hQsWkTZuukihRYrHZbPLTT7Nk/frV1nUete5ZWnnnnVZP/BzbtWsiGTJkln79hjvW1a1b0ZyP7vt5rrN373Zm33p+q1YtkTt3bpvz+fDDbuLt7f3YzyZr1uwCAACAx6NSBgBiEXvVy/nz/z1x2/37d5kv1tmy5YxwGx2e1LPnEPOzBgSjRn0lefMWeux+J00aYSp4pkz5wYQx06aNt9aNlObN28vw4V+a4VX6Jd7u2rWrMnBgZzl+/IgJEcqUqSyTJ4+yQoQ/zet3794x4VCCBAmle/dB5jzu3LllAhlf35LmnDRQ0LBHf65Zs755X/LkKSVHjrxWSNPChA+ZM2ezXh8o//13Jsz5amjz7bdfmn49XboMlO3bN0u3bi3Fw8PDOo85JmQYOXKA3LhxPcz7Zs2aIrly5bdCjoXSpElLc01//70qwvuiPW/69etggql27XqYe3vs2CFzfUoDmZkzJ5vhZD17DpWcOfOaEOzff/+WF+FZr/P333+RQ4f2ySefTLDeN0CWLVsov/76k3ntcZ8NAAAAnoxKGQCIReLFiydubm5WKHP2idtqtUauXPlMo+CIaJWEm9uD/D5jxixmeNSTVK5cw1ExolUVEyeOsEIXHVpVxKzLkSOPFcAcdmy/ZMl8U4ExYcK35njK3/+ezJ//vZQr95pcuHDOBAW6r/Llq5jX7cGLSp48hXh6ekmyZCnCnJ8GNfXrN3U8L1y4hKn20N446dKld6zXXjzDh082zZLz5CkgX3012vo5tQkZ9F6+/vpbsmbNSjlz5qTZp13VqrXkvffamJ/fequhCSpWrvxVKlSoGu590enD9To0AEmZMrVZV7t2I/MYEBBgeuK8+WY9K5jqYtZpxYpeu1bWlCpVQZ7Xs15n6tTpZPDgsaZ3kAZbc+dOl8OH95vXnvTZAAAA4PEIZQAgFtEhMNq0V79kP4n2k3kRX/YfljJlGsfP9iFKWjFjp1UVWjVit3PnFvO6PZBRWiWiYY0GCZkyZZVXXslgKlP0fRrUaLAQGRrCLFo011SJaPCh7JUpdnq/Qu8vQYJEJjSx30M9X6VBUWgPn0OWLK+aoCsi27ZtNNdoD2RC279/tzmvh0OvAgV8ZfbsaXLv3j2JGzeuPI9nvU6tOArdzDlOHB+5f9/f/Pw8nw0AAAAYvgQAscrVq5clODhY0qZN/9jttLpBhwtFpvLlZdMqGe1tU61aUceifWI0YLpy5ZIJE4YNmyhlylQyQ2l0Jqjhw/vL9evXHrvfxYvnmf288UZd+eabRbJ06UZ5mTSA0qFYEbl164bpsRPRa/Z9hKbBibp8+YJER8/62QAAAOABKmUAIBbZuvVf8/ikUEYrOry8vExPFGfTao379+9L5879HnktadLk5lGHG2kfFrVnzw4ZMqS7TJnyufTt+1mE+9UeLTozlX04jVbdvEy3bt00VSUR0QqSiHr92KtnQlcQPXh+0zwmSpREoqtn+WwAAADwAKEMAMQSGjosXvyD6a1StGjpx24bmX4ydvahK1qB8zLoLEA7d26T7NnzmJ44T5IvXyHTDPfw4QMS+hxtthDHc62yuX79qqROXc6x7siRA/IiBQcHhfo52NxTPa+I5MlT0Axh0uqfh8ObTJmymSqZPXu2h+lJo7NRvfpqzjA9Xh7m7R0nTOCkwU5AwH1xhvA+GwAAAESMUAYAYjBtyqqz6Jw4cdTMiqPPBw0a88Rw42n6yWiFh06JrFMfa0WHBhC+viXkRdFmt7/8Mk+GDeslDRs2M1UzOhRGm8q+/35bK7DZKl98MVyqVHnTCihymbBFp9zWprh2WbPmMKGITlF98+YN03RWZxPScy5Zsry17rpppKvTUOsU1iEhIWbozfPQ4VEagGmfGJ3GWoeE6VTeT7rOAQM+MlNza2XNihWLTRPdNGnSmam8dXYk7dmi05Rv2LDGfE6DB49x7CNJkmTmfZs3rzfTkms/mCxZsouf30Zz365duyJffjnqua8tsiLz2QAAACBihDIAEIP9/PN3smDBbBOc6NTWAweOMrMkPY69n0zbtt0lMrQKpX//ETJlymjp1aut6UPzIkOZePHiy7hxM83UykOG9LCCEx8zrEqnxlYFChSRpk3byJo1v8uPP35rAozmzTtInTpNHPto376ntY9PpG/fDia40GoNnQb7q6/GyPDh/SRp0mRmamwdDjVr1pcSGBgYqSqhx9EZh3S66mnTJljhTHIz1bRWijzuOseMmWGd51ATZGjQpb1Y9FFpKKM0qNGZp3T7zp37hwnPNPzQYEenEJ848XvJkSO3tGjRwfpMr1lBTyXx8vI2n+vDzYxflsh8NgAAAIjYk6fnAGKpRYvKnaxUaWjGxIkzCVzTYT+bHPTzkVJ10okrWbfuLxk5coD1xX/1cwcTrqpGjeImjHj33Q8EscPJvbfl4rFLUr25AHAx/v7XZNmyTjfq1dsQfRt4IVby9fVdbj1M8PPzWyFwWVTKAICL0R4ktWo1JJABAAAAnIxQBgBcTPHiZcwCAAAAwLkIZQAAeEojRnwpqVO71rA3AAAAvHiEMgAAPCVtdgwAAAA8r6iZMxMAAAAAAABhEMoAAAAAAAA4AcOXAAAAAACIYiEhIcesJUDg0ghlAAAAAACIYu7u7lmtxVvg0hi+BAAAAAAA4ASEMgAAAAAAAE5AKAMAiBJnz56WxYvnCQAAAIAHCGUAAFFizZrf5euvxwperJCQEDl9+oQEBgYKAAAAYhZCGQAAYrAVK36RDz6oLzdv3hAAAADELIQyAAAAAAAATsCU2ACAp3b9+jVp1KiqdO7cX954o45ZN3LkQPnrr+UyduwMyZu3oFnXqlU9KVGirLRp09Xx3hMnjsrnn38sZ86clAIFikqnTn0kVao0jtf37t0pP/44U3bt8pOkSZNJ3brvSq1aDRyvz5kzXf755w/p0mWAzJgxUY4fPyzz5/8lwcHB8u23X8rmzevk/Pn/zDn06DFEkiVLHuF19O7dTtKmTS9BQYGyfftmuXPnthQtWkp69hwqceLEMdvs3r3d2k9rmT59vjn2unV/ysSJ30vGjFkee7yAgAAZM2aI7Nu3U27cuCYZMmSW8uWrSoMG7+sUmGablSt/kxUrFsuRIwfM6+3a9ZR8+QqFOT9dnyhRYlm1aok5v0qVqsuHH3YTb29vadHibfnvvzNm23feqW4ef/11vePcAQAAEL1RKQMAeGpJkiSVdOnSy9GjBx3rDh/eL3HjxjMBg7p3754JXnLkyOvYRvufTJo0Qt58s74JVU6cOGI9H+l4/dq1qzJwYGcraDligpwyZSrL5MmjrBDmzzDHv3r1snz6aR8ToHTvPsismzVrivz00yzJnj23CYuuXbsiffq0E5vN9thrWbXqN+tc71pB0VTr2KNMODN16rhHttPj+fj4SNeuA01Q8qTjLVgwWzZsWC21azey1n9qBVBFrOdrTHiktmzZYEIbNzc3614MlNSp00m/fh3k4sXzYY77+++/yKFD++STTyaYe7Zs2UIrePnJvNar1ydSr15T83P//iNk9OhpBDIAAAAxCJUyAIBnkjNnPjl27JD52d/f3zSbfeONuo5Qxh7Y5MqVL8z7Pvqon6kyUVoVs379asdrS5bMN9UgEyZ8a4KPB/u+J/Pnfy/lyr3m2O7GjevSqlUnadiwmeP4v/wyTypUqCo9egw26woWLCLvvVdTNm9eb6p1IpIuXQYrDBkuHh4eJmjSahat+Gnbtrt4eXk5tkuZMrUJRSJ7PL0PSZMml/r13zOvly5dMcxxf/75O1NVM2bMdPO8UqVq1jEbm8Dlgw8+cmynYc3gwWPF09NTMmfOJnPnTjcBmMqdO78JsFTevIUkefIUAgAAgJiDShkAwDPRsMUeyhw8uEdeeSWD5M/v6whl9DFx4iSSJk06x3t02I49kFE+PnHl/n1/x/OdO7eYoUz2QEblzJnX2v9eCQoKCnP8atVqO34+cGC3CUqKFCnlWJc8eUor0Ehrzu1xkiVLYQIZu6xZs8vdu3fk3LkzYbarWrXWUx2vZMnycuHCOTOsa9u2jaZKyE6vZdeubWHerxUzeq2679B0vxrI2MWJ4xPmngEAACDmolIGAPBMtErDXiFz4MAeefXVXGYoj1ZuaOigoUyuXPmfap9aJaPDd6pVK/rIa1euXDKhh9JwRwMfu9u3b5nHsWOHmiW0h4cDPUn8+AnMo1bjhKZVL09zvCpV3jRDlTZsWCNDh/aQRImSSKtWH0nFiq+b4VI6zEn7xOgSWugQCwAAxF7WvwXuWEuwwKURygAAnkm2bDlNBcfRo4fMcBoNYDJkyGR6mujQJV3Kln3tqUQ4Jr8AABAASURBVPapQ4Tu378vnTv3e+S10KFIeO9TzZu3lzx5CoR5TSthnoZ9aunnPZ5WvlSvXtssd+/ela+/HiPDh/eTLFlelUyZspr+NMWLl5WaNeuHeb+3Nz1hAABwBda/FeJbi4fApRHKAACeiQYy9iFMOryoVq2GZn2OHHlk//7dpmKmdesuT7NLyZ27gOzcuU2yZ88j8eLFi/T7MmXKJgkSJJTAwAApWLCoPI3g4LDDovbu3fHIsKvnPZ5ei96fFSt+MWGVhjLaA0arf572fB9mH3oVEsIv2gAAAGIaQhkAwDPTZr9+fpvMkB3th6K0guaPP5aa4Tn2dZGlMxVpA91hw3qZJr5aNaOzD2mD2/ffbxvh+7TqRLefM2eapEiR2vS3OXz4gJlZaeTIr8xsURHRKa91am2dyens2dOydu0fpgImdB+Xpz2ehjp9+3YwVTOFCxc3lTULF841s1PlyfNguvB3320t3bq1kqlTx5vGwBrQ6IxNOuvU0wQ1WbPmMI86vbYOH9NjaTUOAAAAoj9CGQDAM9PQRYMEbcyrQYXSUGbRoh/MOnt/lsiKFy++jBs30/RpGTKkh9mnDovSqbGfpFGj5iYI+vnnWSbgeOWVjKYZ8JPOIV++QmaGJ53yWqtO6tRpIo0bt3iu4+nQJZ2VaeHCOWaWJZ3CW8OZsWNnOCpw8uYtKJ99NkmmTRsvv/32k2noq41/M2TIIk8je/ZcZqaoH3+caaYUb9asHaEMAABADOEmgItatKjcyUqVhmZMnDiTwDUd9rPJQT8fKVWHxqquqnfvduZx5MgpAtd2cu9tuXjsklRvLgBcjL//NVm2rNONevU2JBEgCvn6+i63Hib4+fmtELgspsQGAAAAAABwAkIZAAAAAAAAJ6CnDADAZT3t7FAAAADAi0QoAwBwWa++mlMAAAAAZ2H4EgAAAAAAgBMQygAAAAAAADgBoQwAAAAAAIAT0FMGAAAAAIAoZrPZzlgPgQKXRigDAAAAAEAUc3NzS289eAlcGsOXAACPOHBgj/Tq1VYQu0ydOl5+//1XAQAAQPRAKAMACCMoKEgmTPhUihcvK67o/v37Mn78MNm4ca28SBcunJNRoz6W48ePiLMUKVJSZsz4Qq5fvyYAAABwPkIZAEAYP/zwjQkm6tRpIrHN7du35Pz5/x67TUDAfVm+fLFcvnxRXqSLF8/Ln38uM+fgLBrK5M5dQCZPHikAAABwPkIZAIDDnTu3Zf7876Revabi4eEhsU2bNg1l3ryZ4soaNmwma9f+IUeOHBQAAAA4F6EMAMBh69Z/xd/fX4oWLSWIGfbt2yWzZ0+L9PZ58xaUePHiy/r1fwkAAACci9mXAAAO+/btlMSJk0jq1GnDrO/du52kTZtegoICZfv2zaaiRoObnj2HSpw4ccw2u3dvlx49Wsv06fNlzpzpsm7dnzJx4veSJcursnLlb7JixWI5cuSAZMiQWdq16yn58hWSo0cPSfv270jHjr2lVq0GjuNpyDBnzjSZN2+lOZ+9e3fKjz/OlF27/CRp0mRSt+67YbbX4/3zzx/yzjsfyNy50+XcuTNSoEBR6dSpj6RKlUZWrVoio0cPNtsuX77ILI0aNZeWLTtKZEV0DXZz586QHTu2WNd0ULy9vaVIkVLSqtVH5nzDc+vWTXPtuq9PP51oes3Mm/eNnD59Qs6ePSUZM2aV+vXfk4oVX3e859KlC+a+bNiwRm7cuC7x4yeQbNlyWCFLgkifp8qVK5+5pwAAAHAuKmUAAA4aDLzySsZwX1u16je5d++ufP75VBk4cJQJZ6ZOHffIdp9+2kd8fHyka9eBJhTYsmWDjBkzRKd9lC5dBlqBTzrp16+D6bGigYKGJlu2rA+zD32eP7+vCWSuXbtqHa+zObc2bbpKmTKVZfLkUVYI82eY92iY8d13X0nr1l1k7Nhv5OTJozJp0oPeKb6+JWXUqK/M/kqUKGd+rlmzvkTW467BLnPmbFaAUs0KsIZJ06ZtxM9vk8ycOSnc/dlsNhk+vJ/5uW/fz8x+kydPKTly5LXCohbSp8+nZn+jRg2U//47Y7bTEOajj943zydNmi2ffTZJ4saNJ+XKVZEhQ8ZG+jyVrj9xwnkNhwEAAPAAlTIAAIebN69LypRpwn0tXboM1hf84abXTLp06aV8+ary11/LpW3b7uLl5eXYLmXK1FYgMMDx/Oefv5NkyZJbYcF087xSpWrWexrLr7/+JB988JFUqPC6LFo0VwIDA81+dGYgnZK7ffueZvslS+abypwJE741IY/y978n8+d/bwUSrzmOo7NGjRjxpaRIkco8L1myvCO4SZ48hVk8Pb2sc0khBQsWlafxpGtQpUtXDPOes2dPy+rVKxzPNSixP86ePVV27dpmXdMsSZgwkVmvgVH9+k0d2xcuXMJU+Gzfvsnc73Xr/pKrV69Y1zjFBFm6lC9fRWbNmmKqhnS/kTlPlShRYuuzviEAAABwLiplAAAOGmx4eoaf12uYEbr5b9as2eXu3TtmqFBoVavWCrM/DR90KI+dhgc5c+a1gpfd5nnZspXNdtu2/Wue26tmdL3auXOLCSDsgYzS9x88uNe8z87d3d0RyChv7zhy/76/PK/IXIO6cuWSFZgMkCZNqku1akVlwYLZ1v25/cj+/Pw2muFZnTv3N5VCoWkIo0OaatUqLW+/Xc6s03usbLYQ86jVMXY6bElfDw4OjvR5Kg2/tFpHgzAAAAA4D5UyAAAH/cIf2SBD+5koHVYTWtKkyR0/63An/fKvYYMuoaVJk848an8Tre7YvHm9qW7RUEbX6XAepVUyOvxGg46HaRDycP+bFy0y16DBSNeuLU3fnT59hknevIXk+++/lsWLf3hkf9pIWWmz3dAWL54nU6aMNn1wdIiV3sc33yzpeL148bLm8/nxx2/NNjqsa+XKX822GqRpj5onnWfoa9K+N6ErnAAAABD1CGUAAA765V17t0SGffhL6BDmYTo0R/vLaKDwcA8XrWSx014sf/+9Sjp06GVmgGrcuIXjNR0Odf/+fencud8j+3/csV+UyFzDmjUr5cKFc6afjM5u9Dg6zOny5QsyduxQyZOnoKMR8E8/zZIiRUo6jhG6CkhptZDeH21YrEO6lB6rU6e+kT5PO20YnCrVyw2zAADAEx2zlgCBSyOUAQA45MyZT9avX22qOfQLfmjBwWFDgr17d5g+KA9XYTxMq0a0ouVxfVy0ee/ChXPNrEhaGaP9auxy5y4gO3duk+zZ80i8ePHkeWhFiX0Y0NN40jVcu3bFPKZJ84pjnc5+FJEOHXpLmzYN5PPPPzYNe7XC5fr1q5I6dbnHvl9777z33ofStGlreZbztDtx4qgZ1gQAAJwqq7V4C1waPWUAAA5lylQy/Um0uezDdMrrGTMmWgHJVlm2bJGsXfuH1KnzToQ9aOzefbe1mX556tTx5r3aHLhDh3fNz3Za8aE9a3T2JJ1CO3TQU7t2I4kbN64MG9bLTDm9adM6GTq0p9n2aWXNmsO6Dj+zHz3/8OiwLB3as2/fLsfMR0+6huzZc5vHn3+eZYZh6TCkPXt2mHDr0KH95jUNXuyPGmZ16zZItm3bKEuXLjC9X3QfGzeuNdenw48+/3yQFYzFNecREvIgSLp8+aLpG6Pb6LFPnz7peC0y56m0oufUqeOmSTAAAACci0oZAIBD2rSvmKFE2t+kVKkKYV7Ll6+QmfVIp7zWhr916jQJM8woIhq4aDXItGnj5bfffjK9YrQZbYYMWRzbaCihw3p0WM7DQ2+098q4cTPNcJ8hQ3qYCp5cufKb6pqnpTM6jRv3ifTt20GSJElmXVNh088mNG0YXK9eU/nhh29M1Y5ON/2kayhevIx07NjbVLKsWPGL5M9fWKZPny/ffvulCbhy5Mj9yLmUKFHW3GudVrxo0dJmGuyvvhpjpsrWIU06NbYOz5o160vTkDdOnDjSsmUn+eKLz8KELBpijRr1tZlRKTL3euHCOeY92r8HAAAAzuUmgItatKjcyUqVhmZMnDiTwDUd9rPJQT8fKVUnneB/zp07K61a1ZWBA0c5gpnevduZx5EjpwiiB+05o1OH9+vXQWrUqCPt2vV44nu0subDDxtawdpEKVy4uOB/Tu69LRePXZLqzQWAi/H3vybLlnW6Ua/ehiQCRCFfX9/l1sMEPz+/FQKXxfAlAEAYWi3ToMH7ZqgSoo/x44fJ0qULHc912JhWL2kfG+1HExnff/+VqZAhkAEAAIgeGL4EAHjEO+988MjwJTiXp6eXzJ//naRIkcrRhFmHMZ08eUyaNGkZqX188EFnM1QMAAAA0QOhDADgEdq/JFeufI7nrVt3EThXq1Yfye3bt0zD44CAANMYOVu2HDJkyDgpWbJcpPah02oDAAAg+iCUAQA80auv5hQ4l85A1afPMAEAAEDsQU8ZAAAAAAAAJyCUAQAAAAAAcAJCGQAAAAAAACegpwwAAAAAAFEsJCTkmLUECFwaoQwAAAAAAFHM3d09q7V4C1waw5cAAAAAAACcgFAGAAAAAADACQhlAAAAAAAAnIBQBgAAAAAAwAkIZQAAAAAAAJyAUAYAAAAAAMAJCGUAAAAAAACcgFAGAAAAAADACQhlAAAAAAAAnIBQBgAAAAAAwAk8BQAAAAAARLXTQUFBAQKXRigDwKVdvxQou9deFQCu7calAInjLQAARKUMnp6e/O3j4ghlALisZGlFchQOtn66LnBdR46clFOnzknlyiUFritexgf/TxBxEwAAgKhCKAPAZSVP62YtAhd3Zflp2XfeT4pXLyUAAABAVKLRLwAAAAAAgBMQygAAAAAAADgBw5cAAC7Nw8NdEiSIJwAAAEBUI5QBALg0my1EgoKCBAAAAIhqhDIAAJcWEiLi7x8gAAAAQFQjlAEAuDibuLvTYg0AAABRj1AGAODi3CREy2UAAACAKEYoAwAAAABAFLPZbJeCaGzn8ghlAAAuzcvLU1KkSCoAAABRyc3NLaWX/kMELo3/AAAALi0wMEguX74mAAAAQFQjlAEAuDR3dzexfkslAAAAQFQjlAEAuLSQEJsEBgYKAAAAENWYAxQAAAAAAMAJqJQBALg07a+XPHkSAQAAAKIaoQwAwKVpo98rV64LAAAAENUYvgQAAAAAAOAEVMoAAFyat7enpEqVTAAAAICoRigDAHBpAQFBcvHiVQEAAACiGsOXAAAAAAAAnIBKGQCAS/P29pIUKZIKAAAAENUIZQAALi0gIFAuX74mAAAAQFRj+BIAAAAAAIATUCkDAHBpXl6ekjw5w5cAAAAQ9QhlAAAuLTAwSK5cYfgSAAAAoh7DlwAAAAAAAJyAShkAgEvz8HCXBAniCQAAABDVCGUAAC4tODhEbt++KwAAAEBUI5QBALg0d3cRHx9vAQAAAKIaoQwAwKWFhIj4+wcIAAAAENUIZQAAAAAAiGI2m+1SkEXg0ghlAAAuzdtwg8bWAAAQAElEQVTbS1KmTCYAAABRyc3NLaWXlxffyV0c/wEAAFxaQECgXLp0VQAAAICoRigDAHBxNjMtNgAAABDVCGUAAC7OzUyLDQAAAEQ1fjUIAAAAAADgBFTKAABcmqenhyRJkkgAAACAqEYoAwBwaUFBwXL9+k0BAAAAohqhDADApXl5aaVMQgEAAACiGqEMAMClBQZqpcwtAQAAAKIaoQwAwOW5u9P3HgAAAFGPUAYA4HJef72VXLlyI8y6pUv/No82m038/BYIAADAy2T9m+OMm5tbgMCl8atBAIDLqVu3qqmOsf4hFGbRQKZYsfwCAADwsln/9khvPXgLXBqhDADA5TRu/KZkzJj2kfVJkyaWJk1qCAAAABAVCGUAAC5HZ1uqUqWUqY4JLXv2TFKxYgkBAAAAogKhDADAJTVu/EaYapnEiRNKkyZvCAAAABBVCGUAAC4padJEUrVqaUe1TNas6aVChWICAAAARBVCGQCAy9LKmPTp00iSJImkadNaAgAAAEQlpsQG4LKunLPJ0Z0Cl5ZAKhVsIadPn5e494rK5hU2getKllbk1YJuAgAAEFUIZQC4rKvnRA5t95AMuRIKXFfBghWtReTePYELu34pQK6ev2eFMgIAABBlCGUAuLQkKb0kX/lkAsC1ndx7Wy4eI5kDAABRi1AGAAAAAIAoFhIScsxaAgQujVAGAAAAAIAo5u7untVavAUujdmXAAAAAAAAnIBQBgAAAAAAwAkIZQAAAAAAAJyAUAYAAAAAAMAJCGUAAAAAAACcgFAGAAAAAADACQhlAAAAAAAAnIBQBgAAAAAAwAk8BQAAAAAARLVj1hIgcGmEMgAAAAAARL2s1uItcGkMXwIAAAAAAHACQhkAAAAAAAAnIJQBAAAAAABwAkIZAAAAAAAAJyCUAQAgmgkICJAlS+bL0aOHBAAAALEXoQwAwCk2bFgjO3duFTxq//5dMnHiCPn667HyomzYsEZGjx4sAAAAiD6YEhsA4BR//rlMzp//TyZPni3OdPv2LbOkSZNOoovcuQtIq1adJE+egvKiaAC2desGAQAAQPRBpQwAwKW1adNQ5s2bKdGJt7e3NGzYTPLlKyQAAACIvQhlAAB4Qfbt2yWzZ08TAACAJ7HZbGesJVDg0hi+BACx2MqVv8mKFYvlyJEDkiFDZmnXrmeY6ovevduZ9YkSJZZVq5bInTu3pVKl6vLhh91MtYbdkSMHZc6cabJ7t5/4+MSVQoWKSZs2Xc37dOjPt99+KTt2bJFLly5I5szZ5KOP+km2bDkc7w8JCTHbrF+/Wm7duiEVK1aTwMCAR8736tUrMn36BDPMxsPDQ0qUKC8dO/YWT8+I/7rS43/zzSTr+Jvl+vVrkidPAWnWrL1kz55L1q79w1zXyZNH5ebNG5I3b0Fp3ryDeU3X23usLF++yCyNGjWXli07Rure6bXqPdmwYY3cuHFd4sdPYK45XrwEjm30erVh7/79uyVhwkRSocLrZv/u7g9+J7J793bp0aO1dc3zrX1Nl3Xr/pSJE7+XLFlelRo1ikvTpm3k3Xc/cOxPr0f3d/DgHkmbNr2UKVPJer21aQw8efJIOX36hBw/fliSJUthPkd9v/1Y4Zk7d4asXr1Czp8/KylTppYCBYrKBx98JAkSJBQAAPByubm5pbcevAQujUoZAIiltmzZIGPGDNG/8KVLl4GSOnU66devg1y8eD7Mdr///oscOrRPPvlkgrXdAFm2bKH8+utPjtc19ND3nTt3xgomekiDBu/LsWOH5O7dO+b1Tz/tI3/9tVyqV3/bvF9DgG7dWsnlyxcd+/jxx2/NUqxYaencub9Zt2nTukfOeciQ7ibIqF27sRWQtDAhxZOa3Q4b1lv+/nulvPFGPenUqa8JKC5ePGde0+BCj9m+fU/rtT7i739PPvusjwmJfH1LyqhRX0nixEms8Kec+blmzfqRuncawnz00fvy339nZNKk2dY+J0ncuPGkXLkq1jU8ON89e3ZY97SXCbG6dftYqlatKYsWzTWh08P0Hvr4+EjXrgNNABQe3Z9uFy9efOnZc6iULl3RhDp6Lfre7Nlzy5tv1rPOc4S8/vpbJnDRvj0R2bt3p8yaNUUKFixqvWe4VKtW21q3w1wbAAAAogaVMgAQS/3883eSLFlyK1yYbp5XqlRN2rZtbAIXrYaw08Bh8OCxphpFq1zmzp0uhw/vd7y+dOkC80V98uQ5pppC1a7dyDxqBYif3ybp2/czqVjxdbOuZMny0rhxVVmwYLapuAkODjZhRIUKVa3jdzfbaIXHiRNHTWWO3c6d2+TAgT0mPLGHI1rx8fnnH0uzZu3Crd7Q42/fvln69BlmKkPs12mnFTG62Gk1y6BB3eTs2VMm/EiePIV13V7mOBpORPberVv3l6nqGTFiiqRKlcYs5ctXMSFHrVoNTJjzww8zJH36TNbxRpt96Ou6XsMpDZw0DLLT+6qB1uOE3p/up2zZymFef+utho6fS5Qoa8ItrTjSMCg89s9Ye9fo+ZcqVcFUCgEAACDqUCkDALFQUFCQ7Nq1TYoUKeVYp1/kc+bMawUfu8Nsmzx5yjDDg+LE8ZH79/0dz7dt22gCDHsgE9rWrf+ax8KFizvWxY0bV3LkyGsqO9TJk8dMqFOgQJEw79XhPKHt3LnFPBYtWtqxLleufKbyRYcQhcd+fB12Ex5978yZk6VlyzpSvXoxE8goe5VPeCJz72y2kP+/1niObXTYku5XQyhdNKzSYV6h6T0IDAw0YVJoVavWksex70/fr+cSHg20evVqK3XrVpRq1Yqa0OXevbsR7lOrg9SIEf1NpdO9e/cEAAAAUYtKGQCIhfTLuM1mM31TdAntaad+1h4wDwcodrdv3zSPD1ex6PMzZ078/za3zKNWqTyOvWqmWbO3Hnnt4SFXDx8/dNVJaMOH9zOBTuvWXayQpaQJLvr16yiPE5l7V7x4WRPIaNWLVvZcu3ZVVq781QQdGnDdunXTDCt6+JoTJHhwHy9fvhBmfdKkyeVxNOzR/UX0ORw6tF+6d/9AatSoY6qTtLeNDiF7nLRpXzFDtlas+EWmTZsgX3wxXOrUaSLvv982wuAHAAAALxahDADEQvrlXfuMaHhgHwpk5+0dR55GihSp5Pz5/yJ8TWkIkSRJUsd6DWISJXoQlCRNmsw8Pq46JfS+hg4db849tPTpM4f7Hq3ysR/ffhy7M2dOmSa8zZu3N0OHIisy906H+3To0Ms0CtbGu0qbCGtPm9D7sAdSdvYQyX5vnuac4sSJE2a4V2gLF84xFU7afDl0g+Yn0SFbumjgo72EJk4cYT4H7U0DAACAl49QBgBiqbx5C8mVK5fC9Ep5FnnyFDRDmHRf9hAk9DGUDvexBx86DObQob2makNps12tnDl27HCY9+rQotDy5StsHjV8iOw558/vax537tzq6Gljd/36VfOoPXPsjh49+Mg+tLLFPhwp9HU96d5pn5z33vtQmjZtHe7ruo89e7aHWaf3SY/3LJ+J3h+9zvDotWpfHHsgowGY9s3RIVeRoc2ZNYCaMWNimH5CAAAAeLnoKQMAsZROlawz7EydOt58mde+IR06vBvhF/uIaFNfnfp6wICPzGw+ixfPM01vtXpGp5/W2Y0mThwuCxbMkTVrVkrfvu2td7lJ/frvmfdrCKFNaNes+d3MaqRVGdow195Dxi537vxmX+PGfWIqXHSK7SlTRj92uJH9PZMmjZD582fL6tW/m74qf/+9yvTB0WoVndZa96XnPX/+9+Z99n43KmvWHGaqb91Gp5yO7L3T2aU0ZNFZpHT96dMnzbWFvv/aT2fo0J6mMfB3330l8+bNNPczouFWj6P70ymvhwzpYfanvXJ0Om3tgfPqq7nMazqNtzb4/eyzvuLv72+aKetU4CpJkmSmomjz5vVmeNb330+Vnj3byC+//GiuXacs1zDH3ktn+vQvpEuXFo73AwAA4MXzEMBFNW6cqWuWLJUS+/g8/ZcjxA5Xz4lcOecpGXInlNhIh9hoaLJ06Xzri/c8UwGhjWLLln3N0aD2jz+WmsfQM/RoiKGvV6z4YBYjLy9vKVWqomzb9q8sX77YTIddvHgZM7xHXytdupL8999pE9isWvWbqXQZOHCUmcnJTitazp8/K3PmTDPhRLp0GcwUztor5s036zq2030dP37YnO+Dc7PJ2283jnCaaKXndurUcSuQWS7//vu3OW6VKm9KypSpzPVv2bLeCmR+kCtXLlqB0XDr2Onl4ME9Ur58VfN+rQTSqaX13LSypXLlGpIpU5Yn3ruECRPLb7/9bAIb7T2jQdOGDavNfnUokd7/nDnzyT///GGGBu3a5efo+aKVKUqvX4MUncI6deq0Ya5Lz0ePWaCAr+PzzJEjj9nf8uWLTMDy+uu1TDWMVuXo0Ci9Tm1+nD9/YTObkwZLWbK8at6r/XD0M1q2bJGUKFHeTKmtIYxOO75w4VwJDg4yU57bZ3UaP36YCXV0u/CaPMc2Ny4FyJ1rd+XVQgLAxQQF+Vv/n19+/6efTo8QIAqlTZu2XkBAwOaLFy8eE7gsOvnBZS1aVO5kpUpDMyZOnEngmg772eSgn4+UqvN0jW+Bh2m1yoMmwh1M8KLhRkyn/WVWr14hP/30R5jZuWKrk3tvy8Vjl6R6cwHgYvz9r1mBdacb9ept4Dd1iFK+vr7LrYcJfn5+KwQui+FLAAA8Ja0iWbp0oeO5hhb58hWSNGlecfSyiekOHdon1aq95RKBDAAAgLPwLy0AAJ6Sp6eXzJ//nZmpyD5TlPaV0R4yTZq0lJju/v37ZipxHYYGAACAl4dQBgCAp9Sq1Udmuuthw3qZWaR05qNs2XLIkCHjpGTJchLTaV+g5cs3CwAAAF4uQhkAAJ5S3LhxpU+fYQIAAAA8D3rKAAAAAAAAOAGhDAAAAAAAgBMQygAAAAAAADgBoQwAAAAAAIAT0OgXAAAAAIAoFhIScsxaAgQujUoZAABiCJ1+e8mS+XL06CEBAAAxm7u7e1ZPT09vgUsjlAEAIIbYv3+XTJw4Qr7+eqwAAAAg5mP4EgAAMUTu3AWkVatOkidPQQEAAEDMRygDAEAM4e3tLQ0bNhMAAADEDgxfAgAAAAAAcAJCGQDAE9lsNpk7d4Z06NBUatcuK126tJA1a1Y6Xl+/frX07dtB3n67vLz3Xk2ZPv0LnVEgzD6uXr0io0Z9LA0bVpEmTarJ+PGfSlBQkOP13bu3S7VqReX06RMyYsQAqVmzlBw/fuSJx967d6d8/HEXc+wWLd6W3377+bHXMmfOdGnbtrGsXfuHedR9DhzYRS5ePO/Y5tatm+ZctKmu3eHDB8y6f//927Gud+92MnnyKPnuu6+sa6ou9etXNs91X716tTX77tq1pZw4cfSF3AtVo0Zxcw2h6bXYj6fX9P33Xz9y/wEAABD9EMoAAJ5o3ryZJngoUMBXevYcKhkzZpEDJAweOwAAEABJREFUB3ab1/bs2SGffNJLfHziSrduH0vVqjVl0aK5VjAzIcw+hgzpbsKb2rUbS6NGLWTduj/DbVj76ad9rH35WGHGQMmQIfNjj33t2lUrUOlsAos2bbpKmTIPQpF//vnzsdejYYfus3XrLjJ27Ddy8uRRmTRppDyL33//RY4dOyyffz5VXnvtDfn115+kX7+OUqpUBfnyyx/kzp3b1jGGvpB7ER69/7pdvHjxzf0pXbqiCXUIZQAAAKI/esoAAB5LKzh+/vk7qV79bfnww25mXdmylR2v//DDDEmfPpMMGjTaPC9fvoq4ubnJjz9+awKHxImTyM6d26wgZY906tRHatasb7ZLliyFFWR8LM2atZMECRI69pcyZWrp0mVApI6tlSwaekyY8K0jtPD3vyfz538v5cq99thrGjHiS0mRIpV5XrJk+ScGORFJlSqtDBgwUjw9PeXNN+vJ4sXzpEKF16VOnSbmdQ1n9Hy04kfvy7Pei4iEvv+6/9D3BwAAANEblTIAgMfS4UEafBQoUOSR14KDg8XPb5MUKlQszHrdNjAwUPbvf1DRsnPnFvNYtGhpxza5cuWTgIAAOXLkQJj3Vq1aK1LHtu83Vao0YapIcubMKwcP7g0zHOhh7u7ujkBGeXvHkfv3/eVZJE+e0gQyKn78B4GKhil2GrLouei9sp+zetp7ER77/df7o4EMAAAAYhYqZQAAj3Xr1g3zqBUvD7t7944ZJhM/foIw6xMkSGQeL1++YB41WFHNmr31yD5C93JRSZMmj9Sx7fvV92v/lYdduXJJUqdOK9HNs96L8Njvf8KEiQQAAAAxD6EMAOCx7BUl2vz2YRoGaM+T27dvhVl/+/aDbRMlShJmH0OHjjfbh5Y+fWZ5lmMrrUi5f/++dO7c75HXnhRoOMuz3ovw6P2PEyeOI+gBAABAzEIoAwB4rMyZXzVDcHbv9pOKFV9/5PW8eQvJnj3bw6zbtWubGdJTsOCDCpZ8+QqbRw0Q7OtexLFz5y5gerRkz55H4sWLJy+KDmdSoYdAXb16WV6EZ70Xj9vfzp1bBQAAADEPPWUAAI+l1RwNGzaTpUsXyNSp42Xdur9k9OjB8uWXn5vX3323tZw8eUyGDu1pXtNZjXTGpNq1GzmGHeXOnV+KFSst48Z9Ihs2rJEdO7bIlCmjzSxFz3NsPUbcuHFl2LBeZp+bNq0z56Hn8Dw0MNHmuXv37jDDg3Q67KlTx8mL8Kz3IiJ6/3U2qSFDepj7M3PmZOnRo7UJlHQq7tatG8iKFb8IAAAAoh9CGQDAEzVq1Fzef7+trF//l4waNVDOnz8rZcpUMq/lzVtQPvlkgpw7d0ZGjOgvP/zwjZktqVWrj8LsY8CAUaaqZuLE4TJ4cHfT1NY++9CzHlungR43bqZpKqyhxPjxn5hZjnRq7OfVp8+ncuHCOalRo7gMH95Xevf+RF6UZ70X4dH7r0Oh9P6PHDlANm9eJxUrVhMPDw/T8PjUqeOydu0qAQAAQPTDVA1wWYsWlTtZqdLQjIkTZxK4psN+Njno5yOl6qQTIDa6du2qNG78urRs2dGEW4jYyb235eKxS1K9uQBwMf7+12TZsk436tXbkESAKOTr67vcepjg5+e3QuCy6CkDAEAspZUyXl5epnIJAAAA0Q+hDAAAsdS+fTvNUKaIphQHAACAcxHKAAAQS+mwJQAAAERfhDIAAAAAAES9Y0FBQQECl0YoAwAAAABA1Mvq6enpLXBpTIkNAAAAAADgBIQyAAAAAAAATkAoAwAAAAAA4ASEMgAAAAAAAE5AKAMAAAAAAOAEhDIAEMts2LBGdu7cKog9NmxYI6NHDxYAAADELoQyABDL/PnnMpk6dbxA5PbtW3L+/H/ysly8eF6uX78mL5uGbFu3bhAAAADELoQyAIBYq02bhjJv3kx5Gc6cOSXvvVdTtm/fJAAAAE/LZrNds5YggUvzFAAAAAAAEKXc3NySCt/JXR7/AQBADBYSEiLffvulrF+/Wm7duiEVK1aTwMCAMNvs3r1devRoLdOnz5c5c6bLunV/ysSJ30uWLK+a9y1ZMl/2798tCRMmkgoVXpeWLTuKu7t7mPf26jVUfv31Jzl27JCkTJlGPvjgIylduuJjz23t2j/Mvg8e3CNp06aXMmUqybvvtjb71mFFet47dmyRS5cuSObM2eSjj/pJtmw5HO/v3budZMiQWRIlSiyrVi2RO3duS6VK1eXDD7uJt7e32WbjxrUyd+4MOXnymMSPn0CyZ88tzZq1k6NHDzp6sCxfvsgsjRo1N9d27NhhmTfvGzl9+oScPXtKMmbMKvXrv2fdu9cdx9b79M8/f8g773xg7X+6nDt3RgoUKCqdOvWRVKnSyNixQ+X33381244YMcAsw4Z9IcWKlQ73Xhw5ctDa5zTrfvqJj09cKVSomLRp09Vcm/UbMvnpp1nmszh58qi1/7TWcVuZa32cdu2amPvTr99wx7q6dStK9eq1zb5DX8d7730oM2ZMlCtXLkru3AWkT59P5euvx8rmzeskceIk1n3pJGXLVn4h9z5r1uwCAACAyGH4EgDEYD/++K1ZNAzo3Lm/Wbdp07pwt/300z5WIOAjXbsONF+49+zZIZ980suEBN26fSxVq9aURYvmWuHNhEfeO3XqOKldu5HMmvWb5MlTwOxLw5SI6L51m3jx4kvPnkNNgKMBj4ZI9nP566/lVoDwtnTpMsAENd26tZLLly+G2c/vv/8ihw7ts85zgtlu2bKFJhxSd+/eMeFIggQJpXv3QSbIuHPnlglkfH1LyqhRX5nAoUSJcubnmjXrm/clT55ScuTIa4U0LUw4oYHQqFED5b//zoQ5toY23333lbRu3cU6zjcmMJk0aaR5rUGD963rGmJ+1uPq/vPmLRTuvdAAql+/DibYadeuh3mvhlt6/koDmZkzJ0u+fIXNvcqZM68Jef799295EfQ6NADr0WOwdQ8Hyvbtm6173VI8PDxk8uQ5JkwZOXKA3LhxXV7EvQcAAEDkUSkDADFUcHCwCVEqVKgqbdt2N+u0GuXEiaOmsuFhKVOmNl+u7X74YYakT59JBg0abZ6XL19Fy2hNyKOBhQYadh9+2F0qV35QudGiRUdTPaGhilafhCf0vnWfoaswtCrHz2+T9O37maM6pWTJ8tK4cVVZsGC2qcawS506nQwePFY8PT1NeKJVK4cP7zevXbhwzgQJWsGh567swYtKnjyF9T4vSZYshRQsWNSxXq+rfv2mjueFC5cw16O9YdKlS+9YHxQUZIUjX0qKFKkc5/jPP3+anzXUcnN78HuNjBmzhNn/w5YuXWDOUwMQ/QyUBlwqICDA9Lx588160qZNF7NO75Vem1bWlCpVQZ6XXsfw4ZPNdWig9tVXo62fU5swRT+b119/S9asWSlnzpwM85k/z70HAABA5FApAwAxlA4b0S/GBQoUCbNehyGFp2rVWo6fNdDRYESH0YSm+woMDDTBSWj2MEFp2JEkSdIIqyLs+9Z96Zf+h23d+q95LFy4uGNd3LhxTfWKVtiEplUtGgrYxYnjI/fv+5ufM2XKKq+8kkFmzZpihVM/PFJl8zgawrRv/47UqlVa3n67nFlnr1yx0+odeyCjvL3jOI79NLZt22hCnND30E7vsx734VCnQAFfKwA5IPfu3ZPn9fB1JEiQyJyL/bPRahfl7x/2WC/r3gMAAOB/CGUAIIbSYTFK+3lERtKkyR0/axCgQ4kefq9+YVeXL1947L7ix0/4yHCXh/cdUTh0+/bN/z9WwoeOnfCJxw1Nw4Zhwyaa6iAdaqMzIQ0f3v+JU1QvXjzP9Jt544268s03i2Tp0o3yMmmvn4juhb6mnvVzcJZnvfcAAAAIi1AGAGKopEmTmceHKzwiQ0MC7S9jD3bs7IFJokRJHvt+DRNChzwP7ztOnDjhDqFS9qqNW7duPnTsW0887sN0uJH2afnqq3ny+edTxc9vo0yZ8vlj36M9XIoUKWmG24RXvfKi6fU+fJ/t7Md/1s/BmZ7l3gMAACAsQhkAiKF0RiOtLtHZhELTPiWRoY1p9+zZHmbdrl3bzJCVh4fTBAcHOX7WYVM3b96QXLnyRbhvbVq7c+fWCI9rP5adDtM5dGhvmCFNTytfvkLmuDrsx06vxWYLcTzXmY6uX79q+qXYHTlyQJ6FfWiPDtd6nDx5CppZnq5cufTIa5kyZTNVMo9+Dn7y6qs5w/R4eZgOp9J+MXYa7AQE3BdnCO/eAwAA4Mlo9AsAMZSGAm+91dDMiKNNaLX6Q6eg3rlzi2TIkOWJ79fpqbt3/0CGDu0plSvXMDMCaZNfbUL7cBgwefIoM62yrtcZiXSq5Ndfr/XYfetsSkOG9JDXXnvDNIjdu3eHjBgxxTSb1dmiJk4cbmZw0t4lixf/YL3LzUxNHVka+nzxxXCpUuVNK8DIZcKWLVvWm6a5dlmz5jDTUOvU2xokaVNanW1Ip3PWe3bz5nXTaFdnoNq3b5cZdmWfDvxJtAJG74PuSyteNJzx9S3xyHZ6P3/5ZZ4MGPCRuT6tEFqxYrFpopsmTTrTLFlnR9KeLblz55cNG9aYwGrw4DGOfSRJksy8b/Pm9ebeaT+YLFmym+qU+/fvy7VrV+TLL0dF+tyfV2TuPQAAAJ6MUAYAYrCmTduYL+TDh/czjVo1pNFeKTr99JPkzVvQTHf8zTcTrbCkvwkV9L2tWn30yLY6XfZPP31rqmQyZswqI0d+Zaa7fty+hw4dLzNnTjLTLetMTPqFXadhVgMGjJJJk0bI/Pnfy9Wrl03T2M8+mxSmIe2TaCNhvf41a343YZIGHM2bd5A6dZo4tmnfvqeMG/eJ9O3bwQQbWs2h02B/9dUYc890CJjONKVDsWbN+tI0OdahV5GhoVj//iNkypTR0qtXW1NdFF4oo/dpzJgZ1nkMNUGGBjnai0UflX0GKw1q9H7o9jq9eeiZlzT80GBn4MDOVpj1veTIkVtatOggN25cs4KeSuLl5W1m4HqWoWzPIjL3HgAAPJ7NZrsfor8RgktzE8BFLVpU7mSlSkMzJk6cSeCaDvvZ5KCfj5Sqk04QPg13evRoLaNHT5P8+QsLEFud3HtbLh67JNWbCwAX4+9/TZYt63SjXr0N0beRF2IlX1/f5dbDBD8/vxUCl0VPGQAAAAAAACcglAEAAAAAAHACesoAACKUJcurMmrUV+YRAAAAwItFpQwAIEI65bY2sNVHILY7cOCYTJ48VzZv3m2mTwcAAHjZqJQBAACwZMiQRm7E9ZGZMxdK+/ZDpUiRPFKsWH6zFCyYUwAAAF40QhkAAABL/PjxpF7zutKyZV3zfNu2vaZqZsKE72T//mNWOJPPEdLkypVFAAAAnhehDAAAQDiKFMlrlnbtGktAQKBs2bLHWnbLJ598KWfPXnSENMWL55fMmV8RAACe0r2AgIBggUsjlAEAAHgCb28vKVOmsFnUrVt3HFFBszwAABAASURBVCHNjz8ul9u374YJadKmTSkAADxBXG9vbw+BSyOUAQAAeEoJE8aXypVLmEVdvnzNEdJMm/azeHi4S9Gi+UxAo4/JkycRAACAhxHKAAAAPKcUKZJKjRrlzKLOnLkgW7fukb//3ipjxnwrSZIkDBPSJEgQTwAAAAhlAAAAXrD06VOb5e23XzPPjx49bUKa335bI4MGTZLy5YtJypRJHI2DPT2pXgcAwBURygAAALxk2bJlMEujRjXM8wMHjsumTbtkzpwl0qXLcMmfP4eUK1dEChTIKYUL5xYAAOAaCGUAAACimE6prUuzZrXN8+3b98uOHQdk8uS5snv3IcdQJ62iyZMnmwAAgNiJUAYAAMDJtDpGlxYt6khQULAZ6rR5824ZPnyq3L8fKBkypDEhTYkSBZh+GwCAWIRQBgAAIBrR/jIlSxY0i7p3z98MddKQ5qefVsidO/ccAY0+pkyZTAAAQMxEKAMAABCNxY3rIxUrFjeLunTpqgloNKhZvvwfOXfuUpiQJl68uAIAiP5sNttxNze3AIFLI5QBAACIQbQy5s03K5hFnThx1oQ0OrPTwIETJWvW9CacKVWqkPj65hEAQPRkBTJZrAdvgUsjlAEAAIjBtMeMLg0bVjfP9+w57Bjq9OGHg00FjX3JkSOzAACA6INQBgAAIBbJly+7WVRISIgZ5qTLoEGT5OLFqyacqVChqBQsmEvSpEkhAADAeQhlAAAAYil3d3czjEkXdf36TRPQHD58Sr74Yrb4+MQJU0mjzwEAQNQhlAHg0q5fCpTda68KANd241KAxHGBUf1JkiSSatXKWotIx47vmH40GtL88stf0q/feMmVK4tpKFywYE4pUCCnAACAl4tQBoDLSpZWJEfhYOun6/KiHTx4XLZv3ydZs2aQ4sULCIDoLV7GB/9PEHETV2LvR9OoUQ3zfMeOA7Jr10EZN+47OXr0lJQsWUhKly5kPRaQNGlSCgAAeLEIZQC4rORp3axFXihtrDljxgIpU6awdBtWT9KnTy0AEFMUKpTLLO+/X1vu3LknGzfukA0bdsi0afPFx8fbDIMqX76IFCuWX2cNEQAA8HwIZQDgBZg9+zeZPn2+1KhRTubMGSUpUiQVAIjJ4sePK6+9VsosSoc6/fvvDvnzz43Svv0npgdN6dKFTSVNlizpBQDwdGw22/Hg4OAAgUsjlAGA56BhzOTJc81UtL/99qUkTBhfACA2sg91Un37tpGNG3fKhg3bpWfP0XLv3n0TzuhSpoyveHt7CQDg8dzc3LJ4enq6QEczPA6hDAA8pYCAQFMV8803C02jzL///o4vIABcTsmSBc3SrZvI+fOXzDCnpUvXyscfT5Q8eV41wzg1oMmWLYMAAIDwEcoAQCT5+9+XefOWyddf/yQffFBftm79WQAAYpoA161b1Sxq69Y9sn79dunbd6zpTWMPaPTR+q2wAACAB/hbEQCe4N49fxPE/Pzz79ZvhJvJv//+IACAiBUtms8snTu/J+fPXzYBjU67PWvWL+LjE0fKlfO1liKSPn0aAQDAlRHKAEAEdJjSnDlLzGxKH37Y0PpSMUcAAE8nTZoUUq9eVbOoTZt2yj//+EmHDsPE09NDypZ9ENBoiAMAgKshlAGAcEyZMk++++4X6dmzpaxbN1sAAC9GiRIFzdKjRwszo9O6dX5myu3OnYdL7dqVJX/+HFK+fFEz+xMAALEdoQwAhDJz5iIzm1Lbto0YpgQAL5l9RqemTWuZvl06o9Mff/wrw4dPldy5s1rhTDGpUKGYpE+fWgAgtrHZbBeDg4MDBS6NUAYALAsXrjKLziSyZctPOkWhAACijvaaqVixuFnUtm175e+/t0iHDkMlThxveeONClK0aF7Jly+7AEBsYP17M5WnpydTeLo4QhkALk3/wT9u3HdSrFg+mTp1iMSLR7k8AEQHRYrkNUu3bs3l6NHTZkanzz//Ri5cuGIFN8WkUqUSUqJEAQEAICYjlAHgkg4cOG7+cZ84cQKZOLGfZMiQVgAA0VO2bBnM0qhRDbl06aqsWbNFZs1aLD16fG6FM8XltddKmmFOAADENIQyAFxKUFCQDB8+TQ4fPmmmty5UKLcAAGKOlCmTSYMG1cxy9+49Wb16s/z11ybp3n2UVK5cUqpUKWlCGg8PDwEAILpzFwBwEfPmLZMyZZqamT2++24EgQwAxHA65PTNNyvIkCEdZevWn6VatdImpClV6h0T0ixf/o8EBAQIAADRFZUyAGK9PXsOy+TJP0jWrOll06Z5AgCInV57rZRZ1Jo1m81MTvPmLZXkyZNagU1ZaykjAABEJ4QyAGI17RujoczQoZ0kU6Z0AgBwDaFnctKm7r//vk769x8vVauWNuGM/TUAcKJj1kI5n4tj+BKAWOnff3dYvy1tIRkypJFZs4YTyACAC9MmwJ991tUMcdLGwL/9tkZKlWoiY8bMNFNvA4CTZLUWb4FLo1IGQKzzxRez5dChE7JgwReSJElCAQDA7vXXy5glICBQ/vpro0yd+pOcOXPB9KapWbOiZMzIbHwAgKhDpQyAWOPIkVNSrVpryZMnm0yaNIBABgAQIW9vL6levZx8/fUQmTFjmMSJ4y2dO38mzZv3k19//UtCQkIEAICXjUoZALHCnDlLzD+i58wZJSlSJBUAACIrTZoU0qpVPbPs3n1IVq/eJCVKNJaaNSvI229XkYIFcwoAAC8DoQyAGO+zz74WH5848uOPYwUAgOeRP38Os3z00XtW2L9aJkz4Tm7duivvvPOmvPVWJfHw8BAAAF4UQhkAMVrt2h1kwIC2UqxYfgEA4EXSEEaXY8dOmym2S5V6R+rWrSJNmrxJA3kAz81msx13c3Nj9iUXRygDIEY6d+6S1KrVXhYvniTp06cWAABelqxZM5ilZct68vPPv0vXriMkXbpU8v77taV4cX4pAODZWIFMFmH2JZdHKAOXdvToSvHxSSyIWe7dC5R+/VbKrFn15ebNv2XfPgEAIErkzSsybFhZ2b37vMyfP1tGjrxq/ZIgtxXOpBfETIGB/gIAzkIoA5cVGHh3xJEjv1NiEcMcPWpLtXSpe8OPPrJN2rv3ZwEAwBnc3ESKFhVJlMiW5uefN5SbPt0tja9vyF9ly7rtFcQ47u5utwQAnMBNACCGKFCgQCovL69d27ZtSyMAAEQjRYoUyWaz2Vq4ubk1CA4O7rVjx45fBAAew9fXd7n1MMHPz2+FwGW5CwDEEJ6enucJZAAA0ZH199NR64vVACuQqenh4dHc+rL1b6FChYoJAETsZFBQEI1+XRyhDIAYwfrH7VrrH7q5BACAaGzHjh2HrYCmjpubWxd3d/fu1t9f3wj/5gYQvkzWLx1p9Ovi+AsCQLRXpEiRT6yH33fu3HlIAACIAaxgZpOfn19j68e11t9jAYUKFXpXAAB4CKEMgGjN+oesr81mq2H9w/ZTAQAghrH+/vrWCmg8PTw8cvr6+v4qAACEQigDILrTKpnGAgBADGYFMx9bD1OtXzbYrHCmnAAAIIQyAKIx6x+tdWw2m7/1W8YjAgBADGf9fbbECmd09tPBVjjTWQC4NOvfuWesBxr9ujhCGQDR2SBrGSIAAMQiVjjzmvVlLLP1yweG5gIuzM3NLb31QKNfF0coAyBasv6hWtn6B+sG6x+uuwQAgFjG+vuta0hIyCHr77uvBQDgsghlAERXjd3d3f0EAIBYaseOHbOs35RfLVy4cBMBALgkQhkA0ZL1j9R6AQEBCwQAgFhs27Ztfa1fQhS1gpluAsCl2Gy240JPGZdHKAMg2rH+YVrKeli4e/fuawIAQCxnBTPdrV9GVChUqFBpAeAyrD/3WYSeMi6PUAZAdKShzA0BAMBF3L59u5W7u/toAQC4FEIZANGOlnGHhIRsFQAAXMShQ4cuWw/rihQp0kMAAC6DUAZAtGOz2eIEBwdvEgAAXIifn19/6+/AagLAVVwODAwMErg0QhkA0Y6bm9sbd+/e/U8AAHAtgdZyxdfXt6EAcAUpvLy8PAUujVAGQLSSP3/+pNbD3SNHjtwXAABcjPWLiXn6ywkBALgEQhkA0Yqnp2fakJCQfwQAABd05cqVNTabLYMAiPWsP+sngoKCmBLbxRHKAIhuElu/IUwpAAC4oBMnTly3HvIVLlyYvwuBWM76N29m6xeSTInt4ghlAEQr1l9Oca2HewIAgIuy/i7803rIJACAWI+mQgCiGy9rOS8AALiokJCQAJvNlkoAxGrWn/WrVgjL7EsujlAGQLRi/eXk4+HhEV8AAHBR1pc0D2tJKgBiNXd392TCd3KXx/AlANGK9ZeTu/XbwRABAMBFBQcHH7f+LgwWAECsRygDILpxsxabAADgonQmQut3FAkEABDrEcoAiFas3wy6CQAAAAC4AEIZAAAAAAAAJyCUAQAAAAAAcAI6PQMAAADRiM1mu20tAQIgtrsREBBAU28XRygDAAAARCNubm4JrMVbAMR2ib29vT0ELo3hSwAAAAAAAE5ApQwAAAAQjdhstoshISF3BUBsdzIoKIihii6OShkAAAAgGnFzc0vl4eERTwDEdpk8PT0ZqujiCGUAAAAAAACcgOFLAAAAQDQSEhJyyWaz3RMAsZr1Z/2Mu7s7w5dcHKEMAAAAEI1YX9JSWg9xBUCsZv1ZT289MHzJxTF8CQAAAAAAwAkIZQBEK4GBgf4hISGnBQAAAABiOYYvAYhWvLy8fNzc3DIIAAAu6v97yjAlNhD7nWZKbBDKAAAAANHI//eUYUpsIPbLwJTYYPgSAAAAAACAExDKAAAAAAAAOAHDlwAAAIBoxGazXQ8ODvYXALGam5vbxUCLwKURygAAAADRiPVFLYmnp6ePAIjVrAA2lfVn3Uvg0hi+BAAAAAAA4ASEMgAAAAAAAE7A8CUAAAAgGgkJCTlnPdwRALGa9Wf9mLUECFwalTIAAABANOLu7p7WWuILgFjN+nOe1dPT01vg0ghlAAAAAAAAnCDWDl/66acib9pstmICIEbZtMmW/eRJSd+7t+9gARAtuLm5+TdsuG2EAAAA4IWKtaGMl5dP/eTJ8zRPliybAIg57t49LXfunJa8eUuXEABOFxISKIcOLfW3fiSUAQAAeMFidaPfdOl8JVu2agIg5jh/fqPs27fOCmUaCgDnCwy8o6GMAIg6NpvtWnBwsL8AiO3OW3/eAwUujdmXAEQrnp7ukjAhvQ0BAK7Lzc0tqaenp48AiO3SWH/evQQujUa/AKKVoKAQuXXrxc4CeuDAMVmwYKW17yABAAAAgOiCUAZAtHDlynVZtOgPuXv3nvj4xInUe+7fD5Bhw76StWu3OtYdO3ZaBgyYIOfOXXKsGzt2lgwfPk127jwoAAAAABBdEMoAscjWrXukZcsBUrZsU3nnnZ7y7beLJCQkRJ7Hxx9PlKJFG8iIEdPCrD916j+zXperV6/L87p27YZ8+unXcvz4WfH3vx+TmKPQAAAQAElEQVSp92gos3jxn3Lx4hXHuhMnzsqKFevC7KNdu8by0UdNpWDBnAIAAAAA0QU9ZYBYIiAgQPr3nyCVKhWXd9+tKXv3HpFp0+bL5cvXpUePFvKsbt68Le7u7rJly54w67dt22fWa+hz48ZtSZYsiURXhQvnNgsAADGB9XfrOevv2LsCILY7Zi0BApdGKAPEEt7e3rJgwXhJkOBBk9zXXispwcHBsnjxX88Vyly/fksKFMghO3YcsAKea5IiRVKzfuvWvY71N27ckhfF09NDUqdOLtHVrl0HZdOmXdK6dQMBAOBlsAKZtNbDKQEQ22W1Fm+BSyOUAWIReyBjp1Usbm7yXDSUeeONcqbyZsOG7fLWW5XNeh0q1aTJGyaU0W1CW716k8yfv1J27z4siRLFl9dfLyMdO75jKmvsdu8+JF9//ZPp85I1a3qpVauiWR8UFCwXLjwYjqSh0pdfzpN16/zkv/8umuFHQ4Z0lOTJn64qZ/r0+TJ16s+yefOPjnXFizeS/v0/lH//3WFClsSJE0rTpjWlfv1qjm0uXLhsqo3WrNliXeNN6/7Gkxw5MptHAAAAAHhe9JQBYqmjR0/JkiV/m14qz0PDCA0sihTJa4Uau8067duijXmrVCn1/9v8L5TZsWO/9Oo1RuLG9ZGPP24nNWtWlLlzl8qECd87trlz56507TpSzp+/LL17tzKBjG6jNETy8PAwP0+ZMk9mzVosuXNntQKUNuaY7doNFZvNJi/CyJHTJU2aFPLDD59b11JSRoyYLvv3H3Vc9/vv95UzZy7I7NkjZNKkARIvXlxzzWPH9hYAAAAAeF5UygCxjE77XKrUOya4aN78bUcFSkTbasCSLVtGKwx5tKRG93H79l1TGVKyZEH57rtfzHrtL5MlS3pJnz6NeZ8GGHYzZiyUTJnSyejRPc1zDTF0G2063KJFHUmSJJH8+utq857p04dK5syvmO10fx9+OFg0b9EKGW3UO2/ecqlatbQMHtzBbKPBUM2a7WX9ej8pW7aIPK833igvXbq8b35+7723rHNcbIUyx6wQKJv89dcmEwJNmfKxFdykNIsGNxoUNWhQLdz7BQAAAABPg0oZIBbSqo62bRuZ8KNNm8ERVpZ88slX0rhxD5kzZ0m4r1+9esM8aihTunQh8/zIkZPi57dPSpQoYF5LmjSRafSrNEzRoUDFiuULs58iRfJIYGCQGc6k9P06BMkeyKjEiROEeY8Ob9JgplSpgo51KVMmk7RpU8qePUfkRdAqGTv7NNz37j2Ytck+a1W8eD6ObfQ+3Llzz1wnAAAAADwvQhkglvH09DSByQcf1JcOHd4x4Yb2gwlPliwPQpHMmdOF+7pOU600jMiaNYOkSpXcVMls3LjTEbzo0CZ7o18NLDTMeLjnSqJEDwIX7dGibt26G2FfFnd3NyvoSWxtc8c8Hzp0imPqbV3OnDlvhj29bGXL+prhSlo9o3Tabw25ypUrYu4xAAAvi/XLlIvWLwCYfQmI/U4GBQUx+5KL45sFEIvZwxYNMfLly/7I682b1zFLRG7efBCMJEmS0DxqtcyCBavk7l3/UKFMAsd2Gr5oxYk9UPnffm7//34SmcdkyRKbYVPhCQmxmTAodeoHVSzt2zcxszyFZp8B6mXS4Uq9erWUwYMnm6bFqmDBXNK3b2sBAOBlcnNzS+Xh4XFcAMR2maxf9jH7kosjlAFikW3b9pq+K3b79j1oWvvKK6nkWYQevqS0Amfx4j+tkCSnqSJRGsSE7ilTqFAu2b79wEPntc9UlxQt+uDc8uZ9VVauXB9miu2AgEDz6OnpboZEZcuWQRImjG/WFy2aT5xBmw9/+GFDpr8GAAAA8FIwfAmIJbZv328a5U6aNMf0ddHZjsaN+05Kly5sGtc+C3uFiw5RUtrfxdc3j7z5ZnnHNhrK2HvKqNat68uxY6elZ8/P5a+/NspXX/0oM2cukkaNqjsqZbT5sIY6EyfOMVUxly5dlVGjvjGvBQWFWOtumoqbZs1qy/ff/yqLFv1hpuCePfs3az/dwwyr8vb2kl27DplhTco+Xfb69dvDhEXP4uLFqyZQWrdumzn+yZP/OXrNAAAAAMDzolIGiCUKF84t7do1NtUd2gclV66s0qpVPRNsPCsNR7y8PB1NcBMkiC9Tpw4Js02iRPHNdnY6xGfChL4mcOnff4IEB4dI3bpVwkzNrUHOpEn9ZfjwafL6663llVdSy+DB7eWDDz42U2LbZzbSoVXao3jWrF9McJMxY1qpXbuSo3LH3d1dmjatJd98s9DMEqVTVWsVj57D+PHfmW309WfVqdO78tlnU00gY/fqqxnl668HO4IqAAAAAHhWsXZO10WLyszMn79p82zZqgmA6K169TYmdAk9S5QGM/bnfn4LxNl0+vA9ew5Lhw7DpE6dKtKjRwsBXEFg4B359dfW/vXrb4wrAKJEkSJFplkPm7Zt2zZdAMRavr6+y62HCX5+fisELovhSwCcrnHjGuLl5WUqX+yLhjK6OKufzLBhX8nChascz7UnTqFCuU1Vj73XDgAAL0NISMjl4ODgewIgVrN+AXnW+sVfoMClMXwJgNM1aFBNli1bK8eOnQmzXhv+Nm5cXZxBh219992vkipVMsfwra1b95p+OS1b1hEAAF4W65cTOgUh1WlALGf9AvIV6xd/XgKXRigDwOnix48nb75ZQb78cp5Yvxl0rM+WLaNUrlxKnEF74OjU3r16jTEzQOksUTlyZJZx4/pIuXJFBAAAAACeF6EMgGihbt2qsmzZP3L06CnzPEmShNKkyRviLHHj+siwYZ0FAAAAAF4WesoAiBYSJowvNWqUFQ8PD/M8Q4a0UrFicQEAAACA2IpQBkC0Ua9eVTPtdfz4ceWdd5xXJQMAAAAAUYHhS4i2zh61ydnDApcSXyoVbGEa/iYOLi2bV9gErsMnvkiBcm4CAAAAuApCGURbZ4/Y5MQBH0mdickHXEnevOWsReQeE4G6FP+7wXJx2y0rlBEAcHkhISHnrYc7AiBWs9lsx93c3AIELo1QBtFaqoxxJV/5ZAIgdrt5JVAunrglAAAzJXYa6+GkAIjVrEAmi/XgLXBp9JQBAAAAAABwAkIZAAAAAAAAJyCUAQAAAAAAcAJCGQAAAAAAACcglAEAAAAAAHACZl8CAAAAohGbzXY9ODj4vgCI1aw/65eCLAKXRigDAAAARCNubm5JPD094wiAWM36s57Sy8uL7+QujuFLAAAAAAAATkAoAwAAAAAA4ASEMgAAAAAAAE5AKAMAAAAAAOAEhDIAAAAAAABOQCgDAAAAAADgBEy/BQAAAEQjwcHBF62HuwIgtjsVFBQUIHBpVMoAAAAA0YiHh0cqa4knAGK7jJ6ent4Cl0YoAwAAAAAA4ASEMoALCwgIkCVL5svRo4cktjlwYI9s2rROYpp16/6SjRv/EQAAAACxH6EM4ML2798lEyeOkK+/Hhtm/e+//ypt2zaWt94qI927fyBr1/4hL8KhQ/tk9eoVOlZeXrbffvtZli9fJNHZyZPHZMSIAXLhwjnzPCQkRD75pJcMGtRVAAAAAMR+NPoFoimtYjl//qxkzJhFXoTw9pc7dwFp1aqT5MlT0LHu2LHDMnbsUGnQ4H3r9fyyd+9OiRPHR16Efft2yZQpo6VEifISL96jQ+VbtaonZ86cfGR9164Dxde3hLz3Xs1HXnvjjbrSuXO/R9bv2rVN3n67ieO5n98mE9To+qxZc5j3VapUzfF6ixZvy3//nQmzjwwZMsv06fPlZTl9+oQJqd55p5V57u7uLj16DDaPoV28eF68veNIkiRJBQAAAEDsQSgDRFMajBw+vF9mzFggL0J4+/P29paGDZuF2c7Pb6O4ublJy5YdTThQpkwliSo9ew6R+/f9Hc8PHz4g06aNl9Sp0zrWNW/e3gqRCjiep06d7pH9nDt31gQZBQoUMc83blwrgwZ1k3r1mkqVKm/Kv//+LSNG9LeCqvtSrdpbjveVL19Fatas73geP35CiWpVq4YNns6cOWWFVXWlT59hVohUXQAAAADEHoQyAMK4e/eOeHp6PlKtERVy5coX5rlWkaRKlUYKFSomly5dMOsyZ84mBQsWfex+du7cKgkTJpJs2XKY50WKlJLhwyebahulQdPBg3tl5cpfw4QyKVOmfuK+AQAAAOBFIZQBnOT06ZMyefJIOXbskAQFBUnWrNnNcJscOfKEGaZTrVpRM4xo/PiZ5nmNGsWld+9hcvz4YVm2bKG8+25rSZYshaxatUROnjwqN2/ekLx5C0rz5h0ke/ZcpmLkSftr2rSNtZ8Pwgwf0u1CK1WqggwePMbxXIc1/fjjTNm1y0+SJk0mdeu+K7VqNQjzHj0nbSR84sRRE6w8zVCse/fumVCmceOWpnLnaWiTX72P9mDJy8vLEcjYpU37iqOXy7OI6PMrW7ayeb1373bWMdJbrwXK9u2b5c6d21K0aCnp2XOoxIkTJ8L96vvUyJFTTHWT9vdR2ntGl2HDvpBixUoLACD2CgkJuWez2QIFQKxm/Rv3dnBUNFtEtEYoAzjJlCmfW4HJOROexIsX3wyp2br1XylevKyMGvWV/PDDNyYg0SE9iRIlCfPeefO+kQQJEkqnTn0lS5bs4u9/z3xRf/PNuubLv4Y1n33WR2bMWChJkiR74v7s9DXtu7Jmze/W+yc51k+Y8FmY7a5duyoDB3aW+PETSJs2XeW//05bAcUoc6xy5V4z2+zZs0NGjx4sJUqUNX1S9Ng//TRLIuuvv5bL/fv3rdCojjytgwf3mBDpcbSfS/bsueVZRfT52UMZtWrVb1KyZHn5/POpcu7cGeue9pWpU8dZn1ufSB1D+/roEKzPPx9k+s5osJU9ex4BAMRu1i8V4loPXgIgVrPC1wTWn3cPgUsjlAGcRPu76Bf2N954EDpUrPi64zUdQrN8+WIzZCe84TRXr16WceNmSty4cR3rtCrGTsMS7aFy9uwp06z2Sfuz0+FDmzevM5UpobeLGzdsU16tftHwZ8KEb83+lQZD8+d/7whlFiyYbSp4Bg0aIx4eD/6u0YqS2bOnSmSsWLHYDDN6uLmtHluDjXv37kqVKjWlRYsOjv2rGzeuy/HjR6Rt2+4R7vvQof2mqW/HjmHDkZ07t0nnzs3l1KnjUqRISRN6JU4cfoD1uM/PLl26DNKv33BzfunSpZfy5auasEnPTat3nkTvrZvbg2ofrTJiaBUAAAAQuxDKAE6ilRw6PCd58pRWkFHF0f8kMvTLfehARmdWmjNnmvzzzx8mbLBSd7Ne+8O8DDt3bjG9XuyBjMqZM68JTDR40Z40u3f7mSFDoQMT7fMSGToDlE6f/f77bR3rNGjSIVavvJLR2k9i2bp1g/z883dm/xrM2OlxNfDIlSt/uPvWaae/+mq0CVQ0eLF7661GJozSnjU6/GnhwjkyZEh3GTt2Rrj7icznp6FUtv1hsQAAEABJREFU6OvXIU5axaRVMy9qVi0AAAAAMRehDOAkbdv2MDMHbd++SebNm2n6vLRv3ytMxUtEkiZNHub58OH95MiRA9K6dRcTNGio0K9fR3lZtEpGe9U83HdGXblyycyWdOvWTYkXL4E8i6VLF5jQR3uw2GkoEzqkKV68jFy7dkVWrvztkVBGK34i6tsyffoXppLm669/DLO+Tp3/TZ+tw4R0SJIOydLeMRkyZHpkP8/y+ek1KK3mAQAAAABCGcBJ4sWLZyo/dNGGs59+2kc+/riLzJmz7KlmPtIpkzdsWGOmitYpnaOCzlKk/V46d+73yGv2wEirRO7de/pKHa360QoU7afypAa/GoqsXfuHqX6x37Ndu7ZF2E9G96vDqoYMGWtCnyftW12+fCHcUOZZPj9twqweDtUAAAAAuKaon/MWwCO0sqRixWqmV4xWfygdAhQS8uRm7NevX/3/faRzrDt69OAj20V2f5GRO3cBUxGjTWe1z0noxdvb22yjw5l0GFJoOhPRk2hwosOuHm7wGxgYaGZkCk0b+mq4Yg9B7P1kwuu9otNka+NhbbKrQ5ce9nD1iu5bhb6vEQnv81PBwUFhttu7d4fpUZMmzZP3aaef24N90ZgfAAAAiG2olAGcQGcv6t79AxMO5MtXSDw8PE0/Fu05oj1KlM6q9McfS2X9+tUmkNBZmbQ642Ha18XHx8c0xk2WLLmZflob7iqdAUnDkafZX2TUrt1Ifvllngwb1ksaNmxmqmZ+//0X04/FPsRIt+nTp70sXjxPqld/20wdrVUqTxJRg9+5c6fLpk3/SP3671v3KIUZoqXTcXfpMsCxTUT9ZDSo+fjjrmYmI71fGtDY5clT0LpP22XkyAFm+u0sWV414Yqe92uvvWEa9D4sMp/fg/PZLjNmTDTDsM6ePW2qerSiyR602CtmNm9eb4U1ScNtKpwiRSpJlCixbNy41lQoaTjz8PTeAAAAAGImQhnACZImTSa9e39ipp+eMOFTiRPHR0qUKCfvvtvasU2tWg3k1KljMmrUQDM8Z/DgsWEa09rpF/mhQ8ebL/+DBnU1ocOwYRNlx47Nsn//LmuLd59qf5Gh/VZ09qexY4fKkCE9TCikQUiZMv+bDrpw4eLSsWNvMw32lCmjzbFatuxkqlUeR88rMDDgkfVNm7YxYcno0YNM8JIvX2HrHg6TypWrO7aJqJ/MwYN7zexQfn6bzGKnFTZLlvxrzvW99z40/WZ0O91Ho0bNpV69puGeY2Q+P6WBje5PhzZpw1/tW9O4cQvH63nyFJC8eQvKtGnjzfP69R89ngY4/fuPMPewV6+2pgqIUAYAAACIHdwkllq0qMzM/PmbNs+WrZogZtr8e4jcvZNU8ldIJkBktGvXxPSTCd0Q2Fl6925nHkeOnCJ4sptXAmXdz6flvf6x9q+lGCsw8I78+mtr//r1N8YVAFGiSJEi06yHTdu2bZsuAGItX1/f5dbDBD8/vxUCl0WlDIBYQat/cuTIY4ZlAQAAAEBMQCgDIFbQoUhduw4UAAAAAIgpCGUA4CVo3bqLAAAAAMDjEMoAwEvw6qs5BQAAAAAex10AAAAAAAAQ5QhlAAAAAAAAnIBQBgAAAAAAwAkIZQAAAAAAAJyAUAYAAAAAAMAJCGWAJxg6tKds27ZRXoYrVy7JokU/WI+XBXAGm80mnTs3lzNnTgkAAACAqEUoAzzGmjUrZf/+3ZI3byF5GZYuXSBffTVGli1bKM52+/YtWb58sVy8eF5iqg0b1sjo0YPleV29esX6TBbJ9evXJDbYvn2zDBvWO9zX3NzcpFChYjJhwjABAAAAELUIZYAI3LhxXb744jNp2bKj+Pj4ONZraPGivqxXr/62vPvuB1KjxtvibMePH5Hx44fJhQvnJKbauXOrbN26QZ7XjRvXrJDiUyuQ2xXhNiEhIXL69AkJDAyUF8Hf318mTRop1aoVlcWL5znW+/ltkiFDeki9epWkZ88PZfXq3x957+HDB2TQoG5Sv35lE77oeYW2b98uWb9+dYTHbtKklamU+e23nwUAAABA1CGUASKwaNFciRs3nrz22huOdfrF9b33asr27ZvkRUiVKo28/35bSZEilSBmWbHiF/ngg/py8+YNeV4amrRr11hWrfotzPqNG9dK374dJG3a9NKt28eSOnVaGTGiv/z++6+ObTRM69GjtflvtUuXAXL//n3p2rXlU1U8aej41lsN5fvvv5agoCABAAAAEDUIZYAIbNiwRgoXLi7u7vwxwcujFTJ9+rSTZMlSyIQJs8K8VqRIKRk+fLK0adNFypSpZIUvgyVjxiyycuX/QpkVKxZL4sRJpFevoVK2bGX5+OPPTZ+Yv/5aLk+jaNHSpjps924/AQAAABA1PAXAI7T64eTJY1KrVgPHurFjhzoqFEaMGGCWYcO+kGLFSsucOdPln3/+MJUKM2ZMlOPHD8vs2ctk8uSRZiiJPtcv3ZUqVZemTds4gp7du7ebKofRo6dJ/vyFzbrevdtJhgyZJVGixLJq1RK5c+e2ed+HH3YTb2/vcM83ICBAxowZIvv27TRDb/T95ctXlQYN3jfHmjJltPz990qZN2+l4z2DB3eXS5cuWOc4O8y+Ll48Jz17TpGDB/dKunQZpHPn/pI7d37H63PnzpDVq1fI+fNnJWXK1FKgQFH54IOPJEGChLJ27R/mnE+ePGruYd68BaV58w6SPXsux/v1+jRYSJgwkelhExgYYK5Pz1X7wRw8uEeyZs1hjps5czbH+2rUKG6Gku3YscVUlvj4xJXq1WtLs2bt5HG0P8z06RPMsCYPDw8pUaK8dOzYWzw9//e/P+0bpFUie/fulEyZssrrr9d67D5btHhb/vvvjPn5nXeqm8dff10vceLEMb15vv32S3Oeen/1Gj76qJ9ky5Yj3H1plcqnn04y90oDmtC8vLzE17dEmHVp074SZojZv//+LQULFnX8N6XvyZevsFnfuHGLcI956NA+U03TvHl7c9+Vnp/ekz17dpgwEgAAAMDLRwkAEA4dEqI0lLDTL689ew4xP7/zTisZNeqrMA2Ar169bH257iNFi5aS7t0HmS/b2bPnljffrCf9+o2wvui/ZQKNP/9c9sTj//77L+aL8yefTDBBjzYC/vXXnyLcfsGC2bJhw2qpXbuR9OnzqRWUFDGVPsHBwfK0NMApWbKCdOs2yPRN6d+/kwkalIYWs2ZNMSFAv37DpVq12ta6HabCQukwGw2p2rfvKZ069bFChnvy2Wd9zH4evr5jxw7L559PNcPD9Nr69esopUpVkC+//MEEURqCPUyPnStXfiv4WihNmrQ09/Pvv1c99nqGDOlu+qnUrt1YGjVqIevW/Slffz3W8frdu3dk0KCuZriPhjUayCxcOPex++zV6xOpV6+p+bl//xEmVNNARul/A1qlov2C9LPTsKRbt1Zy+fLFCPengVxkK7I05NPgSGlFjAY0Go6FpsPhNFwLj35WGshpFY49kFF6/DRpXpFTp44JAAAAgKhBpQwQjps3H4QMWs1hp9Unbm4PvjhrpYcGE6Hpl91WrTpJw4bNHOu0T4ddiRJlTTigFRtVq9aUx0mdOp31xXmsqVzQSou5c6fL4cP7I9z+yJEDkjRpcqlf/z3zvHTpivKsNFCpXLmG+Tlr1uzSunUD+eOPpfL2240d56DXqP1wNERp1Ki5471aERO6KiZ+/ASmAe3Zs6fM/bNLlSqtDBgw0lyfhlba2LZChdelTp0m5nXd7/z535vQQWcHsqtatZa8914b87PeWw1zdChPhQpVw72WnTu3yYEDe0xAVLNmfbNOK5Y+//xjU2Gj1T1a/aSf3Zgx0x3nmCFDFit4+VAiopVD9uBOg7nkyVOYn7XiRhvz9u37mVSs+LpZV7JkeWncuKoJzrTa6XkcOrTfVOh07NjHPLeHYfHixQ+znV6XVgiFvn/6qOGYNgLWAEnDu4fpf++xZcYpAAAAICagUgYIh73ZqYfH0+WWWjkSmgYCvXq1lbp1K5pZdTTUuHfv7hP3kzx5yjDDa+LE8ZH79/0j3F6/+GvFxMiRA2Xbto2PVKY8jZQp0zh+1vBJh1HpdagSJcqZR202q9Ug9+7dC/NeHUY1c+ZkadmyjlSvXswEMkqrUSK6vvjxE/7/cf9X7aGhgn4GD1f6PNwQOUuWV+Xo0YMSkZ07t5hH7ZdilytXPnOeGmQp7aGSLFnyMKGRXvOz2Lr1X/MYevhP3LhxJUeOvGZY0PPQz/Srr0abz7pIkZJmnYYuT0OHVR06tNcEfvHixXvkdS8vbzOcDAAAAEDUoFIGCIfOZKN0+E1k6fAPbbhqp1UN3bvrdNd1TIWE9uzQYSwvQ5Uqb5oAY8OGNTJ0aA8rVEgirVp95KjWeB5aPXHt2hXzs/Yz0WFbOvPQtGkT5IsvhpvqFp1BSisxhg/vZ8KO1q27mOBAwxwdlvSyaCXOtWtXI3xdh0GpZs3eeuQ1++xEOjQrXrwE8iLcvn3TPGqoFJo+P3PmhDyP6dO/MNU5X3/9o2NdkiRJzaP9Ov93HrfMa6GrjJQ9ENR+POHR8My+TwAAAAAvH6EMEI40adKZR60+0Qasz2LhwjmmwqVNm64RNuh9UfTLtza91eXu3bvWF/cxJiDRShJ7/5FnpV/ws2XL6Xiuw7Z00coN7XUzceIIU8FSsGAxEwpp89jy5atIVLh166apuomIvbJm6NDxpsdPaOnTZzaPSZMmM31aXgT78fS8Qocbeg81KHtW2lhZhz8NGTLWDBuz089dj3Pu3Nkw2587d8YMZ3vYBx90NtNsa6XT2LEzHgltLl06b/ogAQAAAIgaDF8CwqHDdrRPh84iFJp9yE1kGuhev37V9C+xBzJahaC9VV42HZZSq9aDXjb2oT3e3nEcQ7LstDFxeIKCAh0/a2WG9i0J3dDYTiuDtE+L3icdlqXXq7Qfjt3jhhY9i+DgoFA/B5uhRzocKSI6C5HSHir2MMm+2PvA6NAivRdXrvzvfkRmCI/O5KRCQv7334L9Pu3atc2xTod46ZChZ53RaOfOrWZWKu2Lo0OXHqbXsn//LseQtcDAQNOQuVChYo9sq/ehd+9hZvaqn3/+Lsxr2oNGP+ucOfMKAAAAgKhBpQwQDg0cypSpZHqEtGjRwbFeKyG034hWG2gPFA0GHp6y2O7VV3PJ9u2bZeXK38wwm+XLF5kpj0+ceDBd9LP2LXmY9hXp27eDCYD0i7+el84epEOw8uR5UOWjDXu1ekMrQhInTiqLFs21zuOIaWj7MJ0+umHD5qaHzQ8/zDBDsuxTRH///VQrcNgqZcu+ZipwdNpnDZt0Jh/tyaLVKCtWLDY9WvQ6tVmv0n4qL+LLvjYE1uvUY2nTZA0R6tZ91/F6kiTJzHVu3rzezAKlDXn1cdy4T8wQMg2QdKpovQ+ffTbJvEevTafD/uabiWbYlQY/kyePeuK56LTdSj9fraIeZlcAABAASURBVC7R+54nTwFzvIkTh5vpsLWKZ/HiH6yt3BxNmJ+GhmIff9zVzKal16wBjZ1+tjr9dYMGzaRr1xamn5BWKOnQMqWzTYVm7z+TL18h07RZZ7LSkEcDSLV58zoTOj5Pk2gAAAAAT4dQBoiAThfcpk1DM6OOfrlX+qVVp0DWaaO1ga9WKUQUyjRt2sb08NCQQxsG6xdmDQa0D8upU8fNl+MXQYeg9Ogx2AyX0uoHrfrQcEaHp9iHYVWsWE0OHz5gfXlvac5JK1w0zNiyZcMj+9Ng4ptvJpnqFx0C86ApbHzHPdGw5++/V8rRo4fMTEuDBo12fJHXYUIzZkw0U0xriDBs2EQruNlsKjlE3pXnValSdROqaD8bDX70ukPfR+2t88sv82TgwM5WMPK95MiRWwYMGGXd889MUKJVK9rbxz6dtdKeORrQ6OuNG79u+uZ07z7Y9AN6HL32tm27y48/zjR9bXQ2Jx0upsebNGmECaT0s3jllQxm/w83KY6Mgwf3mr5GOqOTLnYaGi5Z8q/jPHSKbm3iq9OiP5i5a4y5joi0bNnJDDX77LO+1rnONv9d//rrj+a/i9B9kQAAzmEF6Vqy+eSyXAAxmvVn/X7I88zQgVjBTWKpRYvKzMyfv2nzbNmqCWKmzb+HyN07SSV/hWTiLOPHDzNhxqRJ3z/SfwNRq0aN4iboevfdDwQvlk4LrkHjt9/+4rRGvzevBMq6n0/Le/35cxbdBAbesUK71v7162+MKwCiRJEiRaZZD5u2bds2XQDEWr6+vsuthwl+fn4rBC6LnjLAYzRr1t40Tf3771UCxEY6rOnbbyebBs3MvAQAAABELYYvAY+hM/MMG/aF5MiRR4DYSCvABg783DFEDwAAAEDUIZQBnkCbt8L5Roz4MszMTnhx+G8cAAAAcA5CGQAxgjZVBgAAAIDYhJ4yAAAAAAAATkAoAwAAAAAA4AQMXwIAAACiEZvNdj04ONhfAMRqbm5uFwMtApdGKAMAAABEI9YXtSSenp4+AiBWswLYVNaf9f9j707gbKr/P45/ZrEv2ZdsWSM7hUqiElJpQbYioiQtKluLNUupbKVSVEik9GvTrr2UJZFCUpaQfV+Gmf95f/3P7c6YYWaMWe68no/HdWfuPXPn3Dvuued8zmfJYsjUKF8CAAAAAABIAwRlAAAAAAAA0gBBGQAAAAAAgDRAUAYAAAAAACANEJQBAAAAAABIAwRlAAAAAAAA0gBBGQAAACAdiY6O3uRdHTAAoe5P73LEkKkRlAEAAADSkfDw8OLeVU4DEOrKeZeshkyNoAwAAAAAAEAaICgDAAAAAACQBgjKAAAAAAAApAGCMgAAAAAAAGkg0oB07N91B23ZVzsMQGg7cvCYAQAAAJkNQRmkWyUqhJnFHPa+OmzIPNau3WB//LHOmja9yJB55MxhVqyUAQAAAJkKQRmkWyXKh3kXQyaz97ONtmjt91av+cUGAEBmFB0dfdS7kEIIhL6oI0eORBsyNYIyAAAAQDoSHh4e6V0iDECoy5I1a1b6vGZy/AcAAAAAAABIA2TKAEhXIiPDLU+eXAYAAAAAoY6gDIB0JTo6xqKijhoAAAAAhDqCMgDSFQVlDh1i4hYAAACA0EdQBkC6EhamS5gBAAAAQKgjKAMgXYmJ0SXGAAAAACDUMX0JAAAAAAAgDZApAyBdyZIlwgoXzm8AAGRW0dHRW2NiYg4YgFC3/ujRo0cMmRpBGQDpSlTUMdu6dacBAJBZhYeHF/auchqAUFcqMjIyqyFTIygDIF0JDw+zrFmzGAAAAACEOoIyANIV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\" style=\"max-width:100%;height:auto;\" alt=\"Figure 5.1 \u2014 Implementation architecture.\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a27101aa",
   "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": "5e9ac7b7",
   "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": "5111be4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded 2,219,201 rows x 32 features; positive rate 0.2720\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 = 'sibasispradhan/edge-iiotset-dataset'; 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 = None                                                   # read the file WHOLE: it is sorted by\n",
    "#                                                               label, so any head-cap would drop a class\n",
    "files = sorted(glob.glob(DEST + '/**/*.csv', recursive=True))\n",
    "ONLY = 'DNN-EdgeIIoT-dataset.csv'                                          # canonical file (others are variants)\n",
    "files = [f for f in files if os.path.basename(f) == ONLY] or files\n",
    "assert len(files) == 1, f'expected the canonical file {ONLY}, got {files}'\n",
    "df = pd.concat([pd.read_csv(f, low_memory=False, nrows=NROWS) 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 = 'Attack_label'; FAMILY = 'Attack_type'\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(['ip.src_host', 'ip.dst_host', 'arp.src.proto_ipv4', 'arp.dst.proto_ipv4', 'http.request.full_uri', 'http.request.uri.query', 'http.file_data', 'http.referer', 'http.request.version', 'http.response', 'tcp.payload', 'tcp.options', 'tcp.srcport', 'tcp.dstport', 'dns.qry.name', 'dns.qry.name.len', 'dns.qry.qu', 'mqtt.msg', 'mqtt.msg_decoded_as', 'mqtt.topic', 'mqtt.protoname', 'mbtcp.trans_id', 'mbtcp.unit_id', 'mqtt.conack.flags']))  # 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",
    "# CRITICAL: coerce numeric-looking STRINGS *before* any label-encoding. Some captures serialise\n",
    "# the same numeric value differently per class ('0' vs '0.0' vs '0x00000000'), which turns an\n",
    "# innocuous protocol field into a PERFECT label proxy purely through text formatting.\n",
    "# Coercing first collapses those spellings; only genuinely non-numeric columns are encoded.\n",
    "for c in list(X.select_dtypes(include='object').columns):\n",
    "    _num = pd.to_numeric(X[c].astype(str).str.strip(), errors='coerce')\n",
    "    if _num.notna().mean() >= 0.95:                            # essentially numeric -> keep numeric\n",
    "        X[c] = _num\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": "7b2cf8a9",
   "metadata": {},
   "source": [
    "## 7. Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d97598f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "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": "4ec83602",
   "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": "6d141285",
   "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": "be2616f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "materialized 2,219,201 rows | trained on 120,000 (stratified subsample) | held-out 554,801\n",
      "MAJORITY-CLASS BASELINE accuracy = 0.7280  (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": [
       "<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>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>0.969980</td>\n",
       "      <td>0.993167</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.969250</td>\n",
       "      <td>0.992720</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>0.962468</td>\n",
       "      <td>0.984829</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.898311</td>\n",
       "      <td>0.914780</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MajorityBaseline</td>\n",
       "      <td>0.728000</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  0.969980  0.993167      1.7\n",
       "1             XGBoost  0.969250  0.992720      0.5\n",
       "2        RandomForest  0.962468  0.984829      0.6\n",
       "3  LogisticRegression  0.898311  0.914780      0.4\n",
       "4    MajorityBaseline  0.728000  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": "c82f1abc",
   "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": "78aea385",
   "metadata": {},
   "outputs": [
    {
     "data": {
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cSaEZWEp3ocSlmU1xm63r7lfdqexol69e4maRaXaSZkklRrPSdAeO7oLRXg86n9KfXbPldEeyNr5PiO5CSQ7NplPfffddvIwz3RGjvR3sd3DrDqbPP/88Vkac9qQYPny4+dl0nHF7OmiWV9z3VHfO6K6nzZs3S2rTLMG4dGeyo13t9k6fPi1//PGHGafuULKnP6O+N7rjR/td6O+nPc20011B9vr37y8ff/xxmvzMAAD3o1nPBw4cMFlAcecamhmmdIduYvSzsWbNmqZHk/YG053AmtmuPUv1s18znFPCrVu3ZMeOHWZuo3O2hOj8RD355JPmq2Z8azbRzJkzTbaaZqOlBOv5ifvZnRQ6N9Q5YlJpjxD7rHetGmDNxHKkdOnSJgtfL5qlnhhrNpijXddHjx41XzW7X7/XLEJ9v3W+p1nrmhGYOXNmh4+xjjEuzdjSbH/t5abj0yx4nY876lnniI7F2sNM31tHdIe69fc7qfN5AACczdqvSvuB6ueo9tPUtR2txKNrJHHXCrQqkj3tV6rrDZpNFXc9QjO0dQ1M+8prn1CtgmS/vqOZZo7WtB544IF7XkNJbL5mNW/ePJMJpWslWh3HnmZOaaa79jHVeaWus9jTij72x3TeqfM/rVikayh3W0eLSzO2rO+BjklfV+fJFSpUMOtXaXWeteero/Ou62+agabn5e+//3bYrxdAwgiGIcPTyYH+Ia0fQFpGMC5NWU4sGKaPi8taAigkJCRFxqgLHSNHjox1TCcxjoJhmjJt/eDVRp2aTq6TH/2g06/6YR63RJH9B642P9eFBC0zo2UGjxw5YoJQd5OcBqi6EKULGPny5Ys38YpLU7p1LPpB7+g1tPyN0lTvuHTCoAstcenPn5RShfdKJzn62hpk1XOoZRn190h/V3Sh8G6sP4tO4uKm0Vt/Zp006f3iBsPS4vcRAODe7BdZ9DNaS+TpIsunn34aazOLspa1SWjuYc96n7Nnz8Z6bK5cuRIsj5dcZ86ckejo6HjjjDtP0YUHXSywlsjRhRHd7KSLD1quUcsfpwTrz5jURaK4wbC4G6ASowFF+2DYtWvXzNeEymBbj2sJn7vR8o4679E5o27MsW4U098Raylq+7mILnY1a9bMbLTSRSj7uab+TulGKPv7OwqGaWDSPnCn75mjeZAjb7zxhpmLapkg3SjliAY/lS6cAQCQUVjXjnRDsW6i1nWFp59+2qz1xOUo0KMBEp0r6eOtcz57+tked0P0xo0bzf0T2mByN1q+T0sFaplCDQDpZ7NuyNa1laS049i2bVus9SF7OofTgI+uq+kaStxgWFLXUBxtQtK2J9bWJ4ltUNf1IQ2S2c+50uI87927V0aPHm029uicU8tUx50XA0gegmHI8KwLARqYcSSh41Y6uYjLmr2kH2xJpX9w64ecdQHInn64Wms8605WRwESRzQLSYNIOqHQD74PPvjA9GWw7nZ25NlnnzW7TSZOnGh29+pukrgBl/tlXVRxtJMlofcnoUUr63FHCzWO3hvr+6OBwtSiAbiVK1ea7EBdrBk6dKg5rsFWrf/80UcfxdsBnZo/8738PgIAkJC4G3T0j3KdN1izqNIz3YWrEsvS1t5duhlH+7/aB+E0kKTBMM2gT6lg2P1w1OvKWTQApj09dAFLF650I5CeOw126RxUF540qGTfu+vLL780C12aaaeblPR77ZGhfcI0uKUZfHF7fdm/R3rRfl4a1NLfSX28znHv1vP1q6++Mu+jbrLSMSfEGtCj5yoAICNJSq/PuBs/HM2VNFijl4RoZreVrk3o3MpRZldS6GZv3bitQaHff//dVMGxBqW0J2zcnrLOWENJaBNS3GCYdYO6rjlpsEk3i+nc4+GHH7b1S02L86yBMw0O6hqiViDQgKNWNtDX1zmWZtOFh4ff9XkAxOb4rxMgA7GWublw4YLD2xM6ntL0D3i1YsWKVHn+unXrmq93K5VXuXJlqVevnlnU0p3e3bp1MzuyU5J1spGUXSjWnTPWMpIJ7ahOaFdzSrBOVnQSEVdCu6V1gqKLO1ri6dChQya7UBddNOvubgto6eFnBgAgsUUWvegf51r6RRcqnnvuObMRJKFFFv08vBvrfQoWLBhr4UIXC+LuZL1X1sWDxJ5vwoQJ5mvcoEqlSpVMKUfdVaylsB3NFRLbbGO9zT7AY/0ZnbEz1zqPsC4gxWU9ntDmorgbgbRhvZZlzpMnj0yZMsVcNKilpS+t1Re0xLVVxYoVZevWrWbTlWbS60KRLjJpGSdrGSD7+yc0j9cS0vraZcuWNXMsLTedEJ2HaTN5DditWrUqVqnzuLRyhLrXhT0AANI7R1lX1vmBfh5b53yOLvo5aqVzBc2isn523gstYa1Z3zrv03mWzil07qSf27o+5ew1FN2EFPccOMrostL5ns6Rx4wZY1qSaCsM+3LOqX2edSO83k9fV4NwuglJN2zrmK3rgwCSj2AYMjwNUOgfubt27TK7gBPq5ZAS9MMwoewcXXDR3SeaSZTc/ltJYU3vTkpGlGaH6c5c7aem36c0rfmsC0oaaHRU3tCeLp5onwpdJNKgUlzWiUGNGjUktVh3jztayIu7GOZIqVKlTFkCXeDRjDDdgZMYa+lI/d1zFIBLi58ZAICkfJ5rLzANROj8RrOfb9++Hes+WiZYJdZ31DpP0cCI/QYhXUDQjCL9LNTyLinBGlyx7saNS+eD1o1D9evXN4tE9hfrGK0Bs7gLGgk9r32GkX1wyXp+7mUzlO5Q1gWNpF7i9p/V4JHSnluOWOddCfUUi0srF2g2/O7du02wUTcMaYlu7VWmz6V9xbQXnD2d42nQTCsj6LxTyw+9//77tjFZ+3bdjZah1l3P+rq6E9oRXQR68cUXzRxU51KOdsPbs76XdwvIAQDgSrR0oq5dac+spNIN1Rq40ayu+6XrYrr5SOcUWoJZ6XwiKWsojrLmdR5p/VmctYaiGeladUmDUZrVnhbnWctO66afuJlrKjlltgHERjAMGZ7+8dyrVy+z+1V3TtjbuXOnrbl2StAMq4R2RutiwNtvv20WAtq2bWt20TqSlL4NcWnq87hx48z3jj4IHZW60awwDdok5f5KG4LqJamsae5agijujmRrrzMrbe6pH/j/93//FyuYqItKumBivU9q0RrSOknRXhT2i3zaDDVuo1Ol5SWtjd/jLvTpe3G3HcbaN6Rly5ZmQUgXbuxt2rTJjEMDdNY+JgAAOFOVKlXM5hnNyNGsaHvPPPOM+aoZ0oll22sJGf2M1OCafcBEG7srnaPdbUNPUkq9aCaWZi5pz1hHrEEunf/oRhZHF/0c18UZ+7I1VatWNV8T6kmqCzHWDTTW+yrdKawLFfq4uwUM4/58GtzS8oBJvcQNhln7ajhaUNF5jAaktGxRiRIl5H5oOUOd32pT+6Syli987LHHkvwYa3adtbSRPW1CrzuvtWeHBsKSEuCyzmutTesBAHAH+hmpfVJ13qLrLY42dGtveV33sNLNJkp71TvKdr9bBrxuNnKUqW6dOwYEBCT6+C5dupj5lM7P4m6K0TUVHavOMeP2C0sr+ro6V9aNNhoYS4vzrJuRdM1KN3rZ0yw7LWsN4N7QMwzpSmI9AjQYlNAHqKZfa2mfTz75xAQbtNyKBmN+/fVX01hbd6Ek1LMgOXTHqi4IdOzY0exI0R202shTL0obiGvQRz8IdVe07obR3SL6oa5BMA2OWBdKrI+JS3fCWFO19bn059CUaF2g0sWMt956667j1POkk4nkprRbXzMpdHFMd8DoYoeW0NHeEro4pTuD9b3Q4Jb159Aa0fozaHBOF5D0PdGg1G+//SbBwcEmIGXdWZ0adOFMJyk6Vl0Q0Sbxuptn8eLF5n2Im92mQVQtL6m7mfW8aLknzbTT8WsTVGsPscSMHz/e/A5oAFDT2jUgp4FU/Zn1d3HSpEm2kkMAADibbujRzyYNag0aNMiWVa2fk48//rj5DO3QoYPZbKObPuJ+5mmwQrOntZSMPQ1g6Gefzhm0nJ6Wl4lbtk+DUrqwoEGQu81zNLtLx6R9qXTHrGZvW2kpmenTp5uSf/rVWq7RUVDqp59+Mgsu1gx6bUqvATvtJ6a907TstD29TecCGmTTAJOVfpZreUB9vG7O0g0v2jQ+Ll3Y0fNqbRCfEj3DmjRpYuYpmnU3f/5800tCadDROlfR8pf2JZR0HqMLMzqH1Y1c9nRuZC0/bqU9KXQuo78Pb7zxhsPgnu6UttJ55Icffmh+Nj0f9ju4dQFJF8ocBecWLlxofrf0d0h/Lns6r9Y5ts6rdU6VWGlEe9bFtGbNmiXp/gAAuAqdb2lWt35+6hxO11vy5ctn1mu0kpH2uNJ5kHUDU6tWrcxcUOc7OrfQ9STN8Ndglla80YymuJty7OlraN9PfR2dX+i8QecbWn1A5wmvvPJKouPVz/8ff/xRevbsaeYB+lUDUBpk089+zQbX53emN9980wSidOOYBrU0Yz41z7OeMw166XNqvzKtYqCBN72fbsbSqlQAko9gGNIVLbOSEN0NklAwTD9sNBNLP5w0wKEBMS0dowE0LQGkwbC4f9zfC13g0QUFLYWjr6OLDdpc0xrY0ts0AKQ7Z3VhSHeu6qLIrVu3bOUCtReCLiollN6t6c72Kc/6M+tCj5Yu0qBSUvo+pAX9WTXrThd8dBe2Bh51UUQDT40aNbItyFiz97Qnyeeff27Oh/aR0AUvDYzp+5qcncb3She39PdEJyLffPONmVhpdpsu8OjY7WngShd89H3Q3daaEaaBPl2E0cdo5t/d6EKPTlR0kqO/K7oopL+Dbdq0MQt9SS0bBABAWihUqJAJnOhcRzcXffTRR7bb9HNeM6P0M1TnV/o5qBthdH6jc509e/aY7HkNUGkvJ3s6j9HPUv2jXQNUuiii2dM6J9LAiQa0dF6lgRj7PgyJ6d69u3ktXSCwD4ZpnwrdfKSblhIKhFk39GgwTH8uazBMn0d/dv2c189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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 = 0.0004  (180 benign flagged of 403,911)\n",
      "worst per-family recalls: {'MITM': 0.339, 'Uploading': 0.688, 'DDoS_HTTP': 0.694, 'Fingerprinting': 0.717, 'XSS': 0.731, 'Password': 0.732}\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": "e38d3c20",
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f2abbf89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best single-feature AUC = 0.7229  (feature: tcp_seq)\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) = 0.151\n",
      "TRAIN/TEST exact-row contamination       = 0.033  (single-feat grade A, contam grade A)\n",
      "==> data trust grade: A   (worse of the two; F = shortcut and/or heavy contamination)\n"
     ]
    },
    {
     "data": {
      "image/png": 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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": "8dd202b3",
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "433567d2",
   "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>0.993167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>de-duplicated (15% rows removed)</td>\n",
       "      <td>0.994870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>shortcut feature dropped (tcp_seq)</td>\n",
       "      <td>0.991590</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              setting  held_out_auc\n",
       "0                    headline (as-is)      0.993167\n",
       "1    de-duplicated (15% rows removed)      0.994870\n",
       "2  shortcut feature dropped (tcp_seq)      0.991590"
      ]
     },
     "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": "5485c0fe",
   "metadata": {},
   "source": [
    "## 12. Reproducibility & robustness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "de4d7f4e",
   "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 = 0.9929 +/- 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": "d200f186",
   "metadata": {},
   "source": [
    "## 13. Scientific conclusion\n",
    "\n",
    "Four learners reach very high AUC on Edge-IIoTset. The audit quantifies how much is shortcut (a dominant single feature) versus separability that is spread across many protocol fields. The per-family recall breakdown is the honest metric for an IoT detector meant to see many device types.\n",
    "\n",
    "**Validity ledger \u2014 read the headline against these printed numbers:** Majority-class baseline **accuracy**: **0.7280**. 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 **0.9929**). Strongest *single* feature: `tcp_seq` at AUC **0.7229**. The ablation refutes a single-feature story. Dropping that feature barely moves the AUC: **0.993167 \u2192 0.991590**. So the separability is **multi-feature**. That reflects how this corpus was generated, not one leaky column. De-duplication **raises** the AUC, to **0.994870**. That is not evidence the headline is safe. Collapsing duplicates removes the hardest rows. Identical feature vectors carrying conflicting labels get resolved to one label. The de-duplicated task is therefore *easier*, not cleaner. Data-trust grade: **A**. It is the worse of two independent sub-checks. Single-feature AUC 0.7229 scores **A**. Train/test exact-row overlap 0.033 scores **A**. Neither check flags a problem, so nothing here explains the score away. Operational false-positive rate at threshold 0.5: **0.0004**. Worst per-group recalls, exactly as printed: {`MITM`: 0.339, `Uploading`: 0.688, `DDoS_HTTP`: 0.694, `Fingerprinting`: 0.717, `XSS`: 0.731, `Password`: 0.732}. The weakest group sits at **0.339**, so the model misses most of it. That gap, not the aggregate score, is the operationally important result. **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. **Scope:** the split is random, not temporal or entity-grouped. Every number above therefore measures in-distribution separability only."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d287cec",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "1. Ferrag, M.A. et al. (2022). Edge-IIoTset: A New Comprehensive Realistic Cyber Security Dataset of IoT and IIoT Applications. *IEEE Access*, 10.\n",
    "2. Sommer, R. & Paxson, V. (2010). Outside the Closed World: On Using Machine Learning for Network Intrusion Detection. *IEEE S&P*.\n",
    "3. Apruzzese, G. et al. (2023). The role of machine learning in cybersecurity. *ACM DTRAP*."
   ]
  }
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