{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "654dbc49",
   "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 on one NetFlow corpus and test on the other three, measuring how far a detector transfers.\n",
    "\n",
    "### What you will learn\n",
    "\n",
    "1. Build a transfer matrix: fit on each corpus, then score on every corpus.\n",
    "2. Read the diagonal as the in-distribution number and the off-diagonal as the honest one.\n",
    "3. Recognise that a cell below 0.5 means the learned rule is inverted, not merely weak.\n",
    "4. Explain why a shared feature schema is what makes this comparison valid.\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 6: Digital Twins for Remediation Simulation** \u2014 Learning objective 2 (section 6.1) treats fidelity as a **promotion gate**. An in-distribution score is not a deployment estimate, which is the gate this notebook refuses to pass.\n",
    "- **Chapter 8: Federated Threat Intelligence** \u2014 Learning objective 2 (section 8.1) derives why **non-IID** site distributions produce *client drift*. A model fit on one site does not carry to another. That is what the transfer matrix here measures directly.\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": "9eb52f8d",
   "metadata": {},
   "source": [
    "# Does It Transfer? Cross-Dataset Intrusion Detection on One Schema\n",
    "### Train on one corpus, test on another - the check the other 35 notebooks flag as missing\n",
    "\n",
    "**Abstract:** Every other notebook in this series ends the same way. The score is high, and the honest next step is a cross-distribution test we did not run. This notebook runs it. Sarhan et al. re-featured four intrusion corpora into one 43-field NetFlow schema, so the columns line up and only the capture changes. We train a model on each corpus and test it on all four. The diagonal is the usual in-distribution number. The off-diagonal is what the model is worth on traffic it has never seen."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec0c6377",
   "metadata": {},
   "source": [
    "## 1. Research problem\n",
    "\n",
    "**Task:** measure how far an intrusion detector travels.\n",
    "\n",
    "A single held-out split answers a narrow question. It asks whether the model can separate attack from benign *inside one capture*. Deployment asks something harder. It asks whether the model still works on a different network, a different attack generator and a different year.\n",
    "\n",
    "The NF-v2 family was built for exactly this comparison. Four corpora, one schema. So we can hold the feature set fixed and vary only the source."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e1089dc",
   "metadata": {},
   "source": [
    "## 2. Literature review\n",
    "\n",
    "- **Sarhan, Layeghy & Portmann (2022)** - the standard NetFlow feature set and the NF-*-v2 corpora used here.\n",
    "- **Sommer & Paxson (2010)** - *Outside the Closed World*: the argument that in-distribution scores overstate operational value.\n",
    "- **Arp et al. (2022)** - *Dos and Don'ts of Machine Learning in Computer Security*: catalogues sampling and evaluation pitfalls, including this one.\n",
    "- **Apruzzese et al. (2023)** - where ML actually gets deployed in security.\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",
    "| In-distribution NIDS benchmarks | near-perfect scores are routine and say little about transfer |\n",
    "| Cross-dataset NIDS studies | report large drops; this notebook measures the drop directly |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67b9ef7c",
   "metadata": {},
   "source": [
    "## 3. Dataset provenance & honesty caveats\n",
    "\n",
    "| Property | Value |\n",
    "|---|---|\n",
    "| Source | Kaggle `dhoogla/nf{botiot,toniot,unswnb15,csecicids2018}v2` |\n",
    "| Corpora | 4, all on the same 43-field NetFlow v2 schema |\n",
    "| Rows | 400,000 stratified per corpus, 1.6M pooled |\n",
    "| Label | `Label` 0/1; `family` is set to the **source corpus** |\n",
    "| Access | Kaggle API token required, at `~/.kaggle/access_token` (see assignments/SETUP.md) |\n",
    "\n",
    "**Honestly:** each corpus keeps its own attack rate, which differs sharply between them. A transfer score therefore mixes two effects: a different feature distribution, and a different base rate. We report ROC-AUC, which is insensitive to the base rate, so the numbers isolate distribution shift rather than prevalence.\n",
    "\n",
    "### Before you run this: getting the data\n",
    "\n",
    "This notebook downloads its own data on the first run, then caches it. You do not fetch anything by hand.\n",
    "\n",
    "**Dataset:** Kaggle `dhoogla/nfbotiotv2` -> `/tmp/kg_nfbotiotv2`. It is about **485 MB** on disk.\n",
    "\n",
    "**One-time setup.** Sign in at kaggle.com, open **Settings**, and under **API** choose **Create New Token**. Kaggle hands you a `kaggle.json` file. This notebook does *not* read that file. It reads a plain key file, so convert it once:\n",
    "\n",
    "```bash\n",
    "mkdir -p ~/.kaggle\n",
    "python3 -c \"import json;print(json.load(open('kaggle.json'))['key'],end='')\" > ~/.kaggle/access_token\n",
    "chmod 600 ~/.kaggle/access_token\n",
    "```\n",
    "\n",
    "Never paste the token into a cell, a commit, or a screenshot. If it leaks, revoke it from the same Settings page.\n",
    "\n",
    "**If the loader fails:**\n",
    "\n",
    "- `FileNotFoundError: ~/.kaggle/access_token` - you created `kaggle.json` but not the key file. Run the command above.\n",
    "- `401 Unauthorized` - the key is wrong, or a trailing newline crept in.\n",
    "- `403 Forbidden` - open the dataset page on Kaggle while signed in, accept its terms, then re-run the cell.\n",
    "\n",
    "The cache sits under `/tmp`, which macOS clears on reboot. To keep it, move the folder somewhere durable and symlink it back. Do **not** edit the path in the code cell below: that changes a code cell and invalidates the stored outputs you are reviewing."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41d84dc4",
   "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": "d385e611",
   "metadata": {},
   "source": [
    "**Figure 4.1 \u2014 Solution design (methodology).**\n",
    "\n",
    "**Reading the final branch:** the diagram is the shared template for this series. Its rightmost outcome says a surviving score still *needs a cross-distribution test*. That is the one check this notebook does run. Section 9b is that test, so read the branch as discharged here, not outstanding.\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": "0dc21e71",
   "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": "79b389b6",
   "metadata": {},
   "source": [
    "**Figure 5.1 \u2014 Implementation architecture.**\n",
    "\n",
    "<img 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style=\"max-width:100%;height:auto;\" alt=\"Figure 5.1 \u2014 Implementation architecture.\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75b896e5",
   "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.\n",
    "\n",
    "**`family` here is the source corpus, not an attack family.** The code sets `family = corpus`. Its four values are `BoT-IoT`, `ToN-IoT`, `UNSW-NB15` and `CSE-CIC-IDS2018`. So every per-group breakdown in this notebook reads as a per-corpus breakdown. Read the series-wide phrase *attack family* that way wherever it appears below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "13a02797",
   "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": "846f37c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BoT-IoT             400,000 rows | attack rate 0.9957\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ToN-IoT             400,000 rows | attack rate 0.7258\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UNSW-NB15           400,000 rows | attack rate 0.0378\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CSE-CIC-IDS2018     400,000 rows | attack rate 0.1184\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pooled 1,600,000 flows x 41 shared features across 4 corpora\n"
     ]
    }
   ],
   "source": [
    "import os, glob\n",
    "from sklearn.model_selection import train_test_split as _tts\n",
    "# Four corpora, ONE schema. Sarhan et al. re-featured four different intrusion datasets into the\n",
    "# same 43-field NetFlow v2 layout. That is what makes train-on-one / test-on-another possible:\n",
    "# the columns line up, so the only thing that changes between train and test is the capture.\n",
    "os.environ.setdefault('KAGGLE_KEY', open(os.path.expanduser('~/.kaggle/access_token')).read().strip())\n",
    "SETS = {'BoT-IoT': 'dhoogla/nfbotiotv2', 'ToN-IoT': 'dhoogla/nftoniotv2',\n",
    "        'UNSW-NB15': 'dhoogla/nfunswnb15v2', 'CSE-CIC-IDS2018': 'dhoogla/nfcsecicids2018v2'}\n",
    "PER_SET = 400_000                      # stratified sample per corpus, so none dominates\n",
    "import kaggle\n",
    "frames = {}\n",
    "for name, ref in SETS.items():\n",
    "    dest = '/tmp/kg_' + ref.split('/')[-1]\n",
    "    if not glob.glob(dest + '/**/*.parquet', recursive=True):\n",
    "        kaggle.api.authenticate()\n",
    "        print(f'downloading {name} (one-time)...')\n",
    "        kaggle.api.dataset_download_files(ref, path=dest, unzip=True, quiet=True)\n",
    "    f = sorted(glob.glob(dest + '/**/*.parquet', recursive=True), key=os.path.getsize, reverse=True)[0]\n",
    "    d = pd.read_parquet(f)\n",
    "    d.columns = [str(c).strip() for c in d.columns]\n",
    "    if len(d) > PER_SET:               # stratified, so each corpus keeps its own attack rate\n",
    "        d, _ = _tts(d, train_size=PER_SET, random_state=0, stratify=d['Label'])\n",
    "    frames[name] = d.reset_index(drop=True)\n",
    "    print(f'{name:18} {len(d):>8,} rows | attack rate ' + f'{d[\"Label\"].mean():.4f}')\n",
    "# One shared feature list serves every corpus, because the schema is identical.\n",
    "DROPCOLS = ['Label', 'Attack', 'Dataset']\n",
    "feat = [c for c in frames['UNSW-NB15'].columns if c not in DROPCOLS]\n",
    "def _prep(d):\n",
    "    Xa = d[feat].apply(pd.to_numeric, errors='coerce')\n",
    "    Xa = Xa.replace([np.inf, -np.inf], np.nan).fillna(0.0).clip(-1e15, 1e15)\n",
    "    return Xa, d['Label'].astype(int).to_numpy()\n",
    "PREP = {k: _prep(v) for k, v in frames.items()}      # used by the transfer matrix below\n",
    "# df / X / y / family exist so the shared EDA and audit cells still run. 'family' is the SOURCE\n",
    "# CORPUS, so the per-group recall plot reads as per-corpus recall on the pooled split.\n",
    "df = pd.concat([v.assign(corpus=k) for k, v in frames.items()], ignore_index=True)\n",
    "df['y'] = df['Label'].astype(int)\n",
    "df['family'] = df['corpus']\n",
    "X = df[feat].apply(pd.to_numeric, errors='coerce')\n",
    "X = X.replace([np.inf, -np.inf], np.nan).fillna(0.0).clip(-1e15, 1e15)\n",
    "X = X.loc[:, X.nunique() > 1]; feat = list(X.columns)\n",
    "y = df['y'].to_numpy(); family = df['family'].to_numpy()\n",
    "assert len(df) >= 1_000_000, f'floor not met: {len(df):,}'\n",
    "print(f'pooled {len(df):,} flows x {len(feat)} shared features across {len(frames)} corpora')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e596a2d",
   "metadata": {},
   "source": [
    "## 7. Exploratory data analysis\n",
    "\n",
    "**Read the right-hand panel's title with care:** it says *Top attack families*, but the four bars are the four **source corpora**. That title is the series-wide default. Here `family` is set to the corpus, so the panel counts attack rows per corpus, not per attack type. Each corpus contributes the same number of rows, so the bar heights simply follow the per-corpus attack rates printed above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8d3eb40f",
   "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": "b9ed8b53",
   "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": "2b4a5c96",
   "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": "46864cf8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "materialized 1,600,000 rows | trained on 120,000 (stratified subsample) | held-out 400,000\n",
      "MAJORITY-CLASS BASELINE accuracy = 0.5306  (any model must beat THIS, not 0.5, to be interesting)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best model: XGBoost\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "</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>XGBoost</td>\n",
       "      <td>0.992857</td>\n",
       "      <td>0.999200</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LightGBM</td>\n",
       "      <td>0.991378</td>\n",
       "      <td>0.999162</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>0.993635</td>\n",
       "      <td>0.998457</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.864070</td>\n",
       "      <td>0.945969</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MajorityBaseline</td>\n",
       "      <td>0.530600</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             XGBoost  0.992857  0.999200      0.6\n",
       "1            LightGBM  0.991378  0.999162      1.3\n",
       "2        RandomForest  0.993635  0.998457      1.1\n",
       "3  LogisticRegression  0.864070  0.945969      0.8\n",
       "4    MajorityBaseline  0.530600  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": "7b88532c",
   "metadata": {},
   "source": [
    "## 9. Results\n",
    "\n",
    "Diagnostics for the pooled model. **Note the grouping:** in this notebook `family` is the **source corpus**, not an attack family. So the per-group recall below reads as *recall on each corpus when all four are pooled and split at random*. It is a warm-up for the transfer matrix, which is the real experiment and appears in the next section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b88de413",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1560x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "operational FALSE-POSITIVE RATE @0.5 = 0.0065  (1,378 benign flagged of 212,223)\n",
      "worst per-family recalls: {'CSE-CIC-IDS2018': 0.946, 'UNSW-NB15': 0.969, 'ToN-IoT': 0.99, 'BoT-IoT': 1.0}\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": "807352c0",
   "metadata": {},
   "source": [
    "## 9b. The transfer matrix\n",
    "\n",
    "This is the experiment. We fit one model per corpus, then score every model on every corpus.\n",
    "\n",
    "The **diagonal** is the familiar in-distribution number: train and test on the same capture. The **off-diagonal** is the honest one: the model meets a network it has never seen, with the feature set held constant.\n",
    "\n",
    "A cell near 0.5 means the model learned the capture, not the attack. A cell **below** 0.5 means the learned rule is inverted on the new corpus, which is worse than useless.\n",
    "\n",
    "*(Figure 4.1 above is the shared template for this series. Its final branch says a cross-distribution test is still owed. This notebook is that test, so read that branch as satisfied here rather than outstanding.)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e4db1d3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fitted on BoT-IoT (120,000 rows)\n",
      "fitted on ToN-IoT (120,000 rows)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fitted on UNSW-NB15 (120,000 rows)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fitted on CSE-CIC-IDS2018 (120,000 rows)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "in-distribution (diagonal)  mean 0.9966   min 0.9880\n",
      "cross-corpus (off-diagonal) mean 0.5434   min 0.3315   worse-than-random cells: 5 of 12\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>BoT-IoT</th>\n",
       "      <th>ToN-IoT</th>\n",
       "      <th>UNSW-NB15</th>\n",
       "      <th>CSE-CIC-IDS2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BoT-IoT</th>\n",
       "      <td>0.9999</td>\n",
       "      <td>0.3315</td>\n",
       "      <td>0.6120</td>\n",
       "      <td>0.7862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ToN-IoT</th>\n",
       "      <td>0.4910</td>\n",
       "      <td>0.9990</td>\n",
       "      <td>0.3822</td>\n",
       "      <td>0.5468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNSW-NB15</th>\n",
       "      <td>0.7879</td>\n",
       "      <td>0.3527</td>\n",
       "      <td>0.9995</td>\n",
       "      <td>0.3953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CSE-CIC-IDS2018</th>\n",
       "      <td>0.5814</td>\n",
       "      <td>0.6293</td>\n",
       "      <td>0.6243</td>\n",
       "      <td>0.9880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 BoT-IoT  ToN-IoT  UNSW-NB15  CSE-CIC-IDS2018\n",
       "BoT-IoT           0.9999   0.3315     0.6120           0.7862\n",
       "ToN-IoT           0.4910   0.9990     0.3822           0.5468\n",
       "UNSW-NB15         0.7879   0.3527     0.9995           0.3953\n",
       "CSE-CIC-IDS2018   0.5814   0.6293     0.6243           0.9880"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --- The transfer matrix: train on corpus i, test on corpus j ---\n",
    "import xgboost as xgb\n",
    "from sklearn.metrics import roc_auc_score\n",
    "names = list(PREP)\n",
    "TRAIN_N = 120_000                       # same subsample budget the other notebooks use\n",
    "# Split EACH corpus ONCE, up front. A model is fitted only on its corpus's train half, and the\n",
    "# diagonal is scored on that corpus's held-out half. Splitting after training would let training\n",
    "# rows reappear in the diagonal's test set and inflate it, flattering the very comparison this\n",
    "# notebook exists to make.\n",
    "SPLIT = {}\n",
    "for nm in names:\n",
    "    Xa, ya = PREP[nm]\n",
    "    Xtr_, Xte_, ytr_, yte_ = _tts(Xa, ya, test_size=0.25, random_state=RANDOM_STATE, stratify=ya)\n",
    "    if len(Xtr_) > TRAIN_N:\n",
    "        Xtr_, _, ytr_, _ = _tts(Xtr_, ytr_, train_size=TRAIN_N, random_state=RANDOM_STATE, stratify=ytr_)\n",
    "    SPLIT[nm] = (Xtr_, ytr_, Xte_, yte_)\n",
    "models = {}\n",
    "for src in names:                       # one model per SOURCE corpus, fitted on its train half only\n",
    "    Xtr_, ytr_, _, _ = SPLIT[src]\n",
    "    m = xgb.XGBClassifier(n_estimators=120, max_depth=6, tree_method='hist', n_jobs=-1,\n",
    "                          eval_metric='logloss', random_state=RANDOM_STATE)\n",
    "    models[src] = m.fit(Xtr_, ytr_)\n",
    "    print(f'fitted on {src} ({len(Xtr_):,} rows)')\n",
    "M = pd.DataFrame(index=names, columns=names, dtype=float)\n",
    "for src in names:\n",
    "    for tgt in names:\n",
    "        # diagonal: that corpus's own held-out half. off-diagonal: a corpus never trained on, so\n",
    "        # every row of it is unseen by construction.\n",
    "        Xe, ye = (SPLIT[tgt][2], SPLIT[tgt][3]) if src == tgt else PREP[tgt]\n",
    "        p = models[src].predict_proba(Xe)[:, 1]\n",
    "        M.loc[src, tgt] = roc_auc_score(ye, p)\n",
    "diag = np.diag(M.to_numpy().astype(float))\n",
    "off = M.to_numpy().astype(float)[~np.eye(len(names), dtype=bool)]\n",
    "print(f'\\nin-distribution (diagonal)  mean {diag.mean():.4f}   min {diag.min():.4f}')\n",
    "print(f'cross-corpus (off-diagonal) mean {off.mean():.4f}   min {off.min():.4f}   '\n",
    "      f'worse-than-random cells: {(off < 0.5).sum()} of {off.size}')\n",
    "M.round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5ba947f3",
   "metadata": {},
   "outputs": [
    {
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AFxDIBgAAyMPc3a5lZWd8yO69zx1WrFghd955p+zduzfV+XT6HXfcIatXr75h+wYAAADkFQSyAQAA8jA3l4PYbmZZpO3TTz+VW2+9VR577LFU59PpDRo0kI8//pjTCgAAAGQQNbKBNFgsFhm/ZId88ecBuRwZK/XLFpQPut0qtUrlT3W5sMhYuXX8z3LkQoREfNJFPD0SvzeKio036/vu7yNyPjxGapbMJ+90uUUaVyxsW/aVRdvk5+0nZdfJMGlQrqCsHHEf71MGjW37pDx150OSzy9Q/jm6R/rPmyg7Tx50OG+14uVkUqfnpEHZ6iZws2jr/+S5b9+T8OgI2zxPNG4jw1o8JmULFpdTYRfkjV9myYy1S23T8/sHyZvt+0vb2ndKPt9AWXtohwz65l3Ze+YI7106FegzQIIf7CjuAYESvXeXnH9ngsQc2u9w3uJvfSg+VWuIu3+AJERGSsT61XJhyjuSEHY5cQZ3dyn45EAJvL+teATnk7jzZ+XyvNkS9sN3ZrJXSFkp+NQg8a1dT9wDgyTu/Dm5smSBhM6dwfuVwd+P46atlmk/bJPL4dFya7ViMmVYS6lVsYjD+e95Zq6s2X5CvL08bOPeGtBMnulUP8W8H3yzUYa897uM6nm7vPp0UzPuzy3HpM2QxPfQKjomXvx9PeXS70N475xw/+8/V7i63M1GM6wHDhyYrnnbt28vU6ZMue77BAAAAOQ13J0AaZj02x6Z9ddBWfpcMzk16WG5vWJhaTt5lYRHxaa63AvfbJIqxYJTjB+1YKss33lafh96r5x9r4N0vDVEWr+/Uk5cuhY0rVgkUMY8WFuebFKR98cFQ1s8Jr1vbyf3fzhYCg97QP46sE1+HfS+BPj4pZg3yNdffhs0WTYd3SulRraT6uMelYqFS8msJ16xzdO+7t0yucsQeWrOGxL8/L3Sd84bMqXrUGlXp4ltnpk9Rpsgd90Jj5tt7jx1UJY9+4H4e/vyHqZD/m69JLjNw3JqSD853LqJRG3fIiXe+0zc/FK+Z+ri51PkaJfWcqhlYznW/SFx8/GVIiPG2qYHd+gqwQ92ktPDBsihFo3k3FvjpNBzI8Sv4R1muntQsERu2yTH+z5mpp95Zajk69LdDEi/d+dskBlLtsvP73eRs78MkjvqlJZWg7+V8IgYp8sM695IwlY8bxscBbH3HrlgAtm1kwXEm9QLSbKsDnUqFZHurWrxtiFbnT9/XkqUKJGueYsXLy7nzp277vsEAAAA5DUEsnOowMBA2+Dt7S0eHh5Jxv35559Olx07dqx5FPiFF15IMn7atGlSrly5VLfbrFkzefnll9O1jzVr1rTtj6+vr7i7uyfZxzlz5khe8NnK/TKkZTWpXTq/+Hl7yriHaktMXIIs2nzc6TJLt56QHScuy/Mtq6WY9u3fR+SF+6tJmUIB4uXpbtadz89LZq85ZJvniTsrSNu6paRQoPd1O668rH/TjvLO8jmy4+QBiYqNltFLpoq3h5c8XPfuFPPeWbGuFAwIltFLPpOYuFg5e+WivPrzDGlft6mULlDUzNP1thby9d+/ydqD200G6sp9m+T7zStlULPOZroGqzUTe+yP0+TC1csSHRcjLy78WErkK2SC4Ehb8MOPSOjXMyXm4L9iiYmWi59/KG6eXhLQ1PHTCDH795r5bBISxLvMtd9vXqXKSOS2zWZ9KmrLRok5dEB8Kid+JqN3bZew+XMl/tyZxPX9u0fCV/wmfvUb8nZlwCcLNssLjzWU2pWKiJ+vl4zv20RiYuNl4ap9Lp/H+PgE6TnuR3nnuXukYHDqXwSt23FCNu09I890vIX3LRWu18dOHJC24OBgOX36dLpOlc6n8wMAAADIGALZOVR4eLhtGD58uDRp0iTJOH2dmkKFCpnHVvftcz2YkJadO3fa9ke3VaZMmST7mFadyNzgckSMHL5wVRqUK2QbpyVC6oXkl63HQh0ucyE8WgZ//Y983rOheHqkDABY/huSj9t89FKW7//NKNg3QMoXLikbDu+yjYtPiJfNx/bJLSFVU8yv75B+8aP/WZngjbu71CtdJXEet8R57Lm7u0l9u/UlruHaPGadbm5Sv0zKbSIpLSXiVbK0RO3afm1kfLxE/7tHfKqk/DLIquDTg6X8svVS/pc1EtD0Hrn4xSe2aWGLvxPvMmXFWwPXbm7id2sjs42IdU46WPPwEL9bGkr0vj28PemkpUQOn7osDWpcy0L19HSXelWKyZa9iV8QOPLp95ulUIv3pcYjn8vIj1amyN5+c9Y6qVA6v7S/O/Hzl1YgvVn9MlKj/LXSTEiJGtnXn9a9nj9/frrm1fluu+22675PAAAAQF5DIDsXCg0Nlb59+0rp0qWlcOHC0qpVK9m7d2+SeapVqyadO3eW559/PlPbOnHihHTp0kWKFStmhkceeUROnjwpN4uw/8qH5PP3SjI+f4C3bVpyA+dslN5NKjqtof1gvVLyzi+75eC5cImOjZeJv+yWU5cj5UoapUqQPsF+AeZnaGR4kvGXIq7Yptlbc3C7XImKkNcfekZ8vXykZL4i8nKrXonr8k2cX2tmP3pbS2lSqZ54uHvIfdUamkxr6/oiYqJk+Z6/ZXy7vlI0qKDJ0H774YEmsG1dB5xzCwg0PxPCryQZn3AlzAS5nbn46fumLMiRLq1M/evYY9fqkcedPCERG9ZI6enfSIWVm6XEu5/KxakfSMwBx1/uFRn2irh5ekrovJm8VekUdjUxIz5/oE+S8QWCfCXsquPSIhOeaSp7v+sr5359Tr6e8JD8tv6QPPnaz7bpW/adkc9/2CIfvNAize1fuBwp8//YK0+TjZ0md9HManfXBrsv6ODcU089JZs2bZKhQ4eaJ3cc0fHDhg2TzZs3m3YcAAAAgIyhs8dc6PHHHzcZzxs3bjSPpo4aNUruu+8+2b17tynpYfXWW29J1apV5ddff5X7778/w9uJj4+Xtm3bmqC4ZnbrDZjeeD344IOyfv16U+7EFWfPnk1RG3L/fscdumW3YN/EAPbliKRB5tCrMVIqv3+K+b/ZcMQEqL988nan65zY+RZ5ZdF2uX/SH3I1Ol4erl9a7qlWTAolCwbBNWGRV83P/H5JA6AF/IPkRGjKmqSXI8PlgQ8Hy9sdBsqR1xaa5d9ZPleaVr5FzocnZt1rWZGC/sHyyaPDpWT+wrLxyB6ZunqRPHLrtbIX3WeOlYkdBso/I2eKh7u7fL76B9lzRjv0dJy5j2ssVxO/dNBOF+1pHeu4/0p/pCbuxDG5unqFlJj0mRx5+D6R+Dgp/MJL4lOxihx9tK2Z7l2xihR/Y7JGkiRs0bd2G3GXIi+OE59qNeXks73FEnGtVj1SFxyQ+DsrNDw66ZdGV6KkVBHHX0BoDW2rupWLyrvP3Sstn50nkVGxJptbS4pMGnyvFMrnuDa6vS8WbzNB84fTkbkNXG8PP/ywPPHEEzJp0iT55ZdfpFu3blKrVi0JCgqSK1euyPbt2+Xrr7+WXbt2SY8ePcz8AAAAADKGQHYuc+rUKVm6dKls2bLFdBakJk6cKF999ZUZ37VrV9u8pUqVkhEjRsiQIUNk27ZtGd7Whg0bZOvWrfLHH39Ivnz5zLhPPvlEihQpIn///bc0btzYpWP4+OOPZdy4cZIb5PP3lnKFAmTj4YvSuGLio+tx8Qmy9XiodGucst74bztPyd7TYRIydJF5HRufmJWlr9/qfIv0uKO8BPp6yaSu9c2gomLjpeqoJaZzR2ReWNRVOXT+pDQoV0PWHdphxmkWdb2QyvLl+muZn/a2HN8nLT941vb6wTpN5Gp0pG159dGq+Waw+r7fm/L7no221+euXJKes161vS4SVECGtXhMft97bR44lnA1XGJPHhff6rUkeufWxJEeHuJTuapc+WVJuk6b1tP2LFRY3AMDJeFyqAlMX/lxoQliK83EvvrnHxJwV/NrgWwvLyk27h3xLFJUTg7oaTLAkX75An2kXIl8snHXKbm9dikzLi4uQbb+e1a6t6qZrnVoiR6lvylPnA2X7QfOydNv/moGa/mSDbtOyaJV+2T710/alktIsMjURVvkyYfqmAA40jjPmah1TY3s9JsxY4bpP+TNN980/Y3Yl6TSZIACBQqYaZqVDQAAACDjCGTnMseOJQZlKlasaBvn5eUlZcuWlaNHj6aYX2+Wpk+fLh999JEEBFwrcaDz1qhRw/b6s88+S1HTWrdVsGBBc+NlX3tbX+vyrgay+/fvb8qeJM/Ibt++veRE/ZpVkvd+2yPNqhWVikUC5fUfd4mXh7u0v+VaZqHVO13qy7j2dWyv1x04L92mrpF1L98vhf/LuD58PtzU2S5dwF9OhkbKi/O3SLFgP3nMLjAeG5cg8RaLxCVYTIBHg93K18u1LPibzcf/WyBD7+smf+zdKAfOnTClQmLj42Th1lUO59c61ntOH5HouFi5vUIteb/zENNxowbFVaCPv5QrVEJ2njoogT5+8tSd7aVZlfrS8M0+tnVUKVbGlC/RgHbFIqVN9vYfe/+R3/f8fcOOOzcLW/iN5Hu0p0T+s15iTxyTAj37iSUuTq7+b3mKeb1Cyop3hcoS8fdasURcFa8y5aTQgBdMjW0NYquorZsksEUbubpyucSdOSVeZStIQJN7JHzZT2a6m5+fFH/zQ3Hz8JCTz/UhE9tF2sniu3M3SPPbykrFUvnltRlrTSe2jrKkz1y4Kpv3nZEm9UqLv6+X7Dp0XoZO/kPa3VXZvA4pFiRHFj+TZJlHRv1ganCP6JH0780v6w7K0dNh8tRD9Vzd9ZuyRraryyL9tLTIwIEDZfXq1eZJubCwMPP0nD7ddtddd4mfX9pPGwAAAABwjEB2LhMSEmJ+HjhwQOrWrWv+HRcXZwLL2tlicr6+viZju1+/fqbTSCtrx4xpbevSpUtmsAazL168aF472lZ6FS1a1Ay5xfMtq8mVqDhp9d5KCYuMlVvLFZQlz95tMquPXrgqdcf+LEuebSp3VS4qBQK8zWBVJCgxeF0qv58JXqs9p8Lk2bn/yJmwKAny9ZQH65WWX59vLj52Qeqnv9wgX649bHsdPOA78zNm6rWMezj3zrI5EuTjL8uf/dDUsd54ZLc88OEQk2UdUqCY7Hrla2n10RBZvT8x+7fPHQ+aMiF+3j4mm/u1X2bK9L8WXzv/vgHyVa+xUqFwSa1MIX/u3yJN3n1a9p9L/GJJ3VmhjqmRXTAgWC6EX5avNy6TV5ZM5W1Kp9C5M8TN319KTJ4m7gEBEr1np5x6/mmxREaKZ7HiEvLVYjk19GkToNbOG/M90kOKjBwvbh6eEn/5kkSs/0suTf/Itr4LH78rhZ4ZIqU++dKUKIkPC5WrK5fJxZmfmumBzVqI/22NJSE6Ssr9sNK2XNyZk3Kse878Ui0neuGxhnIlIkZaDvrG1My+rXpx+em9LhLo720CzbUenSY/vtdZmtQLkaiYOBn7+WrZe+SCxCdYpHihABPwfrn3HWZdHvoFX9HgJOvX34tB/t5SonDSUiWfLtgs7ZpUklJFk5ajAXICbXtpyTcdAAAAAGQdN4uzHmmQY+jjqZrZs3JlYrClTZs2Eh0dLXPmzDFZPjp93rx5smfPHlOLcezYsbJ8+XKzjNXdd99t6jJqVvbhw9cCpMk1a9bMZAxNmDDB1MiuX7++qfGoJUX0Unn66adNx5JaWsS+Rva0adPMMqmtOzU7d+4029k8tpXULJlYxgQ5n/emQ9m9C3DB/m2pf4mFnKf8pLbZvQvIoJ0Hz0mdbl/Ijh07TLmJ7GD92zrzj75SvmoRl9ZxaO856XnP1Gw9jtzA0VNx9lntGtzWDrrzWoa79RrLjuuj5hfv39DtAch9dvYenN27AADI4rYkhSVzoS+//FLKlStngsylS5c2b7wGrjWI7czkyZNNNnVGaKBa625r0LxSpUpSuXJlk/29ePFilzt6BAAA2VEj293FIW8FXq8XbZeVL1/e4aDTtF8TTT7o1KmTSQgAAAAAkHGUFskFNNPZntat1gxoZzQjWwd79erVMxnWabFmfduXF5k//1oHd848+eSTZgAAADmLm7iJu7hYI9vF5W42b7/9dqrZ1hEREebJOU0Q0E60161bJ1WqpKwlDwAAAMA5AtkAAABAJjt5TG8JkltvvVXGjx8vX331FeccAAAAyABKiwAAAOT50iKuD8g62ln2U089Jb///junFQAAAMggMrIBAADyMHf9z83d5WWRtbRudkb7LQEAAABAIBsAACBPc8tEZnVqdZ/hmsOHD5v+TgAAAABkDGk2AAAAwA1w7NgxmTp1qjRv3pzzDQAAAGQQpUUAAADyMHe3xDrZri6LtE2aNCnV6ZGRkbJ3715ZsmSJeT127FhOKwAAAJBBBLIBAADyMK2P7XKNbBeXu9kMHTo0zXn8/f2lZcuW8vrrr0uVKlVuyH4BAAAAeQmBbAAAgDyMGtnX36FDh1Kd7uvrK0WKFBF3d74YAAAAALItkP3rr7/K9OnT5eDBg3Lp0iWxWCwpbp4OHDiQ2c0AAAAAOVLZsmUzNL+2mQsUKHDd9gcAAADIizIVyJ44caK8+OKLUqxYMWnYsKHUrl076/YMAAAAmeYubmZwdVlkjejoaFm8eLHMmTNHfvnlF4mKiuLUAgAAADcqkD158mS555575KeffhIvL6/MrAoAAADXgXb06HpnjwSyM0OfVPz9999N8HrhwoUSFhZmSox069YtU+sFAAAAbkaZKtSnj0V26tSJIDYAAADwn3/++Ueef/55KVWqlOngcfbs2dKmTRv566+/5PTp0/LFF19kKrN7xIgRUrJkSfHz85NGjRrJsmXL0r38N998I7fffrsEBARI/vz55Y477pA//viD9w4AAAB5O5Ct5UT27t2bdXsDAACALOWmpUXc3F0adFmkj/YX8+qrr0q1atVMG3n+/Pny2GOPmcCxZmZ37NjRBJC1/5jM6Nmzp0yaNMmsW5+O9PDwkNatW8vq1avTXHbs2LHy6KOPSkhIiFnHhAkTpE6dOnLixAneZgAAAOTt0iIff/yxtGrVSm677TYekQQAAMiBKC1y/WmAesOGDVK4cGHztOK0adPkrrvuMtOystNz3ca8efNMPzVDhw4143r06CG1atWS4cOHy5o1a5wuu27dOhk/fry8++67MmTIkCzbJwAAACBXBLIfeeQRiYuLk8cff1yeeeYZKV26tMkKsadZJ1u3bs3sfgIAAMAFbppZ7ebaQ3iuLnezWb9+vZQvX95kOWsJEU/PTDWxndIsb21r9+3b1zbO19dX+vTpI6NGjZJjx46ZbGtH3n//fSlevLg899xzJkP86tWrEhgYeF32EwAAALgeMnV3UrBgQalcubI0bdpU6tevL0WLFpVChQolGXQeAAAAIK+aMmWKlChRQh5++GETLO7Xr5+sWLHCBIyz0ubNm6VKlSoSHBycZLyWMlFbtmxxuqx2OtmgQQP54IMPTIeTQUFBZp913wEAAIDcIFPpIitXrsy6PQEAAMD1qZHtYu4CNbLTp3///mY4dOiQzJkzR+bOnSuff/65CWo3b97cPKGY2drY6tSpUyb4nJx13MmTJ5120H7+/HnT2aR27DhmzBgpU6aMzJgxQwYNGmQ6btfge2rOnj0r586dSzJu//79mToeAAAAICOuz3OPAAAAyBEoLXLjaHmRl19+2Qz//POPCWpbO3vUQPfPP/8sDz74oNx3332mJEhGRUZGio+PT4rx1nXpdEfCw8PNzwsXLpga21oeUGk979q1a5tOH9MKZGvfOOPGjcvwPgMAAABZJdOFD+Pj42XWrFnSpUsXadSokRn037NnzzbTAAAAkH3c3dwzNbgqOjpaRowYISVLlhQ/Pz/TRly2bFmay5UrV86WwZx80JJ2ucWtt95qamZr3epff/1V7r//fhPU1kC2dgrpCj2Pel6Ti4qKsk13tpzSzGsNXlu5u7uboPbx48fl6NGjqW5bA/E7duxIMixatMil4wAAAABueEb25cuXTaP877//NnX2KlSoYMbrTcqCBQvkk08+MQ335HX8AAAAkLf17NnTdE44ePBgE4CeOXOmtG7d2tSOvuuuu5wup50SWjOIrY4cOWKynFu2bCm5jQaLW7RoYYZPP/1UfvjhB1N6xBVaQuTEiRMOS44o/dLAEe2zRrO28+fPn6Jjdu3jxlp+RMuNOKPzWecFAAAAcl1G9ksvvWQem/zwww9NzbxNmzaZQWvoaccxGzduNPMAAAAge7j9VyXbtcG1us4bNmwwJSzeeOMNmThxovTt29fUZi5btqwMHz481WXbt28v3bt3TzJYO0187LHHJLcJCwuT3r17y549e0wwWTOgNZjtinr16sm+ffvMOu2tX7/eNt1ZMF2naXs9JiYmyTRrXW3tABIAAADIs4HshQsX2jq30UcVrfTfzzzzjBk0MxsAAADZVyPb1bIiuqwrNBNbM381gG2lQdw+ffrI2rVrTbmNjNAMZq0/fccdd0huo3WrtQyfs44YM0LLgmjpvqlTp9rGaakR7bRRS7eEhISYcVomRAPn9jSAbi0JaF+SROt416hRw2k2NwAAAJAnSotohzFVq1Z1Or1atWpy8eLFzGwCAAAAmWDNrnZ1WbV///4U0zSD11mpic2bN0uVKlVSlJdr2LCh+bllyxZb0DUtuq7du3fn6qf8rBnlmaXB6s6dO8vIkSPNE5CVKlUygenDhw/L9OnTbfP16NFDVq1alWS72pnjtGnTZMCAASarW8uIfPnll6Zsy5IlS7Jk/wAAAIAcm5GtjefFixc7na7TKlasmJlNAAAAIJtpuY9atWolGT7++GOn82vNZq3nnJx1XEaykzVjOLeWFbketEN1rTuuQehnn31WYmNjZenSpdK0adNUl9MOH7W8S7du3eSLL76QYcOGmZIjP/74o7Rq1eqG7T8AAACQLRnZWlJk4MCBpuMebVBr5o3au3evfPDBB6bTR62VDQAAgOxh8rFdLBGiy6pFixaZBAZ7qdVU1nIaPj4+KcZreRHr9PRISEgwtbZvueUWqV69uuRG3t7ecvfdd0uBAgWyZH16DrXuuA7OrFy50uF4zaDXTjcBAACAmzKQrY81vvnmm/Lrr78mmaZ1sl955RVTJxsAAADZwy0Tta6ty2kQu2bNmuleTrN/tXZzclqT2To9PbQ8xokTJ2TIkCGSW2kAe8WKFdm9GwAAAMDNHchWY8eONVnZy5cvNzX2lPZIf99990nhwoWzYh8BAACQi2gJEQ1AOyo5otLbsaCWFdHyF48++qjkZBqg16cTNdg/aNAgp/PpE4ta71t/2neUDgAAAOAGBLKVBqy7du2aFasCAABAFtIOG/U/V5d1Rb169UwWclhYWJIOH9evX2+bnhbN6F6wYIE0a9Ys3YHv7DJ16lRTsmPXrl2pztemTRsZPny41KlTh6cWAQAAgBvZ2SMAAAByNjc3N1t5kYwPiTWyM6pTp04SHx9vArz2gekZM2ZIo0aNJCQkxIw7evSo7Nmzx+E6fvrpJwkNDc0VnTx+++230rFjR6lQoUKq82kn6J07d5avv/76hu0bAAAAkFdkSUY2AAAAcibt6NHlzh5dXE6D1RqwHTlypOlPRWtsz5o1Sw4fPizTp0+3zdejRw9TB9tisTgsK6IdRmqAOKfbvn17ugPud9xxhyxZsuS67xMAAACQ1xDIBgAAQJabPXu2jB49Wr788ku5dOmSKaexdOlSadq0aZrLakmSH3/80ZTiyJcvX45/d2JiYsTb2ztd8+p8jjrCBAAAAJA6AtkAAAB5mJt4mMHVZV3l6+srEydONIMzK1eudDhe62pHRkZKbqE1vHfs2JGueXW+nF7zGwAAAMiJqJENAACQh2mda2t5kYwOrtbIvtncd999JgNdy6ikRqfrfC1atLhh+wYAAADkFQSyAQAA8jA3cc/UgLSNGDFCoqKi5J577pH169c7nEfH33vvvWa+YcOGcVoBAACA61lapHz58hnOzNH5Dxw4kNH9AgAAAHKFChUqyLfffiuPPvqo6cxRX9euXVuCgoLkypUrppyItof9/f1l3rx5UrFixezeZQAAACBvB7LvvvvuFIHsjRs3ys6dO6VGjRpStWpVM27v3r2ya9cuqVWrltx6661Zu8cAAABIN2uZEFe4utzNSDum3LZtm7z11lumU8tFixbZpmlN7KeeekqGDx9ugtwAAAAArnMge+bMmUleawNdh2XLlplHJe3puC5dusirr77qwm4BAAAgK7jpfy4GpHVZpF+5cuXkk08+MYNmYoeFhZmOKzUzGwAAAEDmZCrN5pVXXpFBgwalCGIr7cRm4MCB8vLLL2dmEwAAAECu4+7ubgY6zAQAAAByQCD733//lUKFCjmdrtOojw0AAJB9tMNGV/+js8eMOXz4sPTv31/Kli1rMrFLly4t+fLlkzJlysiAAQPk0KFD1+ldBgAAAPK+TAWytaOaGTNmSHh4eIpp+jjlF198QR1AAACAbKRlRTIzIH1++OEHqVOnjnz66afi4eEh7dq1k27dupmfnp6eptyITtf5AAAAAFznGtnJTZgwQTp16iTVqlWTnj17SqVKlWyZ2rNmzZIzZ87Id999l5lNAAAAIBPc3dwy0dkjNbLTQzs5f+SRR0wCx2effSZNmjRJMc+ff/4pTz/9tHTt2lX++ecf01E6AAAAgBsUyG7fvr389NNPMmLECHn99deTTKtXr55Mnz5d7r///sxsAgAAAMjRtB1cuHBhWb16tRQsWNDhPBrc1mC2ZmW/8cYb8uWXX97w/QQA3JxOD+6Q3buAm9zv/mXEXSzSPOJYdu9KjnDKM0B+CiwvvUJ3ZK5URhYq/v73kucD2aply5ZmOH36tBw5csSM07qAxYsXz4r9AwAAQCa4ZaLWNTWy02fFihXy5JNPOg1iW+n03r17m2QPAAAA3JxKxF2VPqE7sns3cqVMB7KtNHBN8BoAACBncctEaRFdFmm7cOGClCtXLl2nqnz58mZ+AACAnCRe3MRDLNm9G8D1DWQfPXrUPE6pmSjnzp2TRYsWSdOmTeX8+fMyfvx46dWrl9xyyy2Z3QwAAABcQEb29adlRQ4dOpSueXU+nR8AACA7LQ6sKAXjoyTK3UOOeQZJxdhQuSPipKwIKCOnPQMk1s1d/BNipVb0eakVfe1L+DnB1aVazEU54+lv5vNLiJPGkSelfGyYbZ4tPkVkp29hiREPqRAbKgmaWGG5FiS/6uYpa/xLmRIb1gzlOyJOSIAlzrxe4R8icW7u4muJkwNe+UVTK26JOiMVYi7LqoAQs+2AhFi5++oxKR4f4fQY93gXkM2+xSTS3VNKxV6RgIQ4uejhKw+GH7AdS/2oM1I95qJtmc8K1JU2Vw5I6bhw8/qMh7+s9ythlvOyJEiVmEtya9Rp87yjBv/X+JWUQ975zP76JMRL3ehz5pxFu7nLn/6l5YRnkMS7uYl/Qpw0jDwlFWIvy0nPAFkSVEmeurTVrGejbzE54RVk9nG3TyGzXp3vrojjtucqz3r4yWr/0hLq4SP54qOlcswlWetfSvpd2io3E8/Mdmyj9f4SEhKkUaNGsn//fomLS7zorHUCr169yuOTAAAAyLOaNWtm2ruDBw9OtbzIxYsXzXzNmze/ofsHAADgyF6fAtIi/Ijce/WoxJlwsUhIbJg0jTgm3pYEOeoZJL8FlpN88TESEnfFttwen4LSMvywFI6PlO0+hU3wu1ToLvGWBPnXK79s9i0qrcIPSZH4CNnrXVD+8i8tlWIumWUTROTnwAqSPyFKHrm8R7Mu5H/+IfJLYHl5+Mq/tsDtYa9gs193RpyQI17B8ltAOTnilc8EvAskRMlav5KyMiBEuobtdXhsGizWQPL94YeldNwVOe4ZJMsCy0mROOeB7+RC3X1kaVAFufvqcROQv+ruJb8ElBcPSZD6UWdln3cBOeMZIJ3D9oqfJV4i3DzNPGqrT1GJdfOQRy/vFi9JkHB3L4lLpdzfWQ9/KRdzWbpd3i1X3L1kUVBlKep9VarFXDJB8Z8CK0jt6PPy0JWzEubuLb8Glr8pL+pM1RQfPny45M+fX/bt2ydfffWVWOy+XVFt2rQxndoAAAAge2hZkcwMSNuoUaNMuRB9KnHNmjUO59Hxd999t5lv5MiRnFYAAJDtysaGSZm4KyaE7SUW8RSLCZz6WBLMuLJxVyQk9ooc9wpMsly16ItSJD7SzFM9+qIJ2F7y8DXT9voUNBnbmintISI1Yi5KwfjIJAHbCx6+0iTihPhIgtlWk4jjct7DT855+NvmKx531WQla2tUs729LfEmIF0oIcqM08zoyx6+Eu0ktPmvdwEp99/x6Rz6U4P0GbHTp5A5R5ViQ806ghJipV70WROcV9qBpWau67HHi4i/Jc6cF+u0KDcPk0Et/y1bICHa6bYCEmJMNreWd8mfECOl4sLlnGfi+dBAvq6vftQZM13XUyv6nNyMMpWR/b///U9eeeUVKVKkiMNaf2XKlJETJ05kZhMAAADIBK1z7UaN7OuqRo0aMnfuXOnRo4d5WlHrZdetW1eCgoLkypUrsm3bNlNSxNfX1yR/1KxZ8/ruEAAAQDoExcckea0lLTb4FTeZz1qOQ0tmx7m5iXeMhmmv0ZIjVpptrDSgqzQjuVzs5aTbSbi2HZ3ua4kXH8u1dVpfa9Zysf9GaykOe7od++16Wqzb9TDB8OR0OwXio1LsR5RH+kOhlz185KRnoMzIV8s2zuJmTouh5T2i3DxN6ZFQD18pFndVGkSeNsHsulFnzXyr/EPMcWmpEi0tks/uXNizllWxPz4tzaIi3LwkMCH2v5z5a8dyM8pUIFtLivj7X/u2JDmtme3jk/jNA5Aey27LJ3sqO38kFznLhrc2ZfcuwAWVWhTgvOUyh1/+Nbt3ARl0Ovxa1kl2c7MkDq4ui/Tp0KGD1KtXT95++21ZunSp6TfGqkSJEvLkk0/KsGHDpFKlSpxSAACQIyTv1nubbxGT/ftA+CHJlxBtpv8SUE4sKeZ0TmtXX3H3TjJOX2s9buv0aDcPM1iD2VH/vdZgbVbR7YQn24/kr70l3tS2tq/dbU/rf1eKCZVmEcccbkOX1CxqHWLFXf72Ky6/BpaT7qaciEUaRJ0xgx7fav9SpgRL+yv7M3ws/hY9Fl3jtfcs+Tm+WWTqedH69evLjz/+6HCa1sqeN2+eNG7cODObAAAAAHKFChUqyKeffirHjx+X0NBQOXbsmPmpTyh+9tlnBLEBAECOFuPmIR4Wi+lkUYOmB7zyyXGvoAyto0rMRVN6QztJ1Dzp3d4F5aKHn2160fgIkym92q+UxIi7KQ2iQV6tt601tbOKZktrnW3tyFL3Q+t9H012LIXjImW/d35Tg1r3RTOr7dWMPi8HvfOZ86DZ6rqey+7eZl3qhGegnPPwM9O0braXJd6WCKLbvuiuxVMSs6s9LZYkHV5mhJY30WIvm32Lmm2FunvLDp+bs/PwTGVka32/tm3byjPPPCNdu3Y1486cOSPLly+X119/XXbv3i1TpkzJqn0FAABARmmD2cHjluleFi4JDg42g7PyfFpPGwAAICepE3VWLgb4ytx81cXDkiDlYy+nKBOSlsoxoXLVzVuWBZaVWPEwnSTar0MzarUjyDX+JeXrfNXMuJJxV02njFnZO4uuU+twa5Bcy6SUig032dWa2WylpT5WBpSROflqmOzrxpEn5V+fa1UCisZHSpsrB2WjX3FZ7V/aBJO1pEeN6PNmeqSbp/zlX9JkR2uQulB8lLS4ethM03HaIWWEu6f5cqBYfITc7SSzOy1aOkXP2Wr/UiaYnT8+2tQp1/262WQqkN2qVSuZOXOmPPfcczJ16lQzrnv37qbTR224z549m0Y6AABAdtIgtsuBbBeXg0OLFy+Wt956S9atWyfx8UlrTQIAANxID4YfSDHOzxJvAqapeSxsd4px/S5ttf1bS1/cEn3WDM4EWmKl5dUjTqc3dxDw1XId9rTDQ/vtOqKdTupg9WtAOdPpon1d6jbhB50ei9IAdPJ5rLQTSB0cqR193gzOguz227kt6owZUjsHuh8dr/xre73Np7AE3oR1sjMVyFaPP/64qQn422+/yf79+03d7IoVK8r9999vOrgBAAAA8rply5bJ5MmT5cCBA1KgQAHp3LmzDBkyxEzTetkvv/yyeVqxUKFCMmbMmOzeXQAAgDzvoFc+KR17xZT9OOSV39T/busggJ8bHPcMNJnYAZZYOe/hZ+qZV4++FqS/WWQ6kK0CAgLk4YcfzopVAQAAICuRkX3d/fTTT9KuXTvzVGLhwoVNcsf69evl7NmzEhERIR9++KFJ9Pjoo4+kZ8+e4uvre/13CgAA4Cangev/+ZeWeDctCRJrOm3UbOjcKNTDx3QWqXXMfRPiTAmXelHOs97zqiwJZF+5ckWOHDkily5dMg345KgBCAAAkE2okX3dvf3221KyZEmTlV2tWjW5fPmy6T/mvffeEzc3N9NnTL9+/cTDw+P67wwAAACclijJrWpFXzDDzS5TgewLFy7IwIEDZcGCBbY6fxrI1ga7/b+pAQgAAJCNGdkJ1Mi+njZv3iwjRowwQWyVL18+mTBhgjRo0EDGjRsn/fv3v67bBwAAAG4GmQpkP/XUU7JkyRJ59tlnpUmTJqYeIAAAAHAz0acTy5Ytm2Sc9bUGswEAAABkcyBbO3jUTmz0cUoAAADkQJQWuSGsTyQmf+3t7X1jdgAAAADI4zIVyPb395dy5cpl3d4AAAAga9HZ4w0xe/ZsWbdune11VFSUrT72okWLksyr4ydPnix5hR7f33//7XR68eLF5YEHHrC9Pn36tPzyyy9prlc7xrQ3c+ZM27+bnQ13uMyW/H4S6n2tFnm9S5GSPzaxBKIzhwO8zWBV7mqMGVIT6uUhWwr42V7nj4mXeqGRkpaVRQOTvHZ2HPY4Jt4nrj3XPk/2vzOs9HeR/k6y0t9F+jspNfXq1TOD7TO5ZYsZUnM9fu85k55jigmsmOR11ZiLUjXmku31Xu8Cste7YKrbKRQfKXdGnrS9Pu/hK2v8SqW5fw+GH0jyenGyfXHkjsgTUjg+yvb6L7+ScsHj2u9cRzgm3ieuvcx9nrztft/cqN97J06ckBsayO7evbssXLiQun8AAAA5FYHsG0KfVNQhueRB7LwYyD558qTExcWle34N8mtH8Rllv0xRJ/N4JSTteF6D2EWjUw9kn/NJWkM+IC4hzWUcbTejy6j0LMMxJeJ94trL6OfJ0e8Z/f1jTwMtaf0+Sp68FxoamuHfYVnxey+1dad5TF5Jg/4l45IG/a+4e8upZPOkJcbNI8PLqPQsExOVtHNkDbqltRzH5ORc8j5x7UWl8/Nk93vjRv3eO3v2rNzQQHanTp1k1apVJuLet29fCQkJcdgbe/369TOzGQAAACDHSnC1M808omTJklKqlPOsPPtMQeXr65uipnh62C/z9+njDueJdXdLkTmdlque7ilen/VJfbnk69XtprWMI+lZhmPifeLac+3z1KB46RTz6O+f1H4/OZI/f/4Ur9P6HXY9fu85k55jitm/M8nroISYFK9LxIanmZFtz9sSn+YyjqRnGV13att2hGNyfK54n7j2vNP5efKuVPOG/97z9Mx4WNrNYtHCia5xd3d3WhdQ6ap1fHx8xrMTcHPZuXOn1KpVS95e2lVCKqf+SBNyjoqtl2f3LsAFDVvQMW9uc3h/dHbvAjJoX3iktFy3V3bs2CE1a15rFGbH39YdG96TmtVDXFvH7mNSq+GQbD0O5Fy2aywbro+aX7x/Q7cHIPfZ2Xtwdu9CjnF6cIfs3gUAOVzx97/PFW3JTGVkz5gxIzOLAwAA4HqjtAgAAACAPCBTgewnnngi6/YEAAAAWY9ANgAAAIA8IGlBOAAAAAAAAAAAcnNGdu/evU3N66lTp5pOHfV1WnT+6dOnZ2YfAQAA4CrtDkWzsl1dFgAAAAByWyD7jz/+MB08as/sGsjW1446ebSX1nQAAABcPxZLvBlcXRYAAAAAcl0g+/Dhw6m+BgAAAAAAAAAgR3X2CAAAgBxOy4okuFpaxMXlRCQ6OlpeeeUV+fLLL+XSpUtSp04dmTBhgrRo0SJdy3/zzTfy/vvvy7Zt28TLy0tq1Khhlr/nnnskp9u9e7fMmDFDDh48aI7dkqxEiz6x+Pvvv2fb/gEAAAC5EYFsAACAvCybamT37NlT5s+fL4MHD5bKlSvLzJkzpXXr1rJixQq56667Ul127NixMn78eOnUqZNZT2xsrOzYsUNOnDghOZ0G7nv16mWC71WrVpUCBQqkmCd5YBsAAADADQhk//zzzzJp0iTZtGmTXL582WHDPD6e+ooAAADZQoPYLgeyXVtuw4YNMm/ePJk4caIMHTrUjOvRo4fUqlVLhg8fLmvWrHG67Lp160wQ+91335UhQ4ZIbqNB+FtuucW0kQsXLpzduwMAAADkGe6ZWXjBggXStm1bOXPmjHTt2tV0Avnoo4+af/v5+ZlHSPWRUgAAANw8NBNbOwbv27evbZyvr6/06dNH1q5dK8eOHXO6rJYTKV68uDz33HMmQSI8PFxyk5MnT0rv3r0JYgMAAAA5KZD9xhtvSMOGDWXz5s0ybtw4M04b7nPmzDGPf546dUrKly+fVfsKAAAAVzOyXR1EZP/+/bJz584kw9mzZ51uUtuGVapUkeDg4CTjtd2otmzZ4nRZrR3doEED+eCDD6RIkSISFBQkJUqUkClTpuSK914TOTSYDQAAACAHBbJ37dplsq8148bTM7FKidYwVOXKlZP+/fvLW2+9lTV7CgAAANdrZLs0JJaMa9++vSkLYj98/PHHTjepyQwafE7OOs5ZoFc7Rjx//rz89ddfMnr0aHnxxRdNp4/16tWTQYMGyWeffZbjrwAtuTd9+vRUy6cAAAAAuME1sv39/cXb29v8O3/+/OLj42NuXKyKFSsmhw4dyswmAAAAkBkakE7IXI3sRYsWSaVKlZJM0mxpZyIjI027MDktL2Kd7oi1jMiFCxdMje1HHnnEvNZOH2vXri0TJkyQfv36SU6mSRz58uWTJk2aSI0aNaRMmTIm6cOem5ub/PDDD9m2jwAAAMBNF8jWntg1K9tKs2W0p/bu3btLXFyczJ071zTeAQAAkHtpELtmzZrpnl/7SomOjk4xPioqyjbd2XLKy8vLBK+t3N3dTVB7zJgxcvTo0Rzdvty2bZsJVOs+amDevq1spdMBAAAA3MBA9sMPP2zqF77zzjsm6+all16Shx56yGRnawP96tWr8sUXX2RmEwAAAMgMu1rXLi3rAi0hcuLEiRTjrU/ulSxZ0uFyBQsWNFnb2pZMnsVctGhRW/mRnBzIPnz4cHbvAgAAAJAnZapG9tChQ01WjPXR0bZt28rKlSvlqaeeMo99amc9PXv2zKp9BQAAQDbUyM4ofUpv3759EhYWlmT8+vXrbdMd0cxrnXbu3DmJiYlJMs1aVzu1kiYAAAAA8i6XA9n6uOjixYvN45P2tB7ge++9Z7K0mzdvnhX7CAAAAFe5HMR2PZNby4LEx8fL1KlTk7QdZ8yYIY0aNZKQkBAzThMi9uzZk2RZLSGiy86aNStJSZI5c+aYmtPOsrlzoitXrsjx48fNcSYfAAAAANyg0iLayWPnzp1l8uTJUqdOHVdXAwAAgDxGg9XaThw5cqScPXvW1NjWwLSW3Zg+fbptvh49esiqVavEYpf5rU/1TZs2TQYMGGCyurWMiPbBcuTIEVmyZInkBp988olMmjRJDh486HQeDdYDAAAAuAGBbK2BXblyZTl//ryrqwAAAMD1lpCQOLi6rItmz54to0ePNkForWutiQ9Lly6Vpk2bprqcdvj4xx9/yPDhw01fK9rnipYb+fHHH+X++++XnO7TTz81QXjd1969e5s+ZIYMGWJqf8+cOVOKFSsmzz77bHbvJgAAAHBzdfY4atQoef75503GTdWqVbNurwAAAJBF/quR7eqyLtLA7cSJE83gjPat4oh27KhB39zoww8/NEHsn3/+WS5cuGAC2W3atJF77rnHBOdvu+02Mx4AAADADQxkr1u3TgoVKiS1atWSZs2aSbly5UwWTfLMbS0/AgAAAOR1Bw4cMBnZysvLy/y0dlyZL18+efLJJ+Xjjz+WF154IVv3EwAAALipAtlTpkyx/fv33393OA+BbAAAgGyUiU4bXc/kvnlpsDouLs78Ozg4WPz9/eXYsWO26UFBQXL69Ols3EMAAADgJgxkJ2SibiIAAADybo3sm5U+qbh161bb68aNG5vOH1u3bm3azp999plUqVIlW/cRAAAAyI3cM7Pw0aNHJTIy0ul0nabzAAAAIJtoLDrB4uLAu5ZR3bt3lx07dkh0dLR5PW7cONm9e7eUKVPGlOHbu3evTJgwgRMLAAAA3MhAdvny5WXhwoVOpy9evNjMAwAAANwMevXqJevXrxcfHx/z+s4775SdO3fKpEmTTL8x27ZtM50/AgAAALiBpUUsltR7so+NjRV390zFygEAAJAZWufa1RIh1MjOEhUqVJDnnnsua1YGAAAA3KQyHMgOCwuT0NBQ2+sLFy44LB+i88ybN09KlCiR+b0EAACAa6iRnS3WrVsnK1askLNnz0r//v2lcuXKEhERIXv27DE1sgMDA7NnxwAAAICbJZD93nvvyfjx482/3dzcZPDgwWZwlrFNDUAAAIBsZK137eqyyJCYmBjp2rWr/PDDD6YtrO3ldu3amUC2PqnYsmVLGTJkiLz00kucWQAAAOB6BrK18a0ZJNowHz58uDz66KNSv379JPNogz0gIEBuvfVWue222zK6CQAAACBXGj16tCxdulQ++eQTad68uVStWtU2zdfXVzp37myC3ASyAQAAgOscyL799tvNoK5evSodO3aUWrVqZXQ1AAAAuBG0TxOXa2STkZ1RX3/9tTzzzDPSt29fU4IvuerVq8t3333n2vsBAAAA3MQy1dnjmDFjsm5PAAAAkPWokX1DaU3s2rVrO53u4eFhamUDAAAAyBj3DM4PAAAAwImQkBDToaMzf/31l1SqVInzBwAAAGQQgWwAAICbobNHVwdkSLdu3eSzzz6TtWvXJuk/Rn3++efy7bffSo8ePTirAAAAQAYRyAYAALgZamS7MlAjO8O0E8c77rhDmjZtajp71CD2kCFDpEyZMtKvXz954IEHzGtXRUdHy4gRI6RkyZLi5+cnjRo1kmXLlmV4PS1atDD7NnDgQJf3BQAAALiRCGQDAADkZWRk31De3t7yyy+/yIwZM6RChQpSrVo1E3yuU6eOzJw5U5YsWWLqZLuqZ8+eMmnSJHnsscdk8uTJZl2tW7eW1atXp3sd33//fZKMcQAAACDPd/YIAAAAICnNdO7evbsZstKGDRtk3rx5MnHiRBk6dKgZp2VKatWqJcOHD5c1a9akuY6oqCh54YUXTFb3K6+8wlsHAACAXINANpAGi8Ui8z/8W/74dpdEhsdI+ZpFpPeYphJSpVCKec+fvCJD23ydZFx8bILEx1vk0796SnBBP0mIT5DvPtggqxfvk/DQaClQNEBa96or93WtaVtm659H5bvJG+TkoVDx8vGQek3LyOMv3imB+X15v9Kp5HODpEiXLuIRFCgRO3bKkTHjJPLffx3OW+mzTySgZk3xCAyQ+IgIufy/P+XYm29LfGiomR7UqKFUm/OlxF+9eu19vXJFtt51d7rXgbSNbfukPHXnQ5LPL1D+ObpH+s+bKDtPHnQ4b7Xi5WRSp+ekQdnq4u7mJou2/k+e+/Y9CY+OsM3zROM2MqzFY1K2YHE5FXZB3vhllsxYu9Q2Pb9/kLzZvr+0rX2n5PMNlLWHdsigb96VvWeO8HZlQL4eT0tg6w7i7h8oMf/ulosfvi6xhw84nLfI+PfFu3J1cffzl4SoSInauEYuffaeJFy5nDiDm5sEd3pcAtt0FI+ChSXu9AkJnf6hRK7/07aOQsPGScA9rcQSG2sbd2XxNxI67QPeN2esZUJc4epyuC7mz59vMrD79u1rG+fr6yt9+vSRUaNGybFjx0xnk6l5++23JSEhwQTCCWQDAAAgN6G0SCrKlSsn06ZNSzG+WbNm8vLLL8vhw4dNxo3eMEREXAuexMXFmfErV65M8gin1jDMnz+/5MuXT6pXr27WofQRUy8vLwkLC7PNf/DgQbOO3r17J9n2888/L3feeafD/U3v/ljnCwgIkMDAQAkODpa6devKN998k2R9un+33HKLeUT2rrvuSrE9XZ+uR9dhHUqXLi15zdLpW2Tlgt0ycno7mbqut1SpX1ze6LNUoq5eC6JYFS4ZJDM3900y3NKsrAlEaxBb/TZ3h/z+zS4Z9mkbmbH5KXny1btl9uurZdvqo2Z62MVIebf/z9K4VUWZtqG3TFzaVc4eC5MZr14L5CB1xZ/sI0U6dZR9vfrI5gaNJXzTJqkyY7q4+/s7nP/Ee+/LtntbyKZ6t8qOB9qIu6+vlJswPsV8m+o3kE1165vBPoidkXXAsaEtHpPet7eT+z8cLIWHPSB/Hdgmvw56XwJ8Ej839oJ8/eW3QZNl09G9UmpkO6k+7lGpWLiUzHriWmZh+7p3y+QuQ+SpOW9I8PP3St85b8iUrkOlXZ0mtnlm9hhtgtx1Jzxutrnz1EFZ9uwH4u/NF0bpFdy5hwTe/5CcfbG/HO/UXKJ3bpGib3wsbr4p3zcVOvNjOfnEg3KsfRM59WRHcfP2lYJDRl97bx/uJkEPdZVzY4aYeS7PnS5Fxrwr3pWqJVnP1ZW/ybEH77QNBLHTYEnIRI1sAtmu0DIf2obTNqO2sbSsiP2g41yxefNmqVKlimm72WvYsKH5uWXLllSXP3r0qLz55pvy1ltvmfraGXH27FnZuXNnkmH//v0uHAUAAADgGgLZWUAf0dQbAme0BqE+Wjpy5Eg5f/68Gb777jtzI6L0Jid54Fs77dHHRJcvX55kXTq+ZcuWmdofq61bt0p4eLiEhobK6NGjTa3FXbt22aZXrFhRxo8fnyTrxxFdXtejw/HjxyWvWTZ3h7TtXU/KVC0k3r6e0uW5RhIXGy9/L3OcKWrv4plw2bTiiLTsVss27syRy1L11hJmfapGw1JSulJBObzrvHl94XS4xMbEyz1daoi7h7sJgDduVUkO7zp3HY8ybyn6WDc5Pe0Lidy3TyzR0XL8vcni5u0lBVq2cDh/5J69Zj6bhATxrVA+Q9vMinXczPo37SjvLJ8jO04ekKjYaBm9ZKp4e3jJw3WTfmGg7qxYVwoGBMvoJZ9JTFysnL1yUV79eYa0r9tUShcoaubpelsL+frv32Ttwe3mqYqV+zbJ95tXyqBmnc10DVZrJvbYH6fJhauXJTouRl5c+LGUyFfIBMGRPkHtukjY/NkSe3i/WGKiTaDazdNL/O+6x+H8sQf3mflsLAniVbqs7WVAs/vlypLvJPbIQfMZilj5q0Tv3SGB7RLfN7hG+2vUz4FrA2c9o7R+9d13320SBDRJoWDBglKoUKEkg45zxalTp6REiRIpxlvHnTx5MtXltaSIJil07do1w9v++OOPTdvUfmjfvn2G1wMAAAC4ikB2Fnj11VdNrUJ9nNMRrVdYqVIl09j39PQ02dfa+NeahiooKEgaN26cpMd5/fegQYMkNjZW9u7da7t52bFjh+llPjP7k5y7u7t06tTJ7Idm11j16tVL2rVrJ4ULF5abVcSVaDl34opUrFPMNs7D013K1Sgsh3cnBp5T8/u8XVKoRKDUbVrGNu6eR2rKqcOhcmjXOUlIsMiOtcdNxrV1nnLVC0v95uVk2dc7TcA89FyErP3xX2nYssJ1Osq8xSMwUHxCSkv4tm3XRsbHS8SuXeJfo7rT5UoPfV7qb9kk9Tf9Lflb3CcnP/woxTx1V/4u9db9JVVnz5Sghg1cWgdSCvYNkPKFS8qGw9e+SItPiJfNx/bJLSFVU8zv9l/9Wf3PSsuL6O+yeqUTvyB0c0ucx567u5vUt1tf4hquzWPW6eYm9cuk3CZScvMPFM8SpSR6z7W/G5IQLzEH9oh3xaQZ1Pby9x4kIT+slpCF/xO/O5rJ5a8+s1upeeOSbcg9RUa2f+OmUnrBCik5a7EUfO4lcc9XgLcIOYa2wfTpOQ0qb9q0SVasWOFwcEVkZKT4+PikGK/lRazTndFtLliwQN5//32Xtt2/f3/TDrUfFi1a5NK6AAAAAFcQyM4C9913n8mSHjZsmMPpTZo0MZnOTz/9tCkj4ihbRpe3BrK1buEff/xh1nvvvffaxmt2tpYlsT4+6ur+JKelR7TjIM3kbtAgZXAuLeXLl5dixYqZfV21alWeejRVa2KrgGDvJOMDgn1s05zRIPQf3+0yta/tA2pFSwdJnTtD5KWO8+XxWp/Km08tlS6DG0nZaolfGOi8d3eoKr9+uV2eqDtVnrlrpnj7eUr7p2+9LseY12hNbBVvV6rHvL4cZoLczhx/Z5JsqlfflAc588VMiTp0yDYt8sBB2dH2Qdna7F7Zdm9Lubzqf6ZUiV/1auleB5wL9gswP0Mjw5OMvxRxxTbN3pqD2+VKVIS8/tAz4uvlIyXzFZGXW/VKXJdv4vxaM/vR21pKk0r1xMPdQ+6r1tBkWlvXFxETJcv3/C3j2/WVokEFTYb22w8PNIFt6zqQOveAxPOUcPVKkvEJV8LE7b9pjoR+8aEce+guOfHEg3JlwVcSezyxrJJ5X9askKB2ncWrQhX91tDUwvapVkvcA659dq8smicnej8sxzs2l7MjB4hnidJSdLxrgbmbhqtlRTJTW/smpuXd9Ck3bbNlNS0HEm3/9M9/tA1nne6srffss8/K448/7lJbTxUtWlRq1qyZZNBEDQAAAOBGIZCdRd555x2TlaI1EZPTwLOO1ywZvYnQOtLa+NegtpUGnjXzWrOoN27caGppV6hQwQSlrYFs/dm8eXOT1Z2Z/bGqX7++2Y6/v7+5sdFMbq0Lnl7VqlUztRgPHTpkAtGtWrWS+++/P836jLnp0VS/wMQA9tWwpEHrq2HRtmnO/L3skJmvWcekWcAzxv8pezaekkm/dJOvdj4tr83vJD9+sUWWfb3DTN+14YR88Pwy6Tn6Lpm9rZ98vqG3FC4RJK/1XGwe80bq4q8kBkM9ktUP9cgXLPHhSQOljkQfOSqhv/8uVWdMF7f/Pmtx58+b0iGa2Z1w9aqcnv6FhG/ZIgVbt0r3OuBcWGRiJ5r5/ZJ+0VDAP8g2zd7lyHB54MPBJnP6yGsLZdXzH8vcv38z086HJ3auqWVFRv3wiXzy6HA5N/FnGd6yu0xdvcg2XXWfOVZOXj4n/4ycKfvHz5dLEWGy58yRJPPAOf0sKPeAoCTj3YOCxWLXMaozcSePScTaVVLsjY9M0NpcC9/MkvBfFkmRVyZK6W+Xm4ztqyt+kYTL194T7VAy4dIF2zouvDdefGrUEc9S1558QTLUyL6htK22ffv267JuLSGiT+glZx1XsmRJh8vNnj3btDP79etn+kqxDurKlSvm3/b9qwAAAAA5EYHsVGgJEC3tkZyO02n2NCPlueeeM4NmVCenpUNmzZplgr56s6EB6o4dO8q///5rpt92221SoEABE6zWQacrzXLW2tm6Tc3ItpYVef3115N0sphcWvuj9HFXrW+tWTxax1sfhZ06daqkV/HixU1nRRpY17IkQ4cONcf57bff5plHU/2DfKRIqSA5uP2sbVx8XIIc2X3BlABJjQamtbZ1UIGkHccd3HFW7nqwihQvm89kX2sm9m33lZd/fk+8oTy445yUqlBAGrasaMqYBObzlfsfry3/bjkjly84f2QY/70/4eESfey4BNSufe2UeHiIf/XqErFrd7pOk9b49SpSRDyCkgbokkiwpChdkeF1wAiLuiqHzp+UBuVqXHvL3D2kXkhl2XwssbRScluO75OWHzwrxYa3lspjOsupy+flanSkrDuU+IWQ+mjVfKn1ajcp+EJLM6927Pj7no226eeuXJKes16VkFEPSskX28qUVfOlfKES8vvea/PAOUtEuMSdOiE+VWteG+nuId4VqpryIumhX/R4FCx8LeM6IV4uf/mZnOz5kBzv2EzOTxghXmUrSNTm9c5XkvDfF3ypfB5venqOMjMgQz788EP5/fffTVLBxYsXs/Ts1atXT/bt25ekg3C1fv1623RnnTxqW1JLnuiTdNbBGuTWf//2W+IXggAAAEBORSA7FZoRbQ00W2lQ+ODBg6YjxORefvllOXHihMyYMSPVk65lOCZMmGBuKKwZO1rb1VpGxD6QHRISYgLG06dPNwFwayB71KhRtg4WdXAkvfuj29ZAupZAsc8Sd4WuK62s4dz2aGqLbrVk6Rdb5Ni+CxITFSfzP9xgAswNWjivWX18/0XZveFkkk4erbSjx7+W7DO1t9WJAxdl4++HpHzNIonT6xeXU4dCzTitoa0lTLTDyYLFAyRfIcePDCOps3PmSvEne4tf5cri5uMjpZ4bJJbYOLn027U69FY+5cqZTiDdAxNLIfiWLy8hLw6T8K3bJO7SJTMuuMld4l26tAmUufv6SrEnekjgrfXl4i+/pXsdSN3H/1sgQ+/rJjVLVjDlQsa1fUpi4+Nk4VbH5Yo0G1vLgWjA+65KdeX9zkNMx40aFFeBPv5Sq2RF82VDkK+/PH9vN2lWpb6M+3G6bR1VipWRIkGJtZUrFiktc3qNkz/2/iO/7/mbtyudriz5VoI7Py5e5SqKm7eP5H/iabHEx0nE6j9SzKsZ03533SNu/omfE8/SZSX/U0Mkes8OSQhLzLh2z19QPEuGJP47KJ8U6Pe8eOTLL2EL5iSuxMtb/JvcZ+pzK49iJaTQkJclet8uiTtxrUQJkJ207aaZzy+++KIUKVJEAgICJDg4OMngatkR7dMkPj4+SeKBlhrRtl6jRo3Mtq2B6z17rn2hpJ07Lly4MMWgWrdubf6tywMAAAA5Gc+8p6J3797mRkQb+M2aNTOlQTQTWgO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Za2ID9aKq0Q4MiOrkoqxeLfcmdSEgmnB0stUNlybgVG+3erLVc6wJdVTjy6Neb62jLCRlvXjbIv6pHqYyriPWmNTxUK1OBUVfe+01t0x10HShp78xBUmRMFTfThfV+hvTBbRq+Hn096d6d6rZp86GwGwP1QFUVplq0wMAgkN0E197iRkKsip7LvChibQvl879CqioJreCKgraaESPfsZUbOurXkp083Do3BZMxwRA8Luc9untt992HXuaJFEBaN3Pas4CZSN7r60kH8UnlDyidRXc1vwCXnsVsd2+mjX2L7ctjmnbq0C67icmTZrkJrpUdrl+apQ8khYiN8BFGlRNWKYeRk1goAkeNUGMTgKBgRsNzdFkNcos0aQ30V184urweoS9i3tNIKEMHwWudXLzZrDXBYEyiLz1NKtyly5d3LFFwvj0009doFOBUU3eoRtaBbC9Ej1Zs2Z1Pe6i4LVGRegYqiPi/vvv57AlALV9muxFwwc1iePWrVvDTZIVWNJHbecbb7zhJqjRRC8qNaKLbM0yDgBIHDS8XCNrAh+69tV5QMGLqEqDKNAQ01JiGiGn6wEFzNUJ6iWGyJWc4FnXERHLpWzYsMH99DpYvcxnb5IyT1QlPWK6b/qedN6L6jvRBOS67smfP38sPgmAhKK/Z92HRByRGBMFChRwPyO2Bbq30fW097xHI1W6du3qAtZK7NH19AcffOB/Xm2HkrUGDRrk7nd1rT137lwXv5CI7bZX8kn3wJfaf+2L2vaICXpqswI/S0xEPEfoXlz36YGJLWp7I7a70bW9Gv2p+wndw+/YscOVLdRnV8ITkg4C2UDAUKHABlYNZ6dOnVw5g/nz57sMUNXD0tAeBXD8f0TJk7vsXpWpuNTs7rgydHx0slJtQQXR/v77b3dDcfbsWXdsNNRKVMNLtbR0bJTxo2McWEZEdc7Lli3LYUkAumjTRZTKhwwbNswNE/NuXlWixztGqsfm/Z2tWLHCbr31VhcsVUkfxD8FqJX5oCGPlSpVcuVFNCrFu2j2KPOjY8eOrvanauOp1I/+rSx7AEDioU7kmjVrhnso6KBzsQIjqherYIJn7dq1bkTcpRw4cCDc77oO0HDzwGQRBSwkqgBHXOh6I/BaUr/rOkPBIC84o8+lwFEgzc8RUUz3Ta9Xq1YtV8s2cCi9rl0/+eQTd05VYAxA8NP9iOrda94XjSqNzagJtZ0a2a1r58D1Pv74Y1c2xJtHRqVLIs67paC23ttrH//9999Ir+/d03rrRGy3dW3u1bPWqMkvvvgi2v1XWUeVIwnM4tY+DR061LXVKtMZU+PGjbOjR4/6f1fZzz179ljdunX9yxRc/+WXX/wlXEX3DhHLLkU8b+j71Ahr7bfiAEg6Uib0DgDBQL2XvXr1chmDDRo0cMFONZyqaaehj2q4FVxTjdcKFSq4Wkw1atRww110gapJGTTEHvFDx0c3VppkQydDnYifeeYZdzOiCwEvw0aZQjqpKcNHJ2/1/ubNm5cyIkFAFx4q66LJP5SRpY6jESNGuIs7DZ8LzHRSprZ63dUhoXIWGmaH+KPMEQUt9Pek46ULRq+DQRkRs2bNcvMFqN68Oo507BTwVgefbt51QU62GQCEHl076xygslGaSNsLdCix41L1rnUu0bW0Oqh1HaCgkK7pAidk1HOi6wIFzXXNrVqwcaHrRu2rOlTVEfvtt9+6ydV0TeFNUqbreZ279Bl0LlNwRcEUjRyLKDb71qdPH5cRqaC1vifdU+iaRwEnTW4NIPHQaENN4Kpgru5lVO5CgVmNWFTSRnTUzihJTu1mnTp1XMxB19jqKFN84X//+58/LqF2UG2R6virXVUJRrUxCkKLErnU4abgtzrg1EbpdfLly+famYvRfARqa/X6Tz75pGvLFBj/6quvXMa3Rtzoc6mNatGihS1fvtxlT2sbxUA0+jlw8sZLUfuufdLoG3XgaXt1WurewaM4il5f38uDDz7o7v0mTJjg2uBAuq/QKCElrak2uDpO1SGp7yE2+4QQ4APgW7lypa9+/fq+Zs2a+Xbu3Om+kSlTpviyZMniO378uPvd+/nkk0/66tWrx7eWgMLCwvz/1vGoXbu277vvvnO/T5061R23WbNm+dfRc1WqVPH9888/CbK/8PmOHTvmGzdunO/LL7/0/y0F2rdvn69jx46+8uXL+3755ZdIz0+fPt23ZcsWvsp49tBDD/luuukmX6lSpdzf2e+//+5/7vz58+7noUOHfIUKFfK1aNHCf6y///5738mTJzleABBERo8erXQ739atW93vBQoUiPKatnr16u4REwsWLPDdeuutvlSpUrlzwQcffODr0aOHe59Aeq/HH3/c/3ufPn18FStW9GXOnNmXNm1aX7FixXx9+/b1nTlzxr/OuXPnfM8//7wve/bsvmTJkvlfU/uvfw8YMCDS/njP6bN69L7p06f3bd682VerVi1funTpfDlz5nT76Z3LPPv37/c98MADbh1dTz799NO+1atXR3rN6PZN9G+9dqDffvvNnUczZMjgXvvOO+/0LVq0KMrjs2zZsnDL582b55brJ4CEt337dl/z5s3d33/q1Kld2/fcc8/5Tp8+He3fsWfYsGGuvbvmmmtcO/Tss8/6Dh486H9e9zu6v73xxht9adKk8V133XWuvfjhhx/868yZM8fXsGFDX548eVzbq5+PPPKIb8OGDTHa/wMHDvjatm3ry5s3r9s+X758rp0MvFf++++/fU888YQvW7Zsbh3dCwS2gZdqi71269NPP/V16tTJlyNHDtfW65yj7y+it99+2+2Pvs877rjD9+uvv0Y6F40YMcJXrVo1X9asWd16+o5eeeUV3+HDh2P0uRE6kun/EjqYDiQU/efvZX6qF3X48OEuS0M1r0W9gMrIDqyjrNq86vVr2bJlgu13Uj5OHpWe0BArZfIqu0YzF2uiB03Aqd5u9Upr8gv1zo4ZM8YdU2VtI/6ph12zSnujGZTBoBI9yo4PPL7q8VeWg6i+uXrYldkb2GOPq0/HQ9kf+rv6559/3IgHlQ7REOg5c+a4fyuTLvDYKQNFwxafffZZN3JFo1tUlgkAAABA0qLSrCoFqRhLkyZNEnp3EGKokY0kR3WTNaRHpUMCg6MaXqNhL6rPpJlwRaUMJk6c6GphvfPOO244i7bTsBfED9XCVomXiDMXe31wGk6lIOlvv/3mJgvSsCsdK03uqHIi2lZBNoLYCUPBUA27VWeQhucqkD1z5kw3KUlEGtr29NNPu8lM8uTJ4+owX3/99Qmy30mZ2kXNkK6ap6+99pqrA1q/fn03rFGde/p782rdeW2o2k51TKgOnurRE8QGAAAAAFxpBLKRpEybNs0FW5RlGFiz1ZscQPWZFPxUQFSBNk3+ouBbwYIF3URzt99+u6sD62WS4upSsEz1Br16YApSKxPbq00ua9ascZ0Qd911l6t3qOMlqi32+uuvu/peTMKZMFS/TcFPTSiiSUNEmbwVK1Z0dZM1+aaOpYKhXieFJvFYunSpqzepTgxvhm3ELwWylSHv1cJWG6m/OU36os4+ZVeI/h699lSTVakOverpAQAAAABwpRHIRpLx4osvumxPTRygSVwCA9gK1iiopkkDtI6yeJWNrUlYlIWtDF/NKNyzZ88E/hRJS7p06VwQe8uWLe54eBRQ0zJNrqHJAUWT9WgCQa3nTfaIhAuCiv6WlFkdOJGRsnkXLVrkSr+oo0h/W94xVSdEx44d7cMPP3QZ3Codg/ixcePGcDOKqy3UbOKaoMprI9VeKmD90ksvudIwGhWhY6SOCP3dqbNPk0ICAAAAAHA1ECVAyFu8eLEbDq/gtQIz9913n8si1MPLNlRNV9XGViBG9Xu1nkogqGa2x1sXV9fBgwf92bkKbnqzF6vGtYJtCpwtXLjQzXbcuHFjl3EtCqC1a9fOzWKsetmIfwpsKoO6S5cudubMGRcM1ezXgwYNcj+Vib1y5UpX11zle1RK5LPPPrNt27b5y1Mow5768/FHmfGaNb1Zs2au7XvkkUf8z2mkgzr4vM4ir4xIjhw57Nprr/V3WGTNmtW1mQAAAABQo0YNd29IfWxcDQSyEfKlDTTJgOq8qoaygmcaGu9leiqYrZIUqqesicxuu+02t1xD4ytXruwmp0P8UcCsdOnSrp71+vXr3bLs2bO7IJuyr9u0aeOW6Tj98ccf/okBNTGdKOjdtWvXSBND4urT35K+d02Gqg4gdTaIaisrg1fHThnY6iBSVq+OqwLX2sYr1ZM5c2ZXngLxQ8eoTJkyLmteEzp269bNZcUrI17UNqpjQlnzP/74o6VMmdIt37t3ryu3pBETAAAAAADEl2Q+b8Y0IMRowjhlgmqiOU0IKK+88oqNGTPG1exV8EYUuFGwzQvG6U9CmcCBdZhx9R06dMhlVCugJsreVeCsR48eLnD9/fffW/v27V0WtjooRMfIK22AhOH9nejvxjsO6nlXR4QmdVTQ+vjx466+vDKwNRGgqK6ygt5apokB9Rocx/jz77//unJL5cuXdz8DJ+csVKiQ+7uTrVu32sCBA91x0/Hat2+f60TSsdWxAwAAAAAgvpCRjZCjYLQoy1OZ1gpiexm7mvzv+uuvdxm/Hi+IrXUUFPWC1wSx45eycVu3bm0NGza0atWquXIvadKksccee8wFRnfu3OlqLmvyQE1C5yH4mXAUzFSmvOojq2SIRyMf/v77b+vbt68rP5E+fXq3zsSJE11HhUZFqPyLsu+V7asOC45j/AWw1flw3XXXWb169dywP/HK+aj2fGAZJWVeqx625ghQ+SWV7tmzZw9BbABIwjT3RbFixfzX3FFR2TCd25VA4tFcM6F8vo/4+by5JTQvCICk0fZFRXEGzQOk9kDxhkaNGrnlx44ds1atWlmuXLlc26GkrtiYP3++204/PRpZfsMNN1ioivj5Dhw44O41dV+KpOPCOGEgROgk4Q1/D6RlyhhVYFSZhcrwHTZsmLVt2zbcOkiY46WLAZ3UlbWrTF5d8KvsgYKeCqzpxkBZoaL6yk888YTLqqezIWF9/vnntmnTJhs7dqwr36MRDyoXUrVqVfecyvrooVrLCoKqdIUm71Qnk56vWbNmAn+CpEUdC2vXrrXmzZu7SWwffvhh/3P6Wzp16pQdPnzYlVXyeJn2jz76aALtNQAg2OZWeOutt9x1GZMyX5w6hl9++WV3/n3yySfdfQiApNf2Kf6gEagKVN9yyy0usU5UJlOdfbpH0pxeShBC7GiuHnUG6Du89957+fqSCDKyETIUcFFQVMFRDZXv0KGDC6wpM1QUjNE6qgmrk0bnzp1t1apVCb3bSZImAhQviK1yBcoUFfVQqy6vJnGcN2+eK3Ogmr3fffedK22gE79q94ZyRk9i8eabb1rRokWtZMmSrr75r7/+6o6dbto0EkJlfbp37+6vd96pUyc36aM6Iwhixx+VBylXrpx99dVXrt68d/EckUrAKDO7VKlS7ne1j1OmTHEZZQAAeAEZXWsHTg4cU5rHxJsoOKlQ8oXKqWkuHgBJs+2bO3euS+TRSFSNNtYE695yJdippN///ve/KzLiUffN3r1XUqHR9poPTd8nkgYC2QgZCmyqvIFKFvz111+uBrZKGCgDwhtq4gU/NfmchgWp5w7xS+VDvOFPCk6rY0GZoQq0KbM3Q4YMbsiQjqN6vRVcS506tQu+KaNlzZo1NnjwYA5bEMiZM6c7Rh999JHly5fPpk6d6i6edMOmiVJ17DZu3OiO28GDB13Wr44/GVzxR0O7H3roIVdGZMmSJVa3bl3X9gXySi8tXbrUMmbMaNmyZbN+/fq5v00dy8BSIwCApG306NHWoEGDOGUXK4EhqWUlq3SeRkEFllgBkLTaPiVtqS2I6fLLoet23TsnJcpkV2IV7WzSQSAbiXoyR2Vbazh84AnmwQcfdAE1ZYWqPIWCZ8oqVP2kQLNmzXLrIX4oiFa4cGGX5aleaJUNUc1rZeyqlIjKHaiEiAKhWk+Zo6dPn3aZ84FUAwvxS39nKkkhEWvC6YJOx1HHS0FP/Rw3bpyria1At6hcBcctYfz555+uA69Pnz7+ZWoL9fen9jGwrJI6iUQjHnQMV69ebc8++2wC7TkAIBhH+GjC34ijqjRht5IQrr32WheU0cTBWhZRVDWyde2u806OHDlc8OXmm2+24cOHR9pW1x/aXokq6dKlc6XLdI5TrVS9dyCVpVNZM80HoXWV8fjNN99EWVv2s88+cx3u6pBXgOruu+92ZdMC6Xyp11NShfZRdW6VFBPT7PJ77rnHfvrpJ//oQwCh0fYpaad9+/auTVDboJGqKj2iUeCBcwVolLGus/Vvr6a1fup11TZ5y7V+dJSop5HLuqdSe6k2SPfKEUVVI1v7pBKeKsORNm1al/mt+EhEatM0olZJLUpu0X2eYi7aN7W/gVasWOESZDJlyuQS0dR2/vLLL+HWUWBZ2/78888uNpM9e3a3/40bN7b9+/eHW/fLL790iTdq4/VdqtSK5jfz5vGJSTv79ddf+797hDYC2UiUunTp4k4kajyV4ekNn1HdZF20iobDq7yBshEXLFgQKatQ61FjOX4ou1OTxCkovXjxYndy0gld9ZJ1Y6CbFp2odPLSZI66KNDxVa1lnSR3794dT3uKiHTTpbpjyrzWhYGyqXUzGXhRoQx5XcA899xz/mW6QFHwVBdtuqhIlSoVX2488o6PLkjV2afAtW6i1XbqYvvVV191Ez1qeKTaSmVl66JaWdmqka2OC/1dAgDgWbRokfupGq8eXRtoom5dv2lovM79CrgomB0TClrrel3XiJqkXeeoNm3auImGA6k8Wa9evax8+fKu1qxG7qmcma4ZI3a+K2CjUX96HQWpdR5UQEbnwqjKpGm5ShLqPRSIUad8IM3rceLECde5O3ToUPe++qkkjJhQ0Ejfk/f9AQiNtk/tisqF1KlTxwYNGuQC2SptqqCtKHCrtlGjIdVZpn/roQxi/VTAuGzZsv7lWj8qup5XoFjtmub40vW8Otg0gWRMDBkyxI1+7t27tyuxqiQW3YNH7OBTEFxtm2pN695PQW/dn0ek+zvdpyv2on3QKHcF5XVvoeS1iJ5//nm3rkqoqB3VvWHgXGVe0Fv3k/rutL9qN5XwpnmYYkLrqwPVS8xBiPMBicj27dt9t9xyi698+fK+DRs2+MaMGeOrXbu2r3v37u75ChUq+F544QX373Pnzrmf69at8+XLl8+tj/i1bds2d7wyZ87sK1asmG/OnDlu+cGDB32VK1f29enTx/1+6tQp93PPnj2+FClS+JYsWeJ+3717t+/YsWMctgTWsmVLX506dXxHjhzx/13J/PnzfW+++ab79+LFi31p06b1ffzxx+738+fPJ9j+JmWbN2/27dq1y/fPP/+439esWeN75JFHfKlSpfLdc889vipVqvhmzpzp279/v+/rr7/2JUuWzB07mThxou+HH35I4E8AAAhWXbt2Vaqb7+jRo/5l06dPd8v69+/vX6ZrhapVq7rlo0eP9i/v0aOHWxboxIkTkd5H1/aFChXy/753715fypQpfY0aNQq3Xs+ePd3rPf744/5l7dq1c8sWLlzoX6b9LViwoO+GG27wX5/MmzfPrVe8eHHf6dOn/esOGTLELV+1atVF97Ffv37uHKp7k4t9Pu96VsvfeuutSM8BSNxtn3c/62nSpIlrGzZt2uRfVr16dV+JEiUivW6BAgV89erVu+T7Dx482L3XZ5995l92/PhxX+HChd1ytWcetYd63UAR27AzZ874SpYs6bvrrrv8y5YvX+5eS21ooBYtWrjlat88aot1b6H7jsB2LmPGjL5q1ar5l6n917Y1a9b0hYWF+Ze/9NJL7p7/0KFD0e6jPP3007506dL5YwXRfT5ZtGiRe6/JkydH8Q0i1JCRjURj2bJlbhIy9XTq38rEULaHhqd4Q0iU+ateRA1N0czCXu+eeuiU7YH4o6GaBQsWdJm5GuKp7Hn1wm7YsMENO1XWp8qIiFfHS72oWq4ea8mdOzclKRKASkpoGJlHQ8yUAaDl3igGZS6ph947dhq2q5m4VWZEWdzUwY5fyiBTDU5lNyizQ22eSvZoaKFqz2skhDLdNDpFf4vKAFEJnxIlSvhLizz66KMu2wMAgKioNJXOGcqa82geGi0LLEWlawVl4MWEMv48KkWmMmUqQadrR/0uc+bMcSOHlGEdKKr30P5UrFjRqlSp4l+m/dUITg3bVzmSiJMxBo4aU5ah6P2j2kdlgGsflfWt+w+NHLyULFmyuJ/aDkDotH1q63SfFEilRtQ2fPvtt1fs/fVeui9u0qSJf5nKJqldi4nANkzzFqltVVunCRIDy67KpdpZjfr8/vvvXZmTQoUK+Zdr/3QvoRGgXhzGo/0MLCul99brbN++Pcp9PHr0qGsvtZ5Gw6xbt+6Sn5F2NmkhkI1EQwFs1abTUByv3rUaYdVcVhDn/vvvdycTDZdRA6xgjernTZ8+3Q0bpLRB/FJQ7ccff3Qz1OvEoiFXOvnr+Ig3TEiBbp0MVT5EpSly5crlLw+D+Kd6aaVLl3ZDy3QRIfpdx0/DvES15XUjqKFbCl57NHxXF0Qcv/ilMiFq79SxoLZOF7sKAmjotTqPVFZJQyHVERjYwaDjqU4j1aADACAuFIhQACMwwONdt8eEaqeqnJwSU5TooKH13vwoXiDbC3aoAzaQrje84EXg/kT13rp/CHwtj+4tAnmvp2CPZ8eOHe66SO+nz6l91Hk2cB8vxku4iVgfHEDipbZE9ZxVSzombU1MqG703r17/Y9jx475X0vtX8Q2JKbt7IwZM1zSkeYBUDumNkxlnQLbL72H7hOUiBYoYrurfVRwObp2ViUod+7cGet2VveVigtorgXV3dY+qlyV0M4iogtpWEAioAZNDa4yQZWNraC1sg8VYFOtZdWvu++++1xtPAVRlfmr2q+PPfZYQu96kqSbEWXD6BgokKZjotpZH3/8scsUVWeDsnx1Y6D6VzohVqtWzYYNG5bQu57k6AbLuzBSDTfVN9OxUq+6arDpwkedQroI0s2c6qbpb07Um66LHm+SEgVLET/Uoadjob8xTUylCVw8o0aNcpPA6G9M9emVNaELS2U06EJV2SJaXx19EYMAAABERecNZUarozti8CYuNm/e7EYC6dpD9WVVH1uJJ+qQVd3ZiBNMXw3RzZfjBZ91naNJxDTaTB3H2lcF3TVyTdewMdlHL1ij0VAAEp8r3fZFp0KFCuEC4EpIiTjJYmyplrZqees+W/fg6njUvbkm2v3kk08sPlyqndWobHUOKt6jOt5KslHQXQlSandpZxERgWwkKgqMKmtDk7FoyIqC15rpXHTxqwtfZWdrWeBkDEg4OlF6gVL1sqqDYdy4ce5ErYc3yYSOZ8QZlhE/1ImgmzJvmJqGiemiQUHQd9991zZu3OiG3urCQoFQ9ap7FxRMmJpwNGGqht21bNnSXWB7nQr6qeGP6izSaAdNUKNA9sqVK13Jn7lz57ptFMwGACCmFMQVdXZrtJaodJ9KfyhzMDAr25uI/WI04dfp06ftq6++Cpexp/NbIK88oDptA7MF1aEbmNHnrRvVe3tD02NbalAjP3XtqjJdgZM7zp49O8avoe8rMFMTQGi0fT/88EOk4HZc2xqZOHGim9jR45Xu0GupxGNg8lFM21mNpFVQWPd1XklIUSA7kN5D93f6jEoa9KjdDaRMad0vRtfO6l5EcZnY0ITzas+nTZvmAu4R286YoJ1NWigtgkRH2bu6iFUpkUqVKvmXr1271g1xufnmmxN0/xCZd8LNkSOHPfTQQ+5mRcfxzJkzbniTHgSx458Cngp26uJMF0e6eNEFky5AVFte2VHKvNbs0aqtrL839eSL1qEOdvzTxa2yqEUZYk899ZS76FMwQJ0KusBVEFvHUsdVdUJ//fVXt7469zSvgDLrCWIDAGJL85iId14RjQxSpqJGTQZeX2jOmkvxOsO9rDxvCHnEAIuytnVuC3wPiWoUn/Zn6dKlbm6IwLrWI0eOdNeasb1PiGof9W+v3FpMLF++3F0Le98fgNBo+9TWRWyHNJpEf+8q+xdbGkWppD3v4QWy9V4qw6kSkIGJSGrXYtKGaX+0rx7NF+DdT3hq167tfnr3ep6IbbleTyOtNSeZXsejUfHK8FbioTKrL7edVZwg4r5cqp1VWRLN/4PQR0Y2Eh0NOXzvvfdcQGb8+PH2zDPPuKCMLnrVmKvGMoKXAmsKwCmbXhPpeD3ciF8atqaed5Xq0Q2f6pM3a9bMTcahWte6YNNkHQpk6+ZRy3RBoREPGubFiIf4pwtQleZ58cUX3Q26/pZUTkkZEep0UEeDOou0ni5YleW2b9++cJ1EZIMBAOJKQZWSJUu6LMQnn3zSLdN5SMEXzX2ioIYCxepgjUlNUwVDdF2v19Bk0crq1kTgOpft2bPHv17OnDnduU+TFmuIvMoK/v77724yNZXrCMxQ1H58+umnLoikSdiULKFsamXrKTMxtp3wuk7VMHddL6mciAI0ep2ImeAXo+xtfUeBJcAAJP62T6UXVYZRbV+ZMmXcSEgFeDWH0JWcg0aJKwqYa1SIArYqD6I4iDKjL0Vz6Og+Qe2mykbq3kCxFNW+/uOPP/zraaJ43fcNHjzYZUertKQSmTQiRQLb2T59+rh2TUFrJUXpXnHEiBHu3qN///6x/nyaPFelDhXfUbut99LnCwxsX4r2R8eEuQiSBjKykSgpGKO6dN7FroJrmnwucCZfBB/vZKS65TrJE8SOf7pJ1EWQbsiWLFniLsw0pFdDyHRRooskDQ3WhYxKUqj+csOGDd0knZoYUMcs4oQdiB/KVtBF6COPPOKfQVxZBzpWujHv1auXfz39rhtuXVh6F9wAAFwunVM0Csgb/q7zja4j1Bk+YcIEF9TJmzevCx5fikZSKsNQgQddl3zwwQeuQ11B64jeeust69atmy1btsytq+HuChrp2lLD5gOD3rp+UdKEMglVjlDBcu2zStzFpUSeti1btqz169fPnWt1zaQyeTGhgL72U/ctAEKv7VPQWqMd9VPxCE22rsDxlaSAtUo4qfNP7Zru2RREjknQWCVXlQijySO1j+roU3saVXuodk3JTSr9qTKTSmKaPHmyey6wndX9h2pvK7jvtYtKkFJZqMAR8zGlTj59hwrQd+3a1QYOHOja8JgGxVXSRKOLaWeTjmS+2HRzAEFEQ+ebNm1qVatWdY0ygIvTxVarVq1clpL+ZnTRoBpmuoHUxY1uQJXJpAsS3SCmTZvWPzxOVI+ZiYoSnrIjFMjWhamyJnSRqeHWuvjUzb0C3bppVnabOvd0QQ0AwJWgwKw6wRVg0HwLCUkThCmLT0EdBdCDkc7T+q40saWuqwAkTsHU9sUnzbFTrlw5d5+oDstgpPvaH3/80V/GCaGPQDYSNQ2hZ7I54NKUwaSLLtUmv9jIBWUQqBdeE5eoDpwuCHTxguBx6tQpF7TWLOYalaIhgxoyrZtllX1ReSVNmqIh2IETUwEAcCUom08l/ZR9GF/zZSgLMmIgWOdBZQKqFJpKdwQbjWpTeQElCWj4PYDELSHavvgUVTurLGeV+VD5lNhO4hgfVAZF2eCa0F61xJE0EMgGgCRAAWrVdZs5c6a7sdJQWdXG1jAsZVkrS1vLRPXLdZE2a9YsGzVqFMO04plq1ylzS7WtNcTOm6Fck2mpVIiXHa/h0rp5V4kY1ezUkEPdLKs0jIYBquwSAAChQJNQ66FAhc5zOv9piLyG2n/33XcJvXsAkOipY1BJTKr9rXsOzUOgh0o+qQY2ECwIZANAiFJWribqUE0zTYSqEhOqOaZAtgKdX3zxhav5qCxeTZqqLIOMGTO6bffv3++Gkqk+GeKPJl9RmRBlwWuCqiFDhrhAth5e5ofKvTz77LOuHpxKjCgLQcP9RMuoPQ8ACDW6VunYsaO7Njly5Iirha05IlRWRIFtAMDl0YSJCmYr41zzKmleJM1tpdJNXjINEAwIZANACPr5559d/XgFqzVZ44oVK1zta12gaBIOBUn1uy5QJk2a5Gqebd++3Q0Z8zKAEb/lQlTbWrOH63ho8hRdMHpZ8rJ48WI3L0DFihVt2rRprkNCk2SpfIh+6jgDAAAAABCq6FYBgBDhBaD1U7UiNbu2Jv9T1rU3SYcmddREqSpb4cmTJ49VqFDBn/FLEDthMrFV83/t2rWWKlUq/7HUsdJxWbNmjZuos3Pnzv46mwpyK2NeQ/5q1qyZAHsNAAAAAED8ISMbAEIko3fPnj1WsGDBcMtVdkKZ2R988EGUQ28PHTrkMnmLFy9uQ4cODZcBjKtPweszZ85Y9erV3SScGjYdWAs74rHKnDmzfzsmugUAAAAAJCWhN9UqACQxW7dutXTp0lmZMmVcBvavv/7qMnrlk08+cQ/N5OwtU21JTZL07rvvWqFChVwZCwW6CWLHn7///tv9VDBaQekdO3bYTTfd5F8WkerUKYjt1csmiA0AAAAASGoIZANAIjZv3jxLnTq13XLLLS4geuDAAatbt66reb1w4UJXYmTQoEEuwK36y6LyFaqxPH36dBfgVlkLxB91NNx8883u+HhB7WzZstnSpUvtxIkT/vIweqi0iDK2X3zxRdf5oOco/QIAAAAASIoIZANAIjVgwABr27atm8hRGdWnT5+2+++/3yZPnuyC25o8ULWTq1WrZhkzZnQB7X///dfVw1Zg9IcffqC2cjxSUFpy5cpld911l+tcULD6xhtvdDXM1amgILd4AWvVx165cqUdPHjQsmTJEp+7CwAAAABAUCGQDQCJjFcipGXLlq4mtkqHqOxE9+7d3YSAKlExevRomzp1qquR3a5dO1dfefz48TZlyhQXHNVyb3JHXH0KUvfu3dsdl7x581r79u3t7Nmz9swzz7jnR44c6X7v2rWry5ZXKRE9+vTpY/Xq1bNatWpZiRIlOFQAAAAAgCSLyR4BIJFQoNOrY+1NCDhnzhx77bXXXDmRF154wWVkp0mTxr7//nv/duvXr7cJEybY77//bmPHjiWzNx4pGP3AAw+48iHqeFBmfIsWLdzknJ9++ql16dLFhgwZYk2bNrWff/7Z+vfv7zLl8+fP78qNHD9+3MaNG2elSpWKz90GAAAAACDoEMgGgERAAWuVDFHQWhM0Kivbq5WsrN1vvvnGXnrpJRfwLF++vPXr188FtpFw1IHw0EMPWdmyZW3MmDGRnt+7d6/17dvXZs2aZb/88otlzZrVlYdZtWqVO7aalPPOO+9MkH0HAAAAACDYEMgGgERAkziqZIjKUzzxxBOWPn16f1a2fipg6pWoUMBbNbCXLFniJoGUwMA34ofqlmuCRpV00XevyThTpEjhamV7ZV1+++03V/olT548NmnSJLcs8HkAAAAAAHABd8oAkAgoW3fixIkuOKoSFOIFsfVT9bGVtb1z505r06aN3XHHHS6Q7SGIHf9lYIYPH+6Om757BacVxBYFqb0658rWfvnll+27776zd9991/88AAAAAAAIL2WE3wEAQUp1sDVpoILZmqyxaNGiLkiqoKn+nSNHDhfIVnB09uzZ/nraiH+qga2AdPbs2aMMTuu4LV++3AW4a9eubR06dHAZ9wAAAAAAIGqkfQFAIqIsX9VQ/uSTT1y5EWX5KmA9c+ZMy5Url6uPLQSxE5YmdVRnw1dffWUnT570L1cGvWfGjBn2119/Wdq0aa1Tp06uowIAAAAAAESNQDYAJCJp0qSxzz//3L7++mtr3bq1K0nx/PPP29NPP+0md8ydO3dC72KSEhikjhis7tKli61evdreeOMNO3z4sFumMjAycOBAd+w0cadQTgQAAAAAgItjskcASIR+/PFHV1PZq309bNgwy5kzZ0LvVpJx7Ngxq1evnjVv3txatmzpX+5N6CgnTpxwdc3VydCkSROXLa8s7aFDh9q///5rU6dOteLFiyfgpwAAAAAAIPEgkA0AiTygmiFDhoTejSRFAejnnnvO7rrrLlfixaMJHL2OBWVjq2NBWfLjxo1zNcvXrl3rAtkKXvfp0ycBPwEAAAAAAIkPgWwAAGKoVatWrqzLhx9+aA0aNIj0/Pbt293kjV7d8vz584d7/vjx45Y+fXq+bwAAAAAAYulCsU4AABAtZVs/9NBD9s0339iKFSusSJEi/ufmzZvnamA3atTIli9fbvfdd58NGDDAPRcWFhau/jVBbAAAAAAA4obJHgEAuIhTp065kiGqh61yIQsXLvQ/9+qrr1rdunXt0KFD7vf777/fH8RWvWwmcQQAAAAA4MogIxsAgCjs3LnTTaJZqVIlV0akfv36Nn/+fPvggw9c6ZAhQ4ZYmjRpbPPmzZY3b95I23uTPgIAAAAAgMtHRjYAABEogF2uXDlXMiRz5syutrUMHDjQ0qVLZy1atLCqVavazz//HC6IPX36dFu/fj3fJwAAAAAAVxgZ2QAABNTC7ty5s02dOtVN1lixYkW3TKVFVCpEWdZDhw61evXq2U033WRHjx61jBkz2v79+12NbP386aef+D4BAAAAALjCyMgGAOD/HTlyxBYtWmSffvqpC2KLgthr1661ffv22bFjx6x06dL27LPPuqzt33//3caPH2/FihWzsmXL2oYNGyxHjhx8nwAAAAAAXGFkZAMAkryJEydalixZXPb12bNnbdOmTZYnTx73s3///rZs2TJLnz69C1IvXrzYZW0r87patWqutIi2r1OnTpL/HgEAAAAAuFqS+XTXDgBAEvTLL79Yq1atrHDhwla+fHkrUaKETZgwwZYvX2758+d3pUNuvPFGe+WVV1xQu127dvbcc89Zr169bMeOHTZ8+HB7/fXXLWVK+oUBAAAAALiaCGQDAJIkBaG7du3qAtFt2rSxEydOuIkct23bZitXrrSwsDDLnTu3Va5c2a1/6tQpq127tt19991uu+TJqc4FAAAAAEB8IYUMAJDkHDx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      "text/plain": [
       "<Figure size 1560x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- Visualise the same matrix: bright diagonal, dim everywhere else ---\n",
    "fig, ax = plt.subplots(1, 2, figsize=(13, 5))\n",
    "Mv = M.to_numpy().astype(float)\n",
    "im = ax[0].imshow(Mv, cmap='RdYlGn', vmin=0.3, vmax=1.0)\n",
    "ax[0].set_xticks(range(len(names))); ax[0].set_xticklabels(names, rotation=35, ha='right', fontsize=8)\n",
    "ax[0].set_yticks(range(len(names))); ax[0].set_yticklabels(names, fontsize=8)\n",
    "ax[0].set_xlabel('tested on'); ax[0].set_ylabel('trained on')\n",
    "ax[0].set_title('Transfer matrix (ROC-AUC)')\n",
    "for i in range(len(names)):\n",
    "    for j in range(len(names)):\n",
    "        ax[0].text(j, i, f'{Mv[i,j]:.3f}', ha='center', va='center', fontsize=8,\n",
    "                   color='black' if 0.45 < Mv[i,j] < 0.95 else 'white')\n",
    "fig.colorbar(im, ax=ax[0], shrink=0.8)\n",
    "ax[1].bar(['in-distribution\\n(diagonal)', 'cross-corpus\\n(off-diagonal)'],\n",
    "          [diag.mean(), off.mean()], color=['#2a9d8f', '#e76f51'])\n",
    "ax[1].axhline(0.5, ls='--', c='grey'); ax[1].set_ylim(0, 1.05)\n",
    "ax[1].set_ylabel('mean ROC-AUC'); ax[1].set_title('The gap this notebook exists to show')\n",
    "ax[1].text(1, 0.52, 'random guessing', fontsize=8, color='grey')\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68fff060",
   "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": 9,
   "id": "a206aa7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best single-feature AUC = 0.8643  (feature: DST_TO_SRC_AVG_THROUGHPUT)\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) = 0.000\n",
      "TRAIN/TEST exact-row contamination       = 0.000  (single-feat grade B, contam grade A)\n",
      "==> data trust grade: B   (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": "33ed44be",
   "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": 10,
   "id": "946ba346",
   "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>0.999200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>de-duplicated (0% rows removed)</td>\n",
       "      <td>0.999229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>shortcut feature dropped (DST_TO_SRC_AVG_THROU...</td>\n",
       "      <td>0.999208</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             setting  held_out_auc\n",
       "0                                   headline (as-is)      0.999200\n",
       "1                    de-duplicated (0% rows removed)      0.999229\n",
       "2  shortcut feature dropped (DST_TO_SRC_AVG_THROU...      0.999208"
      ]
     },
     "execution_count": 10,
     "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": "e88aa1a3",
   "metadata": {},
   "source": [
    "## 12. Reproducibility & robustness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "35641929",
   "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": [
      "XGBoost 3-fold CV ROC-AUC = 0.9988 +/- 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": "d7911f7e",
   "metadata": {},
   "source": [
    "## 13. Scientific conclusion\n",
    "\n",
    "Read the matrix, not the diagonal. The diagonal repeats what the other notebooks already report: train and test on the same capture and the score is high. The off-diagonal is the honest number. It is what the model achieves on a network it has never seen, with the feature set held constant. Where an off-diagonal cell falls near 0.5, the model has learned the capture, not the attack. Where it falls **below** 0.5 the situation is worse. The learned rule is actively inverted on the new corpus. So the shortcut does not merely fail, it misleads. Either outcome makes the same point. A high in-distribution score is a statement about a dataset, not about a detector. This is the experiment the other 35 notebooks name and do not run. Their scores should be read in its light (Sommer & Paxson, 2010; Arp et al., 2022).\n",
    "\n",
    "**Transfer ledger \u2014 the experiment's own numbers, exactly as printed in \u00a79b:** One XGBoost model per corpus. Each is fitted on **120,000** rows of that corpus alone. Each corpus is split once, before any model is fitted. So the diagonal is a clean held-out score, not a re-scored training set. In-distribution (diagonal): mean **0.9966**, min **0.9880**. Cross-corpus (off-diagonal): mean **0.5434**, min **0.3315**. Worse-than-random cells: **5 of 12**. The worst cell is train **BoT-IoT**, test **ToN-IoT**, at **0.3315**. Below 0.5 the learned rule is inverted on the new corpus, not merely weak. The best off-diagonal cell is train **UNSW-NB15**, test **BoT-IoT**, at **0.7879**. It still sits below every entry on the diagonal. **What this ledger does not license:** the off-diagonal mean averages twelve cells over four captures. It is not a deployment estimate for any particular network. ROC-AUC is the only metric reported here. It was chosen because the four corpora differ sharply in attack rate, as printed in \u00a76. So these cells track distribution shift rather than prevalence. The matrix reports no threshold, no false-positive rate and no per-corpus recall.\n",
    "\n",
    "**In-distribution ledger (the \u00a78\u2013\u00a712 warm-up) \u2014 read the pooled headline against these printed numbers:** Majority-class baseline **accuracy**: **0.5306**. The accuracy column must clear that bar to mean anything. For ROC-AUC the trivial baseline is 0.5, not that figure. Winning learner: **XGBoost** (3-fold CV ROC-AUC **0.9988**). Strongest *single* feature: `DST_TO_SRC_AVG_THROUGHPUT` at AUC **0.8643**. The ablation refutes a single-feature story. Dropping that feature barely moves the AUC: **0.999200 \u2192 0.999208**. So the separability is **multi-feature**. That reflects how this corpus was generated, not one leaky column. De-duplication changed nothing. There are no exact duplicates to remove. The 0.000029 difference is re-split noise, not a de-duplication effect. Data-trust grade: **B**. It is the worse of two independent sub-checks. Single-feature AUC 0.8643 scores **B**. Train/test exact-row overlap 0.000 scores **A**. The single-feature check drives the grade, not the overlap check. Train/test overlap separately scores A, so overlap is not the issue here. Operational false-positive rate at threshold 0.5: **0.0065**. Worst per-group recalls, exactly as printed: {`CSE-CIC-IDS2018`: 0.946, `UNSW-NB15`: 0.969, `ToN-IoT`: 0.99, `BoT-IoT`: 1.0}. The weakest group sits at **0.946**, which is where detection is thinnest. **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 in this paragraph therefore measures in-distribution separability only. The transfer ledger above is the one that speaks to a different network."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4281cf32",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "1. Sarhan, M., Layeghy, S. & Portmann, M. (2022). Towards a Standard Feature Set for Network Intrusion Detection System Datasets. *Mobile Networks and Applications*.\n",
    "2. Sommer, R. & Paxson, V. (2010). Outside the Closed World: On Using Machine Learning for Network Intrusion Detection. *IEEE S&P*.\n",
    "3. Arp, D. et al. (2022). Dos and Don'ts of Machine Learning in Computer Security. *USENIX Security*.\n",
    "4. Apruzzese, G. et al. (2023). The Role of Machine Learning in Cybersecurity. *ACM DTRAP*."
   ]
  }
 ],
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