{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# BODMAS Cleaned — Malware Classification Dataset\n", "\n", "## Goal of this notebook\n", "\n", "This notebook takes a pre-cleaned dataset of Windows executable (PE) files from\n", "the BODMAS collection, explores it, and demonstrates that a machine learning model\n", "can distinguish legitimate software from malware with high accuracy — using only\n", "the static characteristics of files, without ever executing them.\n", "\n", "### Why this matters\n", "\n", "Traditional malware detection relies on \"signatures\" — known byte sequences\n", "tied to specific viruses. The problem is that every new malware variant needs\n", "a new signature. The machine learning approach is different: instead of looking\n", "for specific signatures, the model learns to recognise **general patterns**\n", "that separate benign from malicious software (e.g. unusually high entropy in\n", "the binary, imports of certain Windows APIs used for code injection, anomalies\n", "in the PE header).\n", "\n", "### What the dataset contains\n", "\n", "**BODMAS** (Blue Hexagon Open Dataset for Malware AnalysiS) is a public dataset\n", "created by Yang et al. (DLS 2021). It contains **134,435 labelled samples**\n", "(77,142 benign + 57,293 malware) with **timestamps** and **malware family labels**.\n", "Each sample is not the original executable but a vector of **2,381 numerical features**\n", "extracted statically from the PE file via [LIEF](https://lief.re/).\n", "After our cleaning pipeline (dedup + constant feature removal), we have\n", "**134,428 samples x 2,326 features**.\n", "\n", "### Notebook structure\n", "\n", "1. **Loading** — how to read the files efficiently\n", "2. **Exploratory Data Analysis (EDA)** — label distribution, feature quality, discriminative signal\n", "3. **Temporal and family analysis** — malware evolution over time\n", "5. **Use Case 1** — which features matter most for telling benign from malicious apart\n", "6. **Use Case 2** — full model evaluation: how many errors, what kind, and how to tune them" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## File Structure\n", "\n", "```\n", "bodmas_cleaned/\n", "├── bodmas_clean.npz # Index file (row/feature counts, file references)\n", "├── bodmas_clean_X.npy # Feature matrix — float32, raw memmap (134,428 × 2,326)\n", "├── bodmas_clean_y.npy # Label vector — int32 (0 = benign, 1 = malware)\n", "└── bodmas_clean_metadata.parquet # Per-sample metadata (SHA-256, timestamps, families, flags)\n", "\n", "```\n", "\n", "The `.npz` index records `_rows` and `_features` for reliable loading.\n", "The feature matrix `X` is a raw binary file written via `np.memmap(..., mode='w+')`,\n", "so it must be read with `np.memmap` specifying `shape` and `dtype` explicitly.\n", "\n", "There is **no separate unlabeled split** — all BODMAS samples\n", "carry a binary label (0 = benign, 1 = malware)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.metrics import (\n", " classification_report,\n", " confusion_matrix,\n", " ConfusionMatrixDisplay,\n", " roc_auc_score,\n", " roc_curve,\n", ")\n", "\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "sns.set_theme(style=\"whitegrid\")\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading the Data\n", "\n", "The dataset is made of three main files:\n", "\n", "| File | Content | Size |\n", "|------|---------|------|\n", "| `bodmas_clean.npz` | **Index**: stores the row and column counts, needed to open the matrix with the right dimensions | ~1 KB |\n", "| `bodmas_clean_X.npy` | **Feature matrix**: each row is a sample (one PE file), each column is a numerical feature | ~1.2 GB |\n", "| `bodmas_clean_metadata.parquet` | **Metadata**: SHA-256 hash, first-seen timestamp, malware family, label | ~15 MB |\n", "\n", "### Why we use memory-mapping\n", "\n", "Although BODMAS is just 1.2 GB we use **memory-mapping** (`np.memmap`) for consistency and to keep RAM usage low.\n", "The file stays on disk, but NumPy \"sees\" it as if it were an in-memory array.\n", "When we access a row, only that row is actually read from disk.\n", "\n", "Loading happens in three steps:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NPZ index loaded:\n", " Samples: 134,428\n", " Features: 2,326\n", " Referenced files: X=bodmas_clean_X.npy, y=bodmas_clean_y.npy\n" ] } ], "source": [ "from pathlib import Path\n", "\n", "DATA_DIR = Path(\".\") # adjust to your dataset directory\n", "\n", "# Step 1: load the NPZ index (row/column counts)\n", "index = np.load(DATA_DIR / \"bodmas_clean.npz\", allow_pickle=True)\n", "n_rows = int(index[\"_rows\"])\n", "n_features = int(index[\"_features\"])\n", "\n", "print(f\"NPZ index loaded:\")\n", "print(f\" Samples: {n_rows:,}\")\n", "print(f\" Features: {n_features:,}\")\n", "print(f\" Referenced files: X={index['_X_file']}, y={index['_y_file']}\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Feature matrix opened: 134,428 samples x 2,326 features\n", "dtype: float32 — memory-mapped (not fully loaded into RAM)\n" ] } ], "source": [ "# Step 2: memory-map the feature matrix (stays on disk, read on demand)\n", "X = np.memmap(\n", " DATA_DIR / \"bodmas_clean_X.npy\",\n", " dtype=np.float32,\n", " mode=\"r\",\n", " shape=(n_rows, n_features),\n", " order=\"C\",\n", ")\n", "print(f\"Feature matrix opened: {X.shape[0]:,} samples x {X.shape[1]:,} features\")\n", "print(f\"dtype: {X.dtype} — memory-mapped (not fully loaded into RAM)\")" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Metadata loaded: 134,428 rows x 15 columns\n", "Columns: ['sha256', 'timestamp', 'family', 'record_id', 'sample_id', 'label_int', 'label_str', 'timestamp_raw', 'hash_type', 'bodmas_family', 'flag_sha256', 'flag_timestamp_missing', 'flag_family_missing', 'flag_is_benign', 'feature_hash']\n", "Labels: 77,138 benign | 57,290 malware\n", "\n", "All checks passed: 134,428 samples x 2,326 features, labels in {0, 1}\n" ] } ], "source": [ "# Step 3: read metadata and extract labels\n", "meta = pd.read_parquet(DATA_DIR / \"bodmas_clean_metadata.parquet\")\n", "y = meta[\"label_int\"].values\n", "\n", "assert X.shape[0] == len(y), f\"Row mismatch: X={X.shape[0]}, y={len(y)}\"\n", "assert set(np.unique(y)) == {0, 1}, f\"Unexpected labels: {np.unique(y)}\"\n", "\n", "print(f\"Metadata loaded: {meta.shape[0]:,} rows x {meta.shape[1]} columns\")\n", "print(f\"Columns: {list(meta.columns)}\")\n", "print(f\"Labels: {(y == 0).sum():,} benign | {(y == 1).sum():,} malware\")\n", "print(f\"\\nAll checks passed: {n_rows:,} samples x {n_features:,} features, labels in {{0, 1}}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exploratory Data Analysis (EDA)\n", "\n", "Before training any model, we need to understand *what is in the data*:\n", "\n", "1. **How many samples per class?** —\n", " BODMAS is moderately imbalanced (57% benign vs 43% malware). We need to check\n", " whether this imbalance is severe enough to require mitigation.\n", "\n", "2. **Are the features full or empty?** — Many PE features are zero for most samples.\n", " Understanding how many features are \"full\" vs \"empty\" tells us how much of the\n", " feature vector is actually informative.\n", "\n", "3. **Which regions of the matrix are active?** — The features follow the LIEF\n", " schema: header characteristics, import/export flags, byte and entropy histograms.\n", " Visualising the activation profile shows which blocks are dense vs sparse.\n", "\n", "4. **Do the features actually separate benign from malware?** — We check whether\n", " distributions differ between classes. If they do, the model can exploit them." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Benign: 77,138 (57.4%)\n", "Malware: 57,290 (42.6%)\n", "Imbalance ratio: 1.35:1\n", "\n", "Moderate imbalance — no resampling needed, but we will use balanced sampling for training and stratified splits.\n" ] } ], "source": [ "n_benign = (y == 0).sum()\n", "n_malware = (y == 1).sum()\n", "total = n_benign + n_malware\n", "\n", "categories = [\"Benign\", \"Malware\"]\n", "values = [n_benign, n_malware]\n", "colors = [\"#4caf50\", \"#f44336\"]\n", "\n", "fig, ax = plt.subplots(figsize=(7, 5))\n", "bars = ax.bar(categories, values, color=colors, edgecolor=\"black\",\n", " linewidth=0.8, width=0.45)\n", "\n", "for bar, count in zip(bars, values):\n", " pct = 100 * count / total\n", " ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height() * 0.90,\n", " f\"{count:,}\\n({pct:.1f}%)\", ha=\"center\", va=\"top\",\n", " fontsize=12, fontweight=\"bold\", color=\"white\")\n", "\n", "ax.set_ylabel(\"Number of samples\")\n", "ax.set_title(f\"Label distribution — BODMAS cleaned ({total:,} samples)\",\n", " fontsize=12, pad=15)\n", "ax.set_ylim(0, max(values) * 1.15)\n", "ax.grid(axis=\"y\", alpha=0.3)\n", "sns.despine(left=True)\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "imbalance_ratio = max(n_benign, n_malware) / min(n_benign, n_malware)\n", "print(f\"Benign: {n_benign:,} ({100*n_benign/total:.1f}%)\")\n", "print(f\"Malware: {n_malware:,} ({100*n_malware/total:.1f}%)\")\n", "print(f\"Imbalance ratio: {imbalance_ratio:.2f}:1\")\n", "print(f\"\\nModerate imbalance — no resampling needed, but we will use balanced sampling for training and stratified splits.\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sampled 50,000 rows for EDA (from 134,428 total)\n" ] } ], "source": [ "sample_size = 50_000\n", "rng = np.random.default_rng(42)\n", "sample_idx = rng.choice(len(y), size=min(sample_size, len(y)), replace=False)\n", "X_sample = np.array(X[sample_idx]) # copy selected rows into RAM\n", "y_sample = y[sample_idx]\n", "\n", "nonzero_frac = (X_sample != 0).mean(axis=0)\n", "\n", "feat_stats = pd.DataFrame({\n", " \"mean\": X_sample.mean(axis=0),\n", " \"std\": X_sample.std(axis=0),\n", " \"nonzero_frac\": nonzero_frac,\n", "})\n", "\n", "print(f\"Sampled {len(sample_idx):,} rows for EDA (from {len(y):,} total)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Feature sparsity profile\n", "\n", "The chart below shows, for each of the features (X axis), the fraction of\n", "samples where that feature has a value other than zero (Y axis).\n", "\n", "**How to read it:**\n", "\n", "- A tall bar (close to 1.0) means the feature is **dense** — it is active for\n", " almost every file. Example: file size — every PE file has a size.\n", "- A short bar (close to 0) means the feature is **sparse** — it fires for very\n", " few files. Example: imports a rare DLL.\n", "\n", "The dashed lines mark the 10% and 50% thresholds. Features below 10% are\n", "very sparse — useful for catching specific malware families but risky for\n", "overfitting. Features above 50% are the dense \"backbone\" of the dataset.\n", "\n", "**Note:** After our cleaning pipeline, constant features (zero variance) have\n", "been removed — so every feature shown here has *some* variation." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Sparse features (<10% active): 1354\n", "Dense features (>50% active): 596\n", "\n", "Note: constant features were removed during cleaning.\n", "The dense block on the right (~features 1800+) corresponds to byte and entropy histograms — always filled because every file has a byte distribution and an entropy level.\n" ] } ], "source": [ "fig, ax = plt.subplots(figsize=(14, 3.5))\n", "ax.bar(range(n_features), feat_stats[\"nonzero_frac\"], width=1.0,\n", " color=\"steelblue\", alpha=0.7)\n", "ax.axhline(0.10, color=\"red\", ls=\"--\", lw=0.8, label=\"10% threshold\")\n", "ax.axhline(0.50, color=\"orange\", ls=\"--\", lw=0.8, label=\"50% threshold\")\n", "ax.set_xlabel(f\"Feature index (0-{n_features-1})\")\n", "ax.set_ylabel(\"Fraction of samples with value != 0\")\n", "ax.set_title(\"Sparsity profile — for each feature, how many samples 'use' it?\")\n", "ax.set_xlim(0, n_features)\n", "ax.legend(fontsize=9, loc=\"upper right\")\n", "sns.despine(left=True)\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "print(f\"Sparse features (<10% active): {(feat_stats['nonzero_frac'] < 0.10).sum()}\")\n", "print(f\"Dense features (>50% active): {(feat_stats['nonzero_frac'] > 0.50).sum()}\")\n", "print(f\"\\nNote: constant features were removed during cleaning.\")\n", "print(f\"The dense block corresponds to byte and entropy histograms — always filled because every file has a byte distribution and an entropy level.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Per-class feature distributions\n", "\n", "The histograms below overlay benign (green) and malware (red) for the first\n", "8 features. If the two \"humps\" are separated, the feature carries discriminative\n", "signal. If they overlap completely, the feature is noise for classification.\n", "\n", "The **Kolmogorov-Smirnov (KS) test** quantifies this:\n", "- **KS statistic** (0-1): how different the distributions are\n", "- **p-value**: probability the difference is due to chance\n", "- \"Signal = YES\" means the feature genuinely distinguishes the two classes" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Kolmogorov-Smirnov test (first 8 features):\n", " Feature KS stat p-value Signal?\n", " ------------------------------\n", " Feature 0 0.2168 0.00e+00 YES (strong)\n", " Feature 1 0.3504 0.00e+00 YES (strong)\n", " Feature 2 0.3080 0.00e+00 YES (strong)\n", " Feature 3 0.3557 0.00e+00 YES (strong)\n", " Feature 4 0.2832 0.00e+00 YES (strong)\n", " Feature 5 0.3645 0.00e+00 YES (strong)\n", " Feature 6 0.3269 0.00e+00 YES (strong)\n", " Feature 7 0.2459 0.00e+00 YES (strong)\n", "\n", "Features marked 'YES' have statistically different distributions between benign and malware — the model can exploit them.\n" ] } ], "source": [ "fig, axes = plt.subplots(2, 4, figsize=(16, 6))\n", "for i, ax in enumerate(axes.flat):\n", " vals_b = X_sample[y_sample == 0, i]\n", " vals_m = X_sample[y_sample == 1, i]\n", " ax.hist(vals_b, bins=50, alpha=0.5, label=\"Benign\", color=\"#4caf50\", density=True)\n", " ax.hist(vals_m, bins=50, alpha=0.5, label=\"Malware\", color=\"#f44336\", density=True)\n", " ax.set_title(f\"Feature {i}\", fontsize=10)\n", " ax.tick_params(labelsize=8)\n", " if i == 0:\n", " ax.legend(fontsize=8)\n", "plt.suptitle(\"Per-class distributions — benign (green) vs malware (red)\",\n", " fontsize=12, y=1.02)\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "from scipy.stats import ks_2samp\n", "\n", "print(\"Kolmogorov-Smirnov test (first 8 features):\")\n", "print(f\" {'Feature':<10} {'KS stat':>8} {'p-value':>12} {'Signal?'}\")\n", "print(f\" {'---'*10}\")\n", "for i in range(8):\n", " ks, pv = ks_2samp(X_sample[y_sample == 0, i], X_sample[y_sample == 1, i])\n", " signal = \"YES (strong)\" if pv < 0.001 else \"weak\" if pv < 0.05 else \"no\"\n", " print(f\" Feature {i:<3} {ks:>8.4f} {pv:>12.2e} {signal}\")\n", "\n", "print(f\"\\nFeatures marked 'YES' have statistically different distributions between benign and malware — the model can exploit them.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Temporal & Family Analysis\n", "\n", "BODMAS has features as **timestamps** (when each sample was first observed) and **malware family labels** (e.g. Emotet, TrickBot, etc.) that enables analyses like:\n", "\n", "- **Temporal distribution**: how many samples were collected per month?\n", " Is the dataset concentrated in a short period or spread over time?\n", "- **Top malware families**: which families dominate the dataset?\n", " This tells us what kinds of malware the model will learn to detect.\n", "- **Concept drift implications**: if the data spans a long period,\n", " malware techniques may evolve — a model trained on early data might\n", " perform differently on later samples." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Timestamp coverage: 130,716 / 134,428 samples (97.2%)\n", "Date range: 2007-01-01 to 2020-09-30\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# --- Temporal distribution ---\n", "if \"timestamp\" in meta.columns:\n", " ts = pd.to_datetime(meta[\"timestamp\"], errors=\"coerce\")\n", " valid_ts = ts.dropna()\n", " \n", " print(f\"Timestamp coverage: {len(valid_ts):,} / {len(meta):,} samples ({100*len(valid_ts)/len(meta):.1f}%)\")\n", " print(f\"Date range: {valid_ts.min().date()} to {valid_ts.max().date()}\")\n", " \n", " # Monthly distribution\n", " monthly = valid_ts.dt.to_period(\"M\").value_counts().sort_index()\n", " \n", " fig, ax = plt.subplots(figsize=(14, 4))\n", " ax.bar(range(len(monthly)), monthly.values, color=\"steelblue\", alpha=0.8)\n", " \n", " # Label every 3rd month for readability\n", " tick_positions = range(0, len(monthly), max(1, len(monthly)//12))\n", " ax.set_xticks(list(tick_positions))\n", " ax.set_xticklabels([str(monthly.index[i]) for i in tick_positions],\n", " rotation=45, ha=\"right\", fontsize=8)\n", " ax.set_ylabel(\"Samples\")\n", " ax.set_title(\"BODMAS: samples collected per month\")\n", " ax.grid(axis=\"y\", alpha=0.3)\n", " sns.despine(left=True)\n", " plt.tight_layout()\n", " plt.show()\n", "else:\n", " print(\"No timestamp column found in metadata.\")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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LVf1U1/XIvtUpfCLV9IqgLtyioQbq1lpc7dq13U/tI1VldVFvAJ1yTF1n1bui+CR1EWqfxUOV1qe2U1J7jiwTUXQSKdnVRFk7o+fb2een+PPtirrkq4uzqqqaqCoiMu5e69F+UYjVAZ6i1IVZn0HNbSDqMq9eGcWpJ4VC5K7EYp8Up20vaR/p70mkK3vks7Or5y9tWyoNjR3X51NBXWP4IwceIuPuK0rkNam7eUnj5iN/b7V9+izror8tmhNBnw9tv15L0YMZe9LuAMQvJjkDAA/oi7iqfcUnxVHXW02eVPyLfKSbtOiL/jvvvOO6eu5s1mNVrjWGUF+eNRNv8XAtkdMWFZ2RXN2oNZ5TY3JjTWFYlUJV0SPhWhVoBfySZhEXdQnXtuhLrYJL5Mu9xsNqO9UFVt2Eiz6HvrTreSL7RhN4yc6eQxTiNV5UX6A1ljJyURdRdQ/d2ezTO3ud+vKursORcK2qpm7f1TbEmg6gqKeDqv9FX5MOUGjcsHpJKETqoIUOwIgCprrC6yCDQuXO6HHqMly0cqo2pm7Txc9frcq43sdIwNS+jsz4HlF8DO3uhidEnk8HA4pORKaQqIMCep2lPTe3ltGkVpqEq6h58+a5g1g6yKMwqt8jM5RHqL0orEaqxArpGoOrrs8R+l237eozpX0SmQwwovhzlYUOXun9KLq/tc1FD4REqsRFH6Pu299+++0etaXS0uvSgQnNzxAJ13pudROvyM+HhrOoXeq5i74m9ahRhV2vVe1ZvWgifyO1H3Sec/3tLP75iOy/ogdpAFRNVLABwAMKEJohWjOHq1Kocao6FZPGdKoaFJkoLEKTKqkSrWCuiX70hV0TGO2MTmejmchVZVEAUWgv+oVaX3BVpdbYbp1WSetWFUddm9WVt/gM5LGggKVulgr8GqOoSc809lUHAVRl3hl9wdWM3grlka7gkXHD6lZa9PzGeg5VkDVOU92eFdxUZdrdc4jeD3UXjbwfCl4KnuqKroml9uR16jWqiq3wr0qvJq1TJXFPKn3lpUCoiec0Xl2/a1v03qr6pjCgfaReCvqpdqewoVMcaTiBJltT8N4ZhUXtV4WiyOm1VK3X2HON39apszQEQBPYqfu0ZpyOVAzV40AhWAFHwVXjmotXviOP1QEoPa6kbs5apw6eaEy+3jdtvyYk00GqRx55pNT7SYFI2673SO1JPR8UAiOzSkcmctMEfpFTYkUClvabPkd///vf3e2aoFDLKYRF5ldQAD3wwAPdRH07o/dG+0qfRY0B1oGj4qeCKgttc+RsBTrdmw6IqAdJ0SCrNqnXrJm49X7ouiaO08GXyIGw0rSlPfl8aDm1H40P13uviQV1cG93n9FYUqVZf+c0L4X+Rupvil6LruvvhYYF6ACA/o7o76keozkydFBQPR40XKAotRm1+aIT/wGomgjYAOARfalX12R9uVQoUfDVJDoKesXHsmo8rB6n8KBKq4JfZGxxcZEqtGg8cnGqAkdOa6NxhAozGnesLqH6oqyQXdJMyOWlc9CqMqYv+NpGfRlVt2RV+BQuindRLRqw9YVXVf3I5Eb6Mqzb9LNo922F2sg4YtFBAx1kUBVVoWVXVH1UgFJoUkBUYIvsj13NZl2cDlqo+qVgqcCpKp9eg2ZuVqW0ImcZ1mzTamMKnKrQKjBpP6pbbCS0qg3oPVGbUvdqVSrVXVdd2ndGbU+PU9CIBGy9D2pXCpSR0KKKn4YGFO3+q4NKGoOvWdb1fiqUKoxGZnIW9ULQWH21GS2r9l+cehdo/0ZOcaf2oPCmcLizz8bOqI1of+g5ddBHoUptoOiBJnWP1nupAzvaV5oXQQcaFDwjBwRUNVd70WvWex3pgaHtKzoxV3Gq5qobuQ5saP2qzuvUbGeeeaaVN0RqrLNOxaZ9qb8xek3FT4cV+dzoPdBjtM/VQ0YH8/akLZWGgqkOJmobNBmdxmDr3NJ6//Q3TsE9sj/3No2n199atSO9Lh1cUNVff4Mj1XX9PVAbU5vWdmt7dXCn+NhtTYZX9KwEAKquQHh308gCADyj6p6+nKuLdGSWWqAyUMhUpV7DFWI1QzYqhqrkOtCmYI3y08E79RLQUJ7iE1ICqHoYgw0AAPaYKvLqalx0DD9QFan6re74hGsAQsAGAAB7TOO377zzTncao+LnGAeqCp3hQOPxNcYdAIQu4gAAAAAAxAAVbAAAAAAAYoCADQAAAABADBCwAQAAAACIAQI2AAAAAAAxQMD20ObNm90pTgA/UtulDcPPaMPwO9ow/I42jHhEwPZYOBz2ehOAcrVd2jD8ijYMv6MNw+9ow4hHBGwAAAAAAGKAgA0AAAAAQAwQsAEAAAAAiAECNgAAAAAAMUDABgAAAAAgBgjYAAAAAADEAAEbAAAAAIAYIGADAAAAABADBGwAAAAAAGKAgA0AAAAAQAwQsAEAAAAAiAECNgAAAAAAMUDABgAAAAAgBgjYAAAAAADEAAEbAAAAAIAYIGADAAAAABADBGwAAAAAAGKAgA0AAAAAQAwQsAEAAAAAiAECNgAAAAAAMUDA9lAgEPDy6YFyCwb5EwJ/ow3D72jD8DvaMOJNIBwOh73eiKqqYGuWBQsKvN4MoMxCoRD/McLXaMPwO9ow/I42jEBysgVSUuNmR1TzegOqtLw8y5s90ywzw+stAfaYDs2FQoUWDCYYnTHgR7Rh+B1tGH5HG0agfkNLHDKMgI0Y/mHZkGG2Pp1dCn/+p1hYaIGEBDNGO8CHaMPwO9ow/I42jHjEAEoAAAAAAGKAgA0AAAAAQAxUmYC9cOFCO/roo61jx462cuVKrzcHAAAAABBnqkzAfvvtt61Pnz72xRdfWPPmzb3eHAAAAABAnIm7WcQLCwvtpptusnfeeceSk5Nd1TopKcleffVVd97p33//3R577DF75JFH7Omnn7Zt27bZkUceaePGjbMGDRrYnDlzbP78+ZaQkGCffPKJC+O33XabtW7d2q3/tddes/vuu882btxonTp1sptvvtnq16/v9csGAAAAAHgs7gK2gvWyZcvsgw8+sIKCAjvnnHPs7LPPttzcXGvWrJldccUVNnv2bHv++eft8ccft8aNG9v48eNt1KhRNmvWLLeOBQsWuBB99913u3A9depUF8q//PJLmzBhgj366KPWsmVLmzx5so0ZM8aF9fKc+890AXw482c4HHZtmNN0wY9ow/A72jD8jjaMQCjkvk/m5OS4n5VZampq1QzYaWlp9uuvv9rcuXOtZ8+e9uKLL1owGLTPP/88+hhVoYcPH2777befuz527Fjr0KGDrV271l1XeO7du7f7vW/fvq66LaqC//3vf7dDDjnEXb/qqqusffv2tmHDBlf93lM6AJCXl2uFOdtj8toBAAAAwC8ScnMtmJ9vy1evcQXRyky5r0oG7K5du9ro0aPt2Wefdd2327Zta7fffvsOj1H37qZNm+5wNKJu3brRgF2vXr3ofeoqHjmakp6e7kK2upZHVKtWzdasWVOmgK1lk5KSzVKql+m1Ap6fuzIUcgewqGDDj2jD8DvaMPyONoxAcrIlJia6Amdlr2CXVtwFbM0Q3q5dOxs8eLCrLKv796RJk6xGjRrRx6hbuEJxRHZ2tm3atMmNpV6xYsVO160Qffnll9sFF1wQvW358uXWokWLMm+vwonpAvi0WxcBG35FG4bf0Ybhd7RhBFyhJmApKSlxszPiLtl99tlnbjx1Zmam1a5d2010pp9FDRgwwI2j/u2332z79u2uwq1u37ubXVzLPfPMMy5Uq3L3xBNP2FlnnWX5+fl7+VUBAAAAACq7uKtgn3baafbTTz/ZiSeeaHl5eW6mb01Mpip2xMCBA111e9iwYbZ582Y3i7gmNdudLl262GWXXWaXXnqpZWRkuK4MDz30UFwdcQEAAAAAlE0gHC+d3X2oYGOm5U+702x9utebAuwx/eXQafE0TwFjsOFHtGH4HW0YfkcbRqBRE0saOcaCderGzc6Iuy7iAAAAAAB4gYANAAAAAEAMxN0YbL8JNGhoFvB6K4AyCJsFQ4UWCNJFHD5FG4bf0Ybhd7ThKi9Qv2Hc7QPGYHuoYGuWBQsKvNwEoFwi58EG/Io2DL+jDcPvaMMIJCdbICU1bnYEFWwPZReGrGZaLTdJFOA3muBsW3a2O8c8bRh+RBuG39GG4Xe0YcQjSk8eYgJ3xMNRZ8DPaMPwO9ow/I42jHhDwAYAAAAAIAYI2B4KcPJg+Bzjr+F3tGH4HW0YfkcbRrxhkjMPMckZ/I6JSeB3tGH4HW0YfkcbrjoCcTaZ2c4wyZmX8vIsb/ZMs8wMTzcDKItwWP8pFlqQ03TBp2jD8DvaMPyONly1TseVOGQYARt7X3hDhtn6dHY1/PmfYmGhBTQLPudyhw/RhuF3tGH4HW0Y8Ygx2GaWn59v69at8/q9AAAAAAD4GAHbzEaNGmWffPKJ1+8FAAAAAMDHCNhmtnnzZq/fBwAAAACAz1W6gN2/f39bsGCB+/3nn3+2Vq1a2ffff++uf/TRRzZo0CB7+eWXbcCAAdauXTvr2rWrzZo1K7r8p59+6u5r27atnX766fbLL7+427/99lsbMmSIHXnkkda+fXsbO3asFRYW2pQpU2zJkiV244032owZM9xtd9xxh/Xp08fatGlj/fr1s0WLFkXXP3PmTOvRo4d16NDBRowYYVu3bq3wfQQAAAAAqHwqXcDu1q1bNNDqZ3JysgvA8vHHH9v+++9v48ePt8mTJ9tXX31lt956q02aNMkF3YyMDLv88svd5csvv7TjjjvORo8e7Za98sorXTj//PPP7ZVXXnEhXmH8qquucmH55ptvtgsuuMBeffVV97wvvPCCW0evXr3s7rvvduvQMo888ogL4upSrtMKTJ061cO9BQAAAACoLCrdabq6d+/uArMsXrzYTjnlFPviiy/s3HPPdaF2woQJLhQ3bdrUBeqEhAQ3Sdkff/xhn332mR188MGu+ixnn322derUKVp53nfffS0rK8syMzOtdu3atmHDhj89v5ZVhbpmzZq2Zs0aq1GjhnseefPNN23w4MF24IEHuus33XRTuSvYCummC+DDmT/D4bBrwwFmEYcP0Ybhd7Rh+B1tuOoIhELue2NOTo776Uepqan+DNjqvv2f//zHNm3aZF9//bU9/fTTrqv32rVrbePGjXbooYfaXXfd5arQ9erVs9atW7vl9EYpODdq1Ci6rqSkpOj9qnYPGzbMdQHXOhTKS3pz8/Ly7IYbbnChXoF8n332id6n9atbekSDBg3cpawKCgosLy/XCnO2l3kdAAAAAFCZJeTmWjA/35avXmO5ubnmR8qpvgzYCsUdO3Z0wbpZs2bWokULV21+4oknXPfxefPmua7iqibXqVPHtmzZYnPmzHHLKlxrnHaEQrS6kqv6rdD84osvRqvPJ598conPrzHZaWlprlqemJho7777rquai8L2+vXro49dtmyZ2xatvyyqVatmSUnJZinVy7Q84Pm5K0MhCwaDVLDhS7Rh+B1tGH5HG646AsnJLlu1bNnStxXs0qp0ATvSTfyee+5xY6ZFgXv27Nk2ceJEV9nWm6NLdna2q2ZHqsFaTuOz33//fevZs6c9+eSTrpu51hMIBKx69equgq1A/tNPP7llIqE+0tVbgV3BXV3P09PT7eGHH3ZBXU488UT75z//6SZiUxf1adOmuZ/loXBiugA+7dZFwIZf0Ybhd7Rh+B1tuOoIuIJMwFJSUizeVcpkp6CsU2cpWIt+KuRqxvBTTz3VVapVzT7++OPdJGgHHHCALV++3HUZv++++2z69OluGU1Kpoq07leVWUH7qKOOsvfee8/69u3rlokEZz1OlyuuuMK+++471wVAY7g1UZpCvS56/osuushNhqZtVGAfOXKkx3sLAAAAAFAZBMLxXqOvxAo2Zlr+tDvN1qd7vSnAHtNfDvUIUW8PJjmDH9GG4Xe0YfgdbbjqCDRqYkkjx1iwTl2Ld5Wygg0AAAAAgN8QsAEAAAAAiAECNgAAAAAA8TqLeFUSaNDQLOD1VgBlEDYLhgotEGQMNnyKNgy/ow3D72jDVUagfkOrKpjkzEMFW7Ms+H+nCgP8KHIebMCvaMPwO9ow/I42XLXOhR1ISbV4RwXbQ9mFIauZVsvNwgz4jWYQ35adbTVq1KANw5dow/A72jD8jjaMeETpyUOcIQ3xcNQZ8DPaMPyONgy/ow0j3hCwPRTg5MHwObqHw+9ow/A72jAAVC6MwfYQY7Dhd4ybgt/RhuF3tGH4eUytuohnZWVZWloaw80QNxiD7aW8PMubPdMsM8PTzQDKIhzWF7tCCzKLOHyKNgy/ow3D61mhE4cMqxKTVgF7goDtsfCGDLP16V5vBlC2L3aFhRbQJH2cag4+RBuG39GGAaDyYQw2AAAAAAAxQMAuZtWqVbHYrwAAAACAKoaAXcSGDRusf//+3r0bAAAAAADfImAXsX37dsvJyfHu3QAAAAAA+JavA/aiRYusV69eO3TvbtWqlbt94MCBNmbMGGvbtq2dfPLJ9u2330YfN2PGDOvXr5+7T8vPmzfP3T5kyBD3U7evW7fO3Xf99ddbp06d7JFHHrH8/Hy7++67rUePHta1a1ebNGmSuw0AAAAAAF8H7F1ZunSpNWvWzIXtQYMG2WWXXWZ5eXnu+tNPP20zZ860r776yoYPH24TJkxwyzz11FPu59dff22NGjVyv2/ZssU+/vhjO+OMM+zRRx+1xYsX20svvWRz5861b775xh5//HFPXycAAAAAoHKI29N01alTxy699FKrVq2aDR061IXjL7/80tq0aWPPPfec7bPPPq5KnZycbBkZOz8PdZ8+fSwpKcldXn75ZbvhhhusQYMG7r5LLrnExo8fbxdeeGGZtzMUCumfMi8PeHl6mHA47NpwgNN0wYdow/A72jC8FAiF3PcADa/Uz7IoLCy03NxcS0hIcBegMktNTa3aAbtJkyYuXEeoIp2ZmWmBQMCmTp1q77//vntMy5Ytd7meSJiWtWvX2hVXXGHB4H8L//pjovWVVUFBgeXl5VphzvYyrwMAAACoaAm5uRbMz7flq9e4kAzEu/bt28d/wFbQVUiN2Lx58w4zgkcCsH4qHCtkq2u4fv/ggw8sJSXFfvjhB3v99dd3+hxFA7TC9pQpU6x169bu+tatW23Tpk1l3n4dAEhKSjZLqV7mdQBe0cFqVa/1OaSCDT+iDcPvaMPwUiA52RITE12xqjwV7G3btrnKIBVsxAtfB+zmzZu7IL1kyRI7/PDDbdasWdH71O1b1//xj3/YM88848KsuocvWLDAdQvXh3jjxo12zz33uMdrsjJ1A48E55o1a/7p+U466SSbPn26m9ysevXqrru4Hvvwww+X+TW4avj/VcQBP3ZNJGDDr2jD8DvaMLwUcAfYA65gVVYK2LoQsBFPfJ3sGjdubCNHjrQrr7zSevfu7Wb/jmjYsKH9+9//ti5dutgbb7xhDz74oDvKdu6557pQ3LlzZzfTuIJ57dq1bfny5W6Zbt26ucsvv/zyp+fTmO4DDjjAzUqux+i0XhMnTqzgVw0AAAAAqIwC4bL26ajENFP42LFj3TjryqxgY6blT7vTbH2615sC7DH95dBRZ/UGoYs4/Ig2DL+jDcNLgUZNLGnkGAvWqVvmdeh7RFZWlqWlpdFFHHHD1xVsAAAAAAAqCwI2AAAAAAAxEJcBW+OrK3v3cAAAAABAfPH1LOLxINCgoVnZT6UNeCdsFgwVWiDIGGz4FG0YfkcbhocC9Ruy/4GqMsmZXxRszbJgkfN4A34TOQ824Fe0YfgdbRhenws7kJJa5uWZ5AzxiAq2h7ILQ1YzrRazJsKX9J/ituxsq1GjBm0YvkQbht/RhgGg8qH05CE6DyAeKieAn9GG4Xe0YQCoXAjYHgpw8mD4HN3D4Xe0YfgdbRgAKhfGYHuIMdjwO8b+we9ow/A72nD8Ku/4Zj9gDDbiEWOwvZSXZ3mzZ5plZni6GUBZaHrEUKjQgswiDp+iDcPvaMPxPUN34pBhcR+wgXhEwPZYeEOG2fp0rzcDKNsXu8JCCyQkcKo5+BJtGH5HGwaAyocx2AAAAAAAxAABu4w2btxo27Zti8V7AAAAAACIA1U6YK9atcpatWpVpmX79evnQjYAAAAAAFbVA3Z5bN682etNAAAAAABUIr4P2P3797cFCxa433/++WdXkf7+++/d9Y8++sgGDRpkL7/8sg0YMMDatWtnXbt2tVmzZu2wjunTp1uXLl3s2GOPdctELFy40C3Xvn17O+OMM+y7775zt5911lnR5/76668r8NUCAAAAACor388i3q1bN1u0aJEdc8wx7mdycrItWbLEDjvsMPv4449t//33t/Hjx9szzzxjf/vb31wYv+KKK2zgwIHRdairt8K0Hj9ixAh77733bP369XbllVfatGnTXPh+9dVX7YILLrC33nrLnn76aRfkX3/9dfvLX/5S7vNXmi6AD2evDYfDrg0HAl5vDbDnaMPwO9pw/AqEQu7/2JycHPczns+DnZubawkJCe4CVGapqalVI2B3797dJk2a5H5fvHixnXLKKfbFF1/Yueeea5988olNmDDBrrrqKmvatKllZGS4D29+fr798ccfbplAIGCjR4+2pKQk69Wrlx1yyCH24Ycf2q+//uquK8DLaaedZs8//7wL4ieffHJMtr2goMDy8nKtMGd7TNYHAAAA/0vIzbVgfr4tX73GBVAA3lOv5ioRsPVC//Of/9imTZtcd21Vl08//XRbu3atq0wfeuihdtddd9krr7xi9erVs9atW7vlIkcDa9eubTVr1oyur1GjRi6Ia30K5UXp+rp162K27dWqVbOkpGSzlOoxWydQoedfDYUsGAxSwYYv0Ybhd7Th+BVITrbExERr2bJl3FewdVYeVQapYCNe+D5gq/LcsWNHF6ybNWtmLVq0cKH5iSeecNXnefPmua7fb775ptWpU8e2bNlic+bMiS6flZXlut+kpKS462vWrLHevXtbXl6erVix4k+zjh999NEx3X6FE9MF8GnXRAI2/Io2DL+jDcevgDt4HYh+P43ngK0LARvxJC6SnbqJP/nkk9ahQwd3XYF79uzZ1qNHDxeodQRQl+zsbFfNjnTPFn2op06d6rrfKIz/9ttvbjmdhuv99993k57psS+99JItX77c3Sda39atWz181QAAAACAyiRuArZOm6VgLfqpcdaaMfzUU0913b5VzT7++OPdJGgHHHCAC8uiqraqcJrI7KGHHrIHH3zQdRn/61//anfffbcb3x0J7DNmzLCGDRu65bRezSyuMdkAAAAAAATC8Tywo5Ir2Jhp+dPuNFuf7vWmAHtMfznUA0RjpphFHH5EG4bf0YbjV6BRE0saOcaCdepaPNP3CA3XTEtLYww24kZcVLABAAAAAPAaARsAAAAAgBjw/Szifhdo0NAs4PVWAGUQNguGCi0QpIs4fIo2DL+jDcetQP3/zvkDwH8Yg+2hgq1ZFvy/2cwBP4qcBxvwK9ow/I42HN/nwg6kpFo8Yww24hEVbA9lF4asZlotJnWAb/9T3JadbTVq1KANw5dow/A72jAAVD6UnjzEBO6Ih8oJ4Ge0YfgdbRgAKhcCNgAAAAAAMUDA9lCAkwfD5xh/Db+jDcPvaMMAULkwyZmHmOQMfsfkOvA72jD8rqq14aow8VdVwiRniEdMcualvDzLmz3TLDPD080AyiIc1he7Qgtymi74FG0YflfV2rBOXZU4ZBgBG0ClRsD2WHhDhtn6dK83AyjbF7vCQgskJHAud/gSbRh+RxsGgMqn6vQpAgAAAABgLyJgAwAAAAAQAwTsPTR9+nS77rrr3O9Dhw61OXPmxOJ9AAAAAAD4HAEbAAAAAIAYCMbrlP/jxo2zzp07W/fu3W3s2LGWl5dnvXr1spkzZ9oxxxxj7du3t4cfftiefvppO+qoo6xr16721ltvRZe/4447rE+fPtamTRvr16+fLVq0yOuXBQAAAACoxOJyFvF33nnHli1bZh988IEVFBTYOeecY/PmzXP3ffjhh/b666/bt99+a8OGDbOBAwe6x7300ksuVPft29deffVVF6hfeOEFq1mzpt19993u8txzz+2V81eaLoAPZ68Nh8OuDVeF08Mg/tCG4XdVrQ0HQiH3enNyctxP+J+KWrm5uZaQkOAuQGWWmppadQN2Wlqa/frrrzZ37lzr2bOnvfjiixYMBm3atGl2+umnW40aNaxTp07uPySNo05KSnIV7Jtvvtktr8p1jx49XLhes2aNe3xGRuzPVa3wn5eXa4U522O+bgAAgHiSkJtrwfx8W756jQtlAFCR1AO6ygZsheXRo0fbs88+60Jz27Zt7fbbb3f31a5d2/2MHCVTiJZAIBA9Gqru5DfccIN98cUXtu+++9o+++yzV7azWrVqlpSUbJZSfa+sH9jr518NhdzBq6pQOUH8oQ3D76paGw4kJ1tiYqK1bNmSCnYcVbC3bdvmKoNUsBEv4jJgr1y50tq1a2eDBw+2DRs22Pjx423SpEnRIL07U6ZMcVXwTz75xP0hf/fdd23ChAl7ZVv1n6LpAvi0a2JV+WKH+EMbht9VtTYccK8zYCkpKV5vCmIYsHUhYCOexGWy++yzz2zUqFGWmZnpKtbJycnRynVpbNmyxS2jI2np6eluMrT8/Py9us0AAAAAAH+Ly4B92mmnWYcOHezEE090M4lv3rzZBe7SuuKKK+y7775z/ezPPvtsO+6442zTpk3uAgAAAABASQJhpmH0TMHGTMufdqfZ+nTvNgIoR9dEdetST4+q0DUR8Yc2DL+ram040KiJJY0cY8E6db3eFMSI2m9WVpYbmskYbMSLuKxgAwAAAABQ0QjYAAAAAADEQFzOIu4ngQYNzapAty7EobBZMFRogWDV6JqIOEQbht9VsTYcqN/Q600AgN1iDLaHCrZmWbCgwMtNAMolcv5VwK9ow/C7qtaGdS7sQEqq15uBGGEMNuIRFWwPZReGrGZaLSZ1gG//U9yWnW01atSgDcOXaMPwO9owAFQ+VeeQZyXEBO6Ih8oJ4Ge0YfgdbRgAKhcCNgAAAAAAMUDA9lCgKsxIgrhWlcb9IT7RhgEAQCwxBttDNRKCFsjaYnSyhR/p8FBqKEQbhm/5pQ0zqRMAAP5BwPZSXp7lzZ5plpnh6WYAZREOa+xfoQWryOlhEH/80IZ1WqLEIcOYNRkAAJ8gYHssvCHDbH2615sBlC2cFBZaICGBc7nDl2jDAAAg1hhACQAAAABADBCwd2HRokXWq1evWOxnAAAAAECcI2ADAAAAAFBVA/a8efNcZblLly52xx13uN9XrVplL7/8sg0YMMDatWtnXbt2tVmzZrnH677u3bvblClTrHPnztazZ0974YUXoutbunSpnXHGGdahQwcbPHiwff/99396zjlz5thFF11kF1xwgbVp08bOPPNM+/333yv0dQMAAAAAKi/fBewVK1bY9ddf74L1woULLScnx1avXm2hUMjGjx9vkydPtq+++spuvfVWmzRpkm3dutUtt27dOsvPz7ePP/7YrrzySps4caLl5uZaVlaWnX/++faPf/zDPv/8czvvvPNckI4sV9QHH3xgffr0scWLF1vbtm3tqquu8mAPAAAAAAAqo2p+rF737t3bOnbs6K5fe+21rhpdWFhoc+fOtaZNm1pGRoYlJCS4QP3HH39El1WQTkxMtP79+9uYMWMsMzPThfEWLVq4yreccMIJrvL94YcfWv369Xd47latWrkKt4wcOdI9buXKlda8efMyvx4dGDBdAB/OwBwOh10brqynOAL83oYDoZDbRh1M1k+gKH33UbFA33l0AfyGNgw/SU1Njc+AvX79emvcuPEOL7ROnTpWrVo1e/LJJ+2VV16xevXqWevWrd39Rb+Q6HbRY0VfqtLT012XcHUPjygoKHC3Fw/YRYN0cnKye94NGzaUOWDrefLycq0wZ3uZlgcAxLeE3FwL5ufb8tVrXJACAADeaN++fXwG7EaNGtmvv/4avb59+3bbvHmzvfHGG67795tvvumC75YtW9y46d1p2LChG8s9Y8aM6G2qSiuMFx+Lrcp40efdtGmT256yUtBPSko2S6le5nUAnp5DOBSyYDBYaat/gN/bcCA52fW8atmyJRVslFj927Ztmys2UMGGH9GGEY98F7DVvfuUU06xJUuWuCr11KlTXSU4LS3NfQnRJTs72+666y73eN0XqViXpEePHm4894IFC9zkZ+oyrq7kTzzxxJ8e+80339j8+fPdMvfcc48dccQRrkt6eeiLnekC+LR7bWUOJ4Df23DAbVvAUlJSvN4UVNJwogsBG35FG0Y88l2y03jpG2+80UaPHu1mBo+E6mOPPdZVk7t162bHH3+868J9wAEH2PLly3e5vrp169oDDzxgDz74oOsmrrHZ48aNi3YxL+qggw6yF1980VW8f/75ZzcrOQAAAAAAEgj7bNYUzRiu7tnqLif6XTN6q/K8N4/wq7u5TgOmcd6xUrAx0/Kn3Wm2Pj1m6wQqiv5y6MizuiVW1uof4Pc2HGjUxJJGjrFgnbpebwoqIbVfnQ1FvfjoIg4/og0jHvmugq3Jx4YNG2Zr16513b8feughF7DpPgcAAAAA8JLvxmCrG/eZZ55pgwYNchN7tGnTxp3vGgAAAAAAL/mui3g8oYs4/MwP3WsBv7dhuohjV+heC7+jDSMe+a6CHW8CDRqaVdIvdsAuhc2CoUILBCtvOAH83oYD9Rt6vQkAAGAPELC9lJRkSUOGeboJQHlEziEM+JUf2rDOhQ0AAPyBgO2h7MKQ1Uyrxcyf8G23rm3Z2VajRg3aMHyJNgwAAGKtch+2j3MMf0c8VP8AP6MNAwCAWCJgAwAAAAAQAwRsDwUq66w6QClV9rGrwO7QhgEAQCwxBttDNRKCFsjaYnSyhR/p8FBqKEQbRpVow5poLJCSWkFbBgAA/IqA7aW8PMubPdMsM8PTzQDKeg7hUKjQgpX4FEdALNqwTpWVOGQYARsAAOwWAdtj4Q0ZZuvTvd4MoGzhpLDQAgkJnMsdvkQbBgAAscYASgAAAAAAqnrAXrVqlbVq1cqWLFliJ554YoU//5w5c2zo0KEV/rwAAAAAgMrH1wE7okOHDvbGG294vRkAAAAAgCrMdwH7hRdesO7du1vnzp3t+eefd7ctWrTIevXqFa0qX3LJJXb55Zdb27ZtbcCAAfbtt9/axo0b7dBDD7WsrCz3uKeeesoF81Dov3PHTp482SZNmuR+f+SRR+yYY45xzzFy5EjbsGGDu72goMBuu+02t9yxxx5r33zzjUd7AQAAAABQ2fgqYC9dutQmTpxo9957ry1YsMCWLVtW4uN036mnnuqCd5s2bWzq1KlWr149O+igg+yLL75wj9F9eXl59uOPP7rrH3/8sfXo0cNmz57tgvvjjz9uCxcutFq1atmoUaOioXzx4sX21ltv2RNPPGGffvppBb56AAAAAEBl5qtZxOfPn2/HHXectW7d2l0fMWKEvffee396XMuWLa13797u9759+9q4cePc7926dXPBWtXpr7/+2o3bVuBu3LixG8/dvn17F8aHDx9u++23n1tm7NixrmK9du1ae+edd+ycc86xBg0auPs0/lrbVB6ugv5/VXTAbzMwh8Nh14Y5TRfiuQ0HQiH3uJycHPcTqCwKCwstNzfXEhIS3AXwG9ow/CQ1NTX+AnZmZqY1atQoer1Zs2YlPk7V6gj9hxP5QqSu5bfccov99NNP9pe//MWOPvpoe/vtt61u3brWpUsXq1atmutK3rRp0x12pO5XwC7+/EUfVxbqcp6Xl2uFOdvLtR4AwN6TkJtrwfx8W756jQszAACg6mnfvn38BWxVjleuXBm9vn79+j1a/ogjjrD09HR79913rWPHju4yYcIEF6LVPVxUzV6zZk10mezsbNu0aZPVr1/fPb+Wj8jIyCjX61GgT0pKNkupXq71AJ6dQzgUsmAwSAUbcd2GA8nJlpiY6HpHUcFGZav+bdu2zX2PoYINP6INIx75KmD369fPTj/9dHdarsMOO8yNxd4T+s9Hleonn3zSTWimanTNmjVd1++rr77aPUaToj300ENugjOF7dtvv90OOeQQa968ufXv398ee+wx19U8EAi49Sh4l4e+2JkugE+71xKwEe9tOODuD1hKSkpFbh5QqnCiCwEbfkUbRjzyVcA+8MAD3Sze11xzjW3dutWGDBmyx+tQN3EF6kiJX1XsH374wRo2bOiuDxw40M0aPmzYMNu8ebMdeeSRdt9997n7FO5V3VYI139mmklc3c0BAAAAAAiE6e/mmYKNmZY/7U6z9f/rdg74qfqnI8/qGcIkZ4jnNhxo1MSSRo6xYJ26Fbl5wG6p/er0o2lpaXQRhy/RhhGP6JsMAAAAAEAMELABAAAAAIgBAjYAAAAAAFVtkrN4FGjQ0GwXY/+ASitsFgwVWiDIGGzEdxsO1P/vJJgAAAC7Q8D2UlKSJQ0Z5ukmAOUROYcwEO9tWOfCBgAA2B0CtoeyC0NWM60WM3/CtzN/bsvOtho1atCG4Uu0YQAAEGuUnjzEGdIQD9U/wM9owwAAIJYI2B4KcPJg+BzdwwEAAID/oYu4h2okBC2QtcWoAcKPNCdUaihEG0alp/HTgZRUrzcDAABUAQRsL+XlWd7smWaZGZ5uBlAW4bC61xZakFnEUYlpBvDEIcMI2AAAoEIQsD0W3pBhtj7d680AyhawCwstkJDAqeYAAAAAxmADAAAAABAbTHIGAAAAAEAMELCLueGGG+zBBx+Mxb4FAAAAAFQhjMEu5pZbbvHmnQAAAAAA+FqVqWCvWrXKunfvbvfee6916tTJ/b5w4UK7/vrrrV27dta/f39btmyZXXfddTZ9+nS3TK9evdz9evwjjzximZmZNmLECOvZs6e1bt3ahg4dauvWrfP6pQEAAAAAKoEqVcFWGM7Pz7fPPvvM7rvvPrvkkktcxVrdwv/1r3/ZjBkzLBDQ2X3/Z8uWLfbxxx9bXl6e3XbbbVanTh2bP3++5ebmuuWfeuopGz16dJm3KRQK6Z8YvDqg4mcRD4fDrg0X+9gAlUYgFHLtNCcnx/0sqrCw0P0tT0hIcBfAb2jD8DvaMPwkNTW1VI+rUgFbzj33XPdFqmPHjvbkk0/aoEGD3O2dO3e2uXPnWtOmTXd4fJ8+fSwpKcldFKS1YxUo0tPTXdjesGFDmbeloKDA8vJyrTBne7lfFwDgzxJycy2Yn2/LV69xYRoAAKAs2rdvX6rHVbmArVAswWDQ0tLSorfrevHqhjRo0CD6u0L1rbfe6rqbH3jgge7LWo0aNcq8LdWqVbOkpGSzlOplXgfg6XmwQyH32aGCjcoqkJxsiYmJ1rJlyxIr2Nu2bXMHTqlgw49ow/A72jDiUZUL2MW7gO/J46+55hobPny4/f3vf3fXx48fb1lZWeXaHoUT0wXwaRdxAjYqs4A7ABSwlJSUEr/Y6ULAhl/RhuF3tGHEI5LdHtB47MiXtK+++sp1KdeYbgAAAAAACNh74KabbrK7777b9b9X9fq0006zFStW0IoAAAAAABYIlzTwGBWiYGOm5U+702x9OnscvqO/HOrapbGrjMFGZRVo1MSSRo6xYJ26f7pP7VfDfDQfB2Ow4Ue0YfgdbRjxiAo2AAAAAAAxQMAGAAAAACAGCNgAAAAAAMRAlTtNV2UTaNDQbM/OHAZUDmGzYKjQAkHGYKPyCtRv6PUmAACAKoSA7aWkJEsaMszTTQDKIxQK/fdc7kAlFkhO9noTAABAFUHA9lB2YchqptVi9lr4dubPbdnZVqNGDdowAAAAwBhsb3GGNMRDBRsAAADAf9G300MBTh4Mn6N7OAAAAPA/dBH3UI2EoAWythg1QPiR5uZLDYVow9ixXSQnWyAllb0CAACqJAK2l/LyLG/2TLPMDE83AyiLcFhdxAstyCziKDJjd+KQYQRsAABQZRGwPRbekGG2Pt3rzQDKFrALCy2QkMCp5gAAAADGYAMAAAAAEBtMcgYAAAAAQFUK2KtWrbJWrVqVez1ah9YFAAAAAECVDNgAAAAAAFRmvgvY06dPty5dutixxx5rH330kbtt6dKldsYZZ1iHDh1s8ODB9v3337vbFy1aZCeffLKdddZZ1rlz52jl+vnnn7fu3btb165d7ZVXXomue+XKlTZ8+HDr2LGjnXTSSdH1X3TRRfbUU0+537OysuyQQw6xt99+211fsWKFHX300RbWjE8AAAAAgCrLd7OIb9y40RYuXGgff/yxjRgxwl5//XU7//zzbezYsXbCCSfYO++84wJxJAD/+OOPdu+997pQXrNmTXfbsmXL7M0337Tly5fbOeecY4ceeqjtv//+brlTTjnFHnzwQfvyyy/d+hXAFcY///xzGzJkiH3xxRdWrVo19/P4449329GtWzcLBHRW4D0XCoX0T0z3EVARdExJB5bUhsvY/BFnAqGQaxM5OTm+OOhYWFhoubm5lpCQ4C6A39CG4Xe0YfhJampq/AVshdjRo0dbUlKS9erVy1WSFXRbtGhhAwYMcI9RyJ41a5Z9+OGHVr9+fUtOTnbV7qIB+KqrrrIaNWpY69at7bjjjnOh/KijjrKtW7fahRde6B5z5JFHuuCsAN+3b1+bNm2a+8K4ePFiF8KXLFniHvfJJ5+4KnlZFBQUWF5erhXmbI/J/gEALyXk5lowP9+Wr17jgisAAEC8aN++ffwF7Nq1a0er0NKoUSPLyMhwXcLVPbxocE1PT3cBW5fi1eUmTZr8aR16/IYNG3ZYj46q1alTx/bdd1/386effnLdzu+88047/fTTLTMz01W6db0sVAlPSko2S6lepuUBz8+DHQpZMBikgg0nkJxsiYmJ1rJlS99UsLdt2+aOSFPBhh/RhuF3tGHEI18FbI1/VtfDlJQUd33NmjWu+7a6f8+YMWOHsdT16tVzwbukrtsK1JGgrnVoZvGGDRvafvvtZ/PmzYs+TqFblW5RNXv+/PkuVB9wwAF2xBFH2COPPGIHHXSQ1apVq8yvSeHEdAF82kWcgI2IgDvYEoj+jfbDFztdCNjwK9ow/I42jHgU9NuHcOrUqa7roYLwb7/95iYkU5BesGCB+7KvirK6i2t89c7cc889rmqh7uUffPCB9evXzwXm/Px8e+6559zzaPlBgwa5LuCRgK2Jztq1a+eu63lnz57tAj4AAAAAAL6qYKubtkK0KtbNmzd3k5E1a9bMHnjgAZs4caJdffXVVrduXRs3bpwbX63u3CX561//aj169HBVbnXv1rpE67vtttvsrrvuchUYTYCm8C2ahVzVcwVr0U+Ffa0HAAAAAIBA2A8D5eJUwcZMy592p9n6dK83Bdhj+suh3h4au8os4pBAoyaWNHKMBevU9cUOUfvV0KO0tDTGYMOXaMPwO9ow4pGvuogDAAAAAFBZEbABAAAAAKhqY7DjUaBBQ7M/T3QOVH5hs2Co0AJBuojjvwL1G7IrAABAlUbA9lJSkiUNGebpJgDlETkPNlD0XNgAAABVFQHbQ9mFIauZVovJdeDbiUm2ZWe7c8VrojMAAACgqqP05CEmcEc8VLABAAAA/BcBGwAAAACAGCBgeyjAyYPhc4y/BgAAAP6HMdgeqpEQtEDWFqOTLfxIk9+nhkK0YR9OQhZISfV6MwAAAOISAdtLeXmWN3umWWaGp5sBlEU4rDHYhRbkNF2+Oo1W4pBhBGwAAIC9hIDtsfCGDLP16V5vBlC2gF1YaAHNIM653AEAAADGYAMAAAAAUKUnOTvxxBNtyZIltmrVKmvVqpW7bc6cOTZ06NByrff555+3Y4891tq3b29DhgyxZcuWxWiLAQAAAADxzLcB+4033rAOHTrEdJ1fffWVTZkyxe6//35bvHixdevWzS677LKYPgcAAAAAID5V+oBdWFho48aNs86dO1v37t1t7NixlpeXZ7169bJFixaVuMy2bdusTZs29tNPP0Vvu/fee+2GG25wywwcONDGjBljbdu2tZNPPtm+/fZb95h169bZ8OHD7cADD7SEhAQ766yz7LfffrNNmza56vhFF11kF1xwgVv3mWeeab///nuF7QcAAAAAQOVW6QP2O++847ppf/DBB65qrdA8b968XS6TmppqPXv2dMtGvPnmm65buSxdutSaNWvmwvagQYNclVqhvV+/fnb++edHl1m4cKE1bNjQ6tat665rG/r06eOq2wrnV1111V573QAAAAAAf6n0s4inpaXZr7/+anPnznWh+cUXX7RgMGjTpk3b5XIK03rMFVdc4QL6H3/8YR07drQvvvjC6tSpY5deeqlVq1bNjdl+9NFH7csvv7QuXbpEl//mm29cxfu2226L3qax3oMHD3a/jxw50mbNmmUrV6605s2bl/n1hUIh/VPm5QGvaBbxcDjs2nCAWcR9IRAKufcsJyfH/azq1EMqNzfX9VjSBfAb2jD8jjYMP1ERNy4CdteuXW306NH27LPP2s033+wqx7fffvtul1N38uuuu86Fc1Wv+/bt64K5NGnSxIXriEaNGllmZmb0+oIFC+zqq692y6uqHVE0SCcnJ7ugvmHDhjIH7IKCAsvLy7XCnO1lWh4A9kRCbq4F8/Nt+eo1LlgCAACgdDQJdlwEbFWI27Vr5yrHCrPjx4+3SZMm7XY5BeDevXvbe++9Z++++67ddNNN0fu0HlVvAoGA+7l27VoXsuWll16yiRMn2uTJk13FvKiMjIzo79u3b3djsyPLlYVCflJSsllK9TKvA/D0PNihkDtwRQXbHwLJyZaYmGgtW7akgv1/lRPN2aEj0lSw4Ue0YfgdbRjxqNIH7M8++8yefvppe+yxx6x27douONeoUaNUy55wwgkuKGdnZ7uJyYoGZXXv/sc//mHPPPOMC7q6X6f9uvXWW+3xxx93lfLi1G18/vz51qNHD7vnnnvsiCOOsKZNm5br9bmq+v9V1gE/dhEnYPtHwB0MCVhKSorXm1JpvtjpQsCGX9GG4Xe0YcSjSp/sTjvtNHc6Lo2p1kzimzdvtlGjRpVq2aOPPtrNDK7u4fpSGaGJy/7973+7MdeaOO3BBx90VR2FbnWbHDZsmAvYkcuaNWvccgcddJAbA67lfv75Z3dKLwAAAAAAfFHBVrc9naZLl6Lef//96O+R03Hp9Fu6RCg0N2jQIDp7eERSUpLdcccdf3qu3U2cVqtWLXvooYfK/FoAAAAAAPGr0gfsslq9erV9/vnnLqAfeuihXm8OAAAAACDOxW3Afvjhh+3tt9/ebVUaAAAAAIBYCIQ5GapnCjZmWv60O83Wp3u3EUA5JjnT5CTqJcIs4v4QaNTEkkaOsWCdul5vSqWg9puVlWVpaWnMIg5fog3D72jDiEdxW8H2i0CDhmb/m38N8I+wWTBUaIEgAdsvAvUber0JAAAAcY2A7aWkJEsaMszTTQDKI3IebPjrXNgAAADYOwjYHsouDFnNtFp0TYRvu3Vty85256VXN3EAAACgqqP05CGGvyMeKtgAAAAA/ouADQAAAABADBCwPRRg6mX4HOOvAQAAgP9hDLaHaiQELZC1xehkCz/S5PepoZAnbVgTdQVSUiv4WQEAAIBdI2B7KS/P8mbPNMvM8HQzgLKeBzsUKrRgBZ+mS6eaShwyjIANAACASoeA7bHwhgyz9elebwZQtoBdWGgBzSDOudwBAAAAxmCXR35+vq1bt45mBAAAAACIz4B94okn2pIlS3b7uLZt29qaNWvK/DyjRo2yTz75pMzLAwAAAADiR1x2EX/jjTdK9bivv/66XM+zefPmci0PAAAAAIgflaaC/emnn9qAAQNcVfn000+3X375xTIzM23kyJHWuXNn69Onj73yyivRx+/qvl69etmiRYvc761atbInnnjCunbtakcffbTdf//90cfpvlWrVrnfly5dameccYZ16NDBBg8ebN9///0ut23KlCmuSn7jjTfajBkzKmgvAQAAAAAqq0pRwc7IyLDLL7/cbr/9djv22GPtscces9GjR1uDBg2sRYsW9tFHH9mKFSvs/PPPt/3339+OOOIIu+aaa3Z6X3Hffvutvfvuu/b//t//s2HDhtnAgQOtcePG0fuzsrLc8mPHjrUTTjjB3nnnHbvooovs7bfftpycnBK3be7cufbVV1/Zqaee6tYHAAAAAKjaKkXAXrhwoR188MGuEi1nn322tW7d2s477zy77777LCkpyQ466CAXZOfMmWNNmjRxFeqS7ispYJ9zzjlWvXp1O/LII11oX7ly5Q4BW8+vsK4qtShkz5o1yz788EPbtm3bn7atU6dOMXvtoVBI/8RsfUBFziIeDoddG67Q03SFQu55dfBLP4GyKiwstNzcXEtISHAXwG9ow/A72jD8JDU11T8BW929GzVqFL2u0KxArA9dt27dorfrurqEr127dqf3laRevXrR36tVq/bfUFtEenq66xKu7uERBQUF7nb9LL5tCv+xoHXn5eVaYc72mKwPqAoScnMtmJ9vy1evceEIAAAA2Nvat2/vn4CtAKuu3kVPf/XMM8+4isLixYstGAxGu5Lrtu3bt1tKSkqJ95VFw4YNrUuXLjuMpVaVW8F8/vz5f9q2yZMn21VXXWXlpbCflJRsllK93OsCPDkPdijkPoMVWsFOTrbExERr2bIlFWyUiw7MqpeSjkhTwYYf0Ybhd7RhxKNKEbC7d+9u48ePt/fff9969uxpTz75pP3000/Wpk0bmzZtml166aW2adMm12VcXcE1XvqQQw7Z6X17qkePHnbHHXfYggUL3PNrbLXWo8nRSto2Bfvk5GRXzd66dWu5Xrs7QPB/BwkAP3YRr/CA7Z4v4A6yAeX9YqcLARt+RRuG39GGEY8qRbJTpVjjqadPn24dO3Z0QVezdN999922fPly1xX8lFNOcWFXQVp2dd+eqlu3rj3wwAP24IMPum7iY8aMsXHjxrmu4Dvbtsj5tvV75DoAAAAAoOoKhKvoLEF62Zoc7YMPPnCTpnmhYGOm5U+702x9uifPD5SH/nLoyLO61lZoBbtRE0saOcaCdepW3JMiLqn96iwSaWlpdBGHL9GG4Xe0YcSjStFFvKJpcrFly5a5rq21a9f2enMAAAAAAHGgUnQRr2iff/65nXnmmXbWWWeVerp1AAAAAAB2pUpWsLt27Wpff/2115sBAAAAAIgjVTJgVyaBBg3NKnD8KhAzYbNgqNACwQoeg12/YcU9GQAAALAHCNheSkqypCHDPN0EoDwi58GuaDoXNgAAAFDZELA9lF0YsppptZi9Fr6d+XNbdrbVqFGDNgwAAABU1UnOKosqeoY0xFkFGwAAAMB/EbA9FKjIgavAXuBF93AAAACgsqKLuIdqJAQtkLXFqAHCj3R4KDUU2ittWGOsAymcQg8AAAD+QsD2Ul6e5c2eaZaZ4elmAGWhEQ6hUKEFYzyLuGYJTxwyjIANAAAA3yFgeyy8IcNsfbrXmwGULWAXFlogIYFTzQEAAACMwQYAAAAAIDaYoQgAAAAAgBggYAMAAAAAEAMEbAAAAAAAYiBuAva8efOsV69e1qVLF7vjjjvc76tWrbKlS5faGWecYR06dLDBgwfb999/7x6v+44++mi78847rV27dtanTx9buHChu++6666z22+/Pbru1atXW8eOHS0vL8+efvpp69mzp3Xu3NmGDRtmK1eu9Ow1AwAAAAAqj7iYRXzFihV2/fXX20MPPWRHHHGETZgwwYXirVu32vnnn29jx461E044wd555x276KKL7O2333bLbdiwwTZt2mSfffaZffTRR3bllVe6x/Tr189uvvlmF7TlzTfftN69e9uWLVtsypQp9tprr9k+++zjHqPnvO2228q87aFQSP/EbF8AFTmLeDgcdm04pqfpCoXcenNyctxPYG8pLCy03NxcS0hIcBfAb2jD8DvaMPwkNTW16gRsVa8VgFVllmuvvdZeeOEFV5Fu0aKFDRgwwN2ukD1r1iz78MMPrXXr1tHHJicn27HHHmsHH3ywW+aUU06x7OxsV+0+7LDD7K233rIRI0ZY9erVLT8/355//nm3rptuusmCwbJ3AigoKLC8vFwrzNkeoz0B+F9Cbq4F8/Nt+eo1LvwAAAAAXmvfvn3VCdjr16+3xo0b73B0oU6dOi78KiSre3jRUJuenu4Cdq1atdzjIrSOzMxMq1atmusyrmq27ld38qOOOsrd/sgjj9iDDz5ojz76qDVq1MjGjRtn3bt3L9N2a31JSclmKdXLuQcAj86DHQq5z1lMK9jJyZaYmGgtW7akgo29XjnZtm2b+z+DCjb8iDYMv6MNIx7FRcBW0P3111+j17dv326bN2921S+NyZ4xY0b0Po2Zrlevnusari7k6oaakpLi7luzZk00LKtCra7feqzCtsKw1qmfCtdaTuOxr7nmGlu0aFGZt91VwMtRBQe87iIe84Dt1heIfi6BvfnFThcCNvyKNgy/ow0jHsVFsuvfv7+99957tmTJEjcR2dSpU12lWpORqYK9YMECFwS+/PJL1118+fLlbjlV36ZNm+a6fc+fP9/d3qNHD3dfp06d7I8//rCXXnrJjcmWjRs32vDhw93Eafryrwp47dq1PX3tAAAAAIDKIS4q2BpnfeONN9ro0aNd1VqzhauLacOGDe2BBx6wiRMn2tVXX21169Z1XbrVPVzdvkXBu2vXrm7Ssvvvv989RtRd8Pjjj3cTomnGcPnrX//qJky74oorXAV8//33t7vuusvT1w4AAAAAqBziImBrxvDDDz88epotdRHXWGlVmNV9/LnnntvpspopPDJbeHEak923b98dJjJTeNcFAAAAAIC46yKuSct0Tuq1a9e6ruE6dVbbtm3LPIZTXcPVDVyzhZ988skx314AAAAAQPyJiwq2Zgk/88wzbdCgQW5G2DZt2tikSZPKvL5ffvnFnT/773//u6uMAwAAAACwO4GwBiHDEwUbMy1/2p1m69N5B+A7+suh2T81X0FMZxFv1MSSRo6xYJ3/zocA7C1qv1lZWZaWlsZpuuBLtGH4HW0Y8SguKth+FmjQ0CyG4QSoMGGzYKjQAsEYB+z6DWO3MgAAAKACEbC9lJRkSUOGeboJQHnoVHdFJwGMlUBycszXCQAAAOxtBGwPZReGrGZaLbomwrfdurZlZ1uNGjVowwAAAEC8zCLuVwx/RzxUsAEAAAD8FwHbQ4FYDlwFPLA3uocDAAAAfkUXcQ/VSAhaIGuLUQOEH+nwUGootMdtWOOrAympe3HLAAAAAG8QsL2Ul2d5s2eaZWZ4uhlAWU/TFQoVWnAPZhHXDOGJQ4YRsAEAABCXCNgeC2/I4DzY8G/ALiy0QEICp5oDAAAAGIMNAAAAAEBsMEMRAAAAAADxELBXrVplrVq1itn61qxZY23btrVYOv/8823u3LkxXScAAAAAIL7E3Rjspk2b2tdffx3TdT7yyCMxXR8AAAAAIP54XsGOmD59unXp0sWOPfZY++ijj9xtS5cutTPOOMM6dOhggwcPtu+//z5a9e7evbtNmTLFOnfubD179rQXXnihxIr4jBkzrF+/fq6q3atXL5s3b567fdGiRXb66afbjTfeaO3atbPjjz/elixZYpdddpl7rO7LyPjv7N5Dhw61OXPmuN+1jocfftj91HPfdNNNFb6vAAAAAACVT6WpYG/cuNEWLlxoH3/8sY0YMcJef/111zV77NixdsIJJ9g777xjF110kb399tvu8evWrbP8/Hz3+DfeeMNuueUWGzBgwA7rVIh++umn7bnnnrN99tnHZs+ebRMmTHDrk//3//6fnXrqqS5k//Of/7Rzzz3XHnroIZs8ebJdcMEF9swzz7htKe6TTz6xV155xdauXeuCuJ5XIb0sQqGQ/inTsoDXs4iHw2HXhkt9mq5QyC2Tk5PjfgJeKiwstNzcXEtISHAXwG9ow/A72jD8JDU11T8BOxAI2OjRoy0pKclVhg855BD74osvrEWLFtHQrFA8a9Ys+/DDD61169buNgXwxMRE69+/v40ZM8YyMzN3WK8eFwnXCuTJycnRqrTUqFHDBWQ9f/v27e3HH3+0o48+2t2nqnl6enqJ26uqeq1atdzloIMOsv/85z9lCtgFBQWWl5drhTnb93hZwI8ScnMtmJ9vy1evccEGAAAA8APlRd8E7Nq1a1vNmjWj1xs1auSCsLqEK+gWDaQKvZGAXa9ePfezWrVq/6sGF6HgPHXqVHv//fetSZMm1rJlyz89rx4jql6kpaVF7wsGgzutsEWeN/LcxZ+3tLRsUlKyWUr1Mi0PeH4e7FDIfVZKXcFOTnYHxfRZpIKNylA52bZtmzsiTQUbfkQbht/RhhGPKkXAzsrKcl1GU1JSojOBa4y1xmRrDHXEypUrXbjdtGlTqdY7c+ZM1437gw8+cOv+4YcfXNfziEi49pLCiekC+LSL+B4FbPfYQPSzDnj9xU4XAjb8ijYMv6MNIx4FK8uHS5VmdRnVJGS//fabdezY0VWwFyxY4L7Ef/nll667+PLly0u93i1btrhu4apMaIz3Pffc427X2G0AAAAAAOIuYNepU8eFaFWsNcnYgw8+aM2aNbMHHnjA/a5u4hpjPW7cuGj38NLQpGVbt251s30PHDjQDj/8cNctfE9COgAAAAAApREIx9lASHUj79Onj+sOXtkVbMy0/Gl3mq0veTI1oDLTXw71PlEPkVJ3EW/UxJJGjrFgnbp7e/OA3VL71RAlzb/BGGz4EW0YfkcbRjyqFBXsWMnLy7Nly5btMAkZAAAAAAAVIa4C9quvvupO93Xeeed5vSkAAAAAgCqmUswiHiuDBw92Fz8JNGho5v1k5sCeC5sFQ4UWCO5BF/H6DdnTAAAAiFtxFbB9JynJkoYM83orgDKLnAd7T+hc2AAAAEA8ImB7KLswZDXTajG5Dnw7Mcm27GyrUaMGbRgAAACItzHYfhNnE7ijilawAQAAAPwXARsAAAAAgBggYHsoUNqZoYBKak/HXwMAAADxjDHYHqqRELRA1hajky38SIeHUkOhUrVhTWwWSEmtoC0DAAAAvEHA9lJenuXNnmmWmeHpZgBloSkEQqFCC+7mNF06NVfikGEEbAAAAMQ9ArbHwhsyzNane70ZQNkCdmGhBRISOJc7AAAAwBhsAAAAAABigxmKAAAAAACIAQJ2MYsWLbJevXrFYt8CAAAAAKoQAjYAAAAAAJU1YK9atcq6d+9u9957r3Xq1Mn9vnDhQrv++uutXbt21r9/f1u2bJkNHTrU5syZE13uuuuus+nTp7vf33rrLevTp49b/owzzrClS5dGHzdz5kzr3bu3denSxf75z39adna2u72wsNCmTJnibu/cubPdcMMNVlBQ4O6bMWOG9evXz9q2besq1PPmzYuu74EHHrAjjzzSunXrZgsWLNjhtTzyyCN2zDHHuPWNHDnSNmzY4G7Xdl566aVuO0455ZS9sRsBAAAAAD6y12YRX7duneXn59tnn31m9913n11yySV2yy23uND7r3/9ywXenVFQVnCePXu2tWrVygXgu+66yx5//HF77bXX7JlnnnG/169f3z1u0qRJdvPNN7vb33vvPXvppZcsLS3Nzj//fHvqqafs4IMPtqefftqee+4522effdx6J0yYYCeccILNnz/fLaf7atWqZRdddFF0O/S4559/3j1X48aNbfz48TZq1CibNWuWu3/x4sX28ssvW926dcu8n0KhkP4p8/KAl7OIh8Nh14Z3eZquUMg9Licnx/0EKgv9X5Obm2sJCQnuAvgNbRh+RxuGn6Smpnp/mq5zzz3XfWnp2LGjPfnkkzZo0CB3u6rBc+fO3elyWqZGjRou9J566qku9KpaLK+88ooNHz7c9t13X3f9yiuvdBXkm266yd588033nE2bNnX33X333RYIBFwAjoRrBf/k5GTLyPjvuaffeecdt10tWrRw1/VcCtKiMK/n2m+//dz1sWPHWocOHWzt2rXu+hFHHGHNmzcv8/5RdT0vL9cKc7aXeR1AZZeQm2vB/HxbvnqNCzMAAACA37Rv3977gF2nTh33MxgMuopyhK7vrpKlrtnqhq3ArLA9evRoF6TT09Nt4sSJrmodoQpaZmam677dqFGj6O3NmjVzP7dv325Tp061999/35o0aWItW7aMPkbLKTQXX0Y2btwYDeuRoxYK65GA3aBBAyuPatWqWVJSsllK9XKtB/DsPNihkPs877KCnZxsiYmJ7nNHBRuVrXKybds297edCjb8iDYMv6MNIx7t1YCt6vHu7tcHK2Lz5s0u4OoLj37XGO68vDxXZb722mvd2GmF2ssvv9x17xbdv3r1atddXOF6/fr10fUtWbLEjQdXINblgw8+sJSUFPvhhx/s9ddfd4/R+tasWRNdpujy6hZe9D6N9d60aZN7rtK8vtJQODFdAJ92Ed9twHb3B9xnD6hM9P+PLgRs+BVtGH5HG0Y88jTZqVu2xkCrq/Q333zjTpEV+bBdfPHF9tFHH1lSUpKrGlevXt117T7ppJNcdVuVbI3x1qRmV1xxhVvuxBNPdBOgqRv4li1bbPLkyZaVleV+17KqUKgqfc8997jHa3kt8+KLL9ovv/ziHvfwww9Ht2/AgAH26KOP2m+//eaq4Lfffrsdcsgh5eoWDgAAAACIT3u1gr07F154oY0ZM8bNFH744Ye78CzqTq5JzTQRmSrPqiSri7dCssZLq4p81lln2R9//GGHHXaY60quCpnuU7gePHiwq2yffPLJ7nHqOq7JyTT2u3bt2u7+r776ypYvX249evRw23HOOee459aY70jVeuDAgW7ZYcOGuYq6ZhrXhG0AAAAAABQXCDMo0jMFGzMtf9qdZuvTvdsIoBxdxNXbRD1DdtlFvFETSxo5xoJ1yj7bPrA3qP2ql5MO6jIGG35EG4bf0YYRjxj8CwAAAABADBCwAQAAAADw+xhsmAUaNDQr/2TkQMULmwVDhRYI7qaLeP2GFblVAAAAgGcI2F5KSrKkIcM83QSgPCLnwd4dnQsbAAAAiHcEbA9lF4asZlotJteBbycm2ZadbTVq1KANAwAAAIzB9hYTuCMeKtgAAAAA/otJzgAAAAAAiAECtocCu5oZCgAAAADgK4zB9lCNhKAFsrYYnWzhB5qoLJCS6vVmAAAAAJUWAdtLeXmWN3umWWaGp5sB7I5OtZU4ZBgBGwAAANgFArbHwhsyzNane70ZAAAAAIByYgz2/1m1atUe77z8/Hxbt25ded8DAAAAAEAcIGCb2YYNG6x///6l2mHnn3++zZ071/0+atQo++STT/buOwQAAAAA8AW6iJvZ9u3bLScnp1Q77JFHHon+vnnz5r33zgAAAAAAfCUuK9gvv/yyDRgwwNq1a2ddu3a1WbNm2aJFi6xXr147dAlv1aqV+33IkCHuZ9u2bV2Xb11GjBhhnTt3tt69e9tjjz1m4XDYPWbo0KE2Z84cmzJlii1ZssRuvPFGmzFjhkevFAAAAABQWcRdBfs///mPjR8/3p555hn729/+ZgsWLLArrrjCpk2bttNlnnrqKRekv/76a3d98ODBdvDBB9vChQstPT3dLrjgAqtbt66deuqp0WWuuuoq++qrr9xtAwcOrJDXBgAAAACovOIuYDdu3NiNkW7atKllZGRYQkKCm4zsjz/+KNXyv//+u/373/92Ve/q1avb/vvv78Zdv/rqqzsE7FgJhUL6J+brBWIpEAq5XhwaShHpzVFYWGi5ubnuM6YL4De0YfgdbRh+RxuGn6SmplbNgF2tWjV78skn7ZVXXrF69epZ69at3e2RULA7GzdudNXqlJSU6G0K62vXro35thYUFFheXq4V5myP+bqBWErIzbVgfr4tX73GhWoAAACgKmnfvn3VDNhvvPGGffzxx/bmm29anTp1bMuWLW7MdPPmzV2g3d0EZaqAb9q0ybZt2xY9SqHx2vXr198rBwOSkpLNUqrHfN1ALAWSky0xMdFatmy5QwU78jmhgg0/og3D72jD8DvaMOJR3AVsBWoFAV2ys7PtrrvucrcHAgF3Oi5NTHb44Ye7LuARSUlJ7ufWrVutSZMmbrKzO+64w8aOHevGYGuSs/POO+9Pz6XltEx5BINB/VOudQB7WyAYdJ+hoj079J+iLgRs+BVtGH5HG4bf0YYRj+Iu2WmcdKNGjaxbt252/PHHW3Jysh1wwAFuDPbIkSPtyiuvdBOaKURHNGzY0D1el19++cUmT57swniPHj3s7LPPttNOO83+8Y9//Om5TjzxRDebuC4AAAAAgKotEC7t4GTEXMHGTMufdqfZ+nT2Liq1QKMmljRyjAXr1N3hqHNWVpalpaXRRRy+RBuG39GG4Xe0YcSjuKtgAwAAAADgBQI2AAAAAAAxQMAGAAAAACAG4m4Wcb8JNGhoFvB6K4BdC9RvyC4CAAAAdoOA7aWkJEsaMszTTQD25FzYAAAAAHaOgO2h7MKQ1UyrxQzMAAAAABAHGIPtIc6QBgAAAADxg4DtoUCAwdcAAAAAEC/oIu6hGglBC2RtsZCXGwHsZtx1ICWVfQQAAACUAgHbS3l5ljd7pllmhqebAexs5vDEIcMI2AAAAEApEbA9Ft6QYbY+3evNAAAAAACUE2OwAQAAAACIAQI2AAAAAAAxELcBe9WqVdaqVauYrrNXr162aNGimK4TAAAAABAf4jZgAwAAAABQZQL2vHnzXFW4S5cudscdd7jfVXl++eWXbcCAAdauXTvr2rWrzZo1yz1e93Xv3t2mTJlinTt3tp49e9oLL7wQXZ9+1/267/nnn9/huV5//XU7/vjj3TqHDRtm69atc+vT+keMGGGdOnWyL7/80r799lsbMmSIHXnkkda+fXsbO3asFRYWRtezYMEC6927t3uOGTNmVODeAgAAAABUZp4F7BUrVtj111/vgvXChQstJyfHVq9ebaFQyMaPH2+TJ0+2r776ym699VabNGmSbd261S2nYJyfn28ff/yxXXnllTZx4kTLzc21pUuXut/vvfdeF4KXLVsWfa4ff/zRbrjhBrfeL774wv7617/aTTfd5O7LyMhwoVvbcMQRR7h1Dho0yD7//HN75ZVX3Lo+/fTT6Lq+/vprF+SfffZZe+KJJ9xyAAAAAABU87J6rUpwx44d3fVrr73WBVdVi+fOnWtNmzZ14TchIcEF6j/++CO67Pnnn2+JiYnWv39/GzNmjGVmZtr8+fPtuOOOs9atW7vHqCr93nvvud/ffvttO/bYY61Dhw7u+siRI12YjzjhhBMsJSXF/T5z5kzbd999LSsry623du3atmHDhuhjL7nkEqtXr567DB482K27R48eZd4POqBgugCVTCAUsnA47A5+6Wdx+qzq4JY+o7oAfkMbht/RhuF3tGH4SWpqauUO2OvXr7fGjRvvsMF16tSxatWq2ZNPPumqxwqxkcBc9Au+bhc9NhJSFYYbNWoUfUyzZs2ivxe/Ly0tzQ466CDXRVwaNmwYvU9Vc3Uh1wf+0EMPdeG+6HM3adIk+rvW+f3335d5HxQUFFheXq4V5mwv8zqAvSUhN9eC+fm2fPUaF6QBAACAqqp9+/aVO2ArnP7666/R69u3b7fNmzfbG2+84bp/v/nmmy5wb9myxebMmbPb9TVo0MBWrly5Q4CP2GeffXa4TxXpp556ynUFl0Ag4H6uXbvWdSV/8cUX7cADD3S3nXzyyTs8j6rqkdnJ16xZs0Pg3lM6QJCUlGyWUr3M6wD2lkBysusp0rJly51WsLdt2+YOjlHBhh/RhuF3tGH4HW0Y8cizgK3u3aeccootWbLEVamnTp3qKrqqLutLvS7Z2dl21113ucfrvkjFuiT9+vWz008/3a3vsMMOc2OxI/r27WtnnHGGffPNN64q/dBDD7kwXZzGeStsV69e3X3gFex/+ukn99wRDzzwgFu/Ary6tN93333l2g/BYFD/lGsdwN4QCAbd5yEyfKI4fUZ0IWDDr2jD8DvaMPyONox45FnAbtGihd144402evRo1/1U45kVqjVWWhXsbt26uS/uCs4HHHCALV++fJfntVbF+bbbbrNrrrnGBWXNBB6h5TXB2XXXXeeCscZ9T5gwwVXfitLjzj33XFfZVrBo27atC+d67gjdpjHbSUlJNmrUKDdBGgAAAAAAgXBJfT8rgCYZU7dwdT8V/a7wqjHQO6uYxZuCjZmWP+1Os/XpXm8K8CeBRk0saeQYC9apu9OjzpoMUL1O6CIOP6INw+9ow/A72jDikWd9k9PT091kYuqqrS7Y6ratgF1VwjUAAAAAIL541kVcp8w688wzXXdsddVu06aNO981AAAAAAB+5FnAlosvvthdAAAAAADwO08DNswCDRqa/fcsYUClEqj/v/PDAwAAANg9AraXkpIsacgwTzcB2N25sAEAAACUDgHbQ9mFIauZVosZmAEAAAAgDng2izjMPDpDGgAAAABgLyBgeygQYPA1AAAAAMQLuoh7qEZC0AJZWyzk5UYA/zfWOpCSyr4AAAAAyoGA7aW8PMubPdMsM8PTzUDVptnCE4cMI2ADAAAA5UTA9lh4Q4bZ+nSvNwMAAAAAUE6MwQYAAAAAIAYI2AAAAAAAVNWAvWrVKmvVqtUeLbNo0SLr1avXXtsmAAAAAEDV5suADQAAAABAZeObgP3CCy9Y9+7drXPnzvb888+72+bMmWNnn322nXTSSdajRw/LycmxhQsX2oABA6x9+/Z2xhln2HffffendWm5Sy65xC6//HJr27ate/y3337r7ps+fbpdd911Ozx26NChFgqF3Ppuu+02d/u6deusU6dO9vHHH1fYPgAAAAAAVF6+mEV86dKlNnHiRJs5c6YdcMABdvXVV0fvW7JkiT377LP217/+1X7//Xe78sorbdq0adalSxd79dVX7YILLrC33nrrT+tcsGCB3XfffXb33Xe70Dx16lR77LHHdroNwWDQxo8fb6eddpqdeuqpLoj369fPunbtWq7XpuBuugAeCYRCFg6H3QEq/SytwsJCy83NtYSEBHcB/IY2DL+jDcPvaMPwk9TU1PgJ2PPnz7fjjjvOWrdu7a6PGDHC3nvvPfd78+bNo7e/+eabbpx1t27d3HWFYVW7VdVu3LjxDuts2bKl9e7d2/3et29fGzdu3G63Q8tcdNFFdvHFF1tycrJNnjy5XK+roKDA8vJyrTBne7nWA5RHQm6uBfPzbfnqNS4wAwAAANiRekjHTcDOzMy0Ro0aRa83a9Ys+nvDhg2jv2/atMmaNm26w7K6ru7cxQN2vXr1or+r+lbayt3AgQNdhXzIkCFWo0YNK49q1apZUlKyWUr1cq0HKI9AcrIlJia6A0h7WsHetm2bO5pHBRt+RBuG39GG4Xe0YcQjXwTsBg0a2MqVK6PX169fH/09EAhEf1cIX7FixZ9mHD/66KNL/VxanyrLEZs3b97h/gkTJtixxx5rL7/8sg0aNGiPZzMvqeu56QJ4JBAMunafkpKyx/8p6kLAhl/RhuF3tGH4HW0Y8cgXyU5jndUlXOOtt2/fbvfee+9OH/f+++/bRx995ELySy+9ZMuXL3cToJVWixYt3Cm9Nm7c6IK8gnTEu+++67ZBY7GHDx9u//rXv9wfBgAAAAAAfBGwDzzwQDcR2TXXXOPGV++3334lPk4TnWnSskmTJlnHjh1t9uzZNmPGjB26ke/O8ccf72YH17jsc845x0444QR3e1ZWlt18881uhvFatWq5gL1161Y38RoAAAAAAIHwngy6REwVbMy0/Gl3mq1PZ8/CM4FGTSxp5BgL1qm7R8up94YOPKWlpTEGG75EG4bf0Ybhd7RhxCNfVLABAAAAAKjsCNgAAAAAAFSVWcTjWaBBQ7P/TYQOVHwbrF/6OQoAAAAA7BwB20tJSZY0ZJinmwBEzoUNAAAAoHwI2B7KLgxZzbRaTBAFAAAAAHGAMdgeYgJ3AAAAAIgfBGwAAAAAAGKAgO2hQIDZzQAAAAAgXjAG20M1EoIWyNpiIS83AlViArNASqrXmwEAAADEPQK2l/LyLG/2TLPMDE83A/F9Cq7EIcMI2AAAAEAFIGB7LLwhw2x9utebAQAAAAAoJ8ZgAwAAAAAQAwRsAAAAAADiJWDPmTPHhg4dukfLrFq1ylq1ahXT7Vi0aJH16tUrpusEAAAAAFQNlSJgAwAAAABQZQJ2YWGhjRs3zjp37mzdu3e3sWPHWl5enqv4zpw504455hhr3769Pfzww/b000/bUUcdZV27drW33noruo5HHnnEPU7rGDlypG3YsOFPz/PLL7+4ZefPn++ur1y50oYPH24dO3a0k046yT766KMSt+/ll1+2448/3j1Oj//999+j982YMcP69etnbdu2dds7b9686H0PPPCAHXnkkdatWzdbsGDBDut87bXXrG/fvtapUye7/PLLLTMz090+ffp0u/TSS6137952yimnlHYXAgAAAADiWKlnEX/nnXds2bJl9sEHH1hBQYGdc8450aD64Ycf2uuvv27ffvutDRs2zAYOHOge99JLL9kdd9zhQurs2bPt+eeft8cff9waN25s48ePt1GjRtmsWbOiz7F69Wq74IILXHg/7rjj3PNcdNFFLsQ++OCD9uWXX9qIESPslVde2WHbFi5c6J5H4f6ggw5yP7Xc3Llz7euvv3aB/7nnnrN99tnHbceECRPshBNOcCH+mWeecffVqlXLLROh59LjHn30UWvZsqVNnjzZxowZ4w4SyOLFi12or1u3brnegFAopH/KtQ5gZwKhkIXDYcvJyXE/Y0kH3XJzcy0hIcFdAL+hDcPvaMPwO9ow/CQ1NTW2ATstLc1+/fVXF1p79uxpL774ogWDQZs2bZqdfvrpVqNGDVfpVWDUeOqkpCRXwb755puj1WBVlvfbbz93XSG6Q4cOtnbtWnd98+bNLpyffPLJrlIt3333nW3dutUuvPBCdz1SaVaYV0CO0Lr//ve/W+vWrd11VZcVmhX4dVskXK9bt86Sk5MtIyMjetBg0KBB1qJFC3ddAVvBX1599VW3zkMOOcRdv+qqq1yFPlJ1P+KII6x58+ZWHjqAkJeXa4U528u1HmBnEnJzLZifb8tXr3FhGAAAAMCeUxaMacBWWB49erQ9++yzLjSru/Xtt9/u7qtdu7b7Gali1axZ0/0MBALRqtnGjRutadOmOxwBUPU3ErB//vln69Kli7377ruuO3ZiYqKlp6e7QKsgXvRIV506dXbYtk2bNu3wGAX/Jk2auEB92GGH2dSpU+399993t6kaHaEu30WXa9asWfR3PbdCtqrf0Z1VrZqtWbPG/d6gQQMrL60vKSnZLKV6udcFlCSQnOw+S2r3e6OCvW3bNvdZpoINP6INw+9ow/A72jDiUakDtsZCt2vXzgYPHuxCryq9kyZNigbp3VG38Eg4lezsbBeM69evbytWrHCBXd2x1b1cPy+++GJr2LChq3gXHTOt4Ktq+ZYtW6K3NWrUaId1q4qu6/Xq1XPjwxXi1WU9JSXFfvjhB1cBj4TkosutX78++rvuU9BXl/WI5cuXu2q3uqSX5jWXhg4GmC7AXhAIBl1bVdvfG/8p6kLAhl/RhuF3tGH4HW0Y8ajUye6zzz5zY6ZV9VXFWl2tI5Xr0hgwYIALzr/99ptt377dVb/V/TrSzVpVNlXBbrjhBjfeWoFe3bDz8/NdF299ABVw1aX7k08+2WHd6lKux6hLuSZeu//++926VMZXENe26rqq6Pfcc49bRus98cQTXVd3Taymx2nsdtHt1fhsPacC+xNPPGFnnXWWWw4AAAAAgDIH7NNOO811p1Yo1SzgGjOtwF1aqkxrHRpnrVnCFdTvu+++Pz1OoVizgd90001uHLfCtmYi1/jr8847z02uphnBi1LX8muuucauvvpqt206n7XCvJY/99xz3Thu3a5tOPzww92BAQXnHj16uPHdWqcmYlOgL7rOyy67zI3n1utW1fuhhx7aK5VAAAAAAID/BcKxHpiJUivYmGn50+40W5/OXsNeEWjUxJJGjrFgnfLNdl8S9SrJyspyEyAyBht+RBuG39GG4Xe0YcQjBv8CAAAAABADBGwAAAAAACpyFnHsHYEGDc1iMyE58Of2Vb8hewUAAACoIARsLyUlWdKQYZ5uAqrGubABAAAA7H0EbA9lF4asZlotJogCAAAAgDjAGGwPMYE7AAAAAMQPAjYAAAAAADFAwPZQIMDsZgAAAAAQLxiD7aEaCUELZG2xkJcbgbif4CyQkur1ZgAAAABVAgHbS3l5ljd7pllmhqebgfg9RVfikGEEbAAAAKCCELA9Ft6QYbY+3evNAAAAAACUE2OwAQAAAACIAQI2AAAAAAAxQMAGAAAAACAG4i5gL1q0yHr16hW9vmrVKmvVqpW7/e9//7tdf/311q5dOzv++OPtgw8+cI+ZM2eOnX322XbSSSdZjx49bOHChSWuQ4YOHWpPPvlk9L7Fixdbv379KvQ1AgAAAAAqn7gL2LvyzTff2EEHHeTC9sknn2wTJ06M3rdkyRIbP368vfHGG1a9evWdrkNh+p133olef/PNN+2EE07Y69sOAAAAAKjcqtQs4qmpqTZkyBD3+4knnmjTpk2L3te8eXNr3br1btfRt29fmzBhgmVmZlrdunVt/vz59sQTT5R5m0KhkP4p8/LAzgRCIQuHw5aTk+N+xlphYaHl5uZaQkKCuwB+QxuG39GG4Xe0YfgtS5ZGlQrYCsQRCgRFQ0fDhg1LtY569epZp06d7N1337X99tvPXW/ZsmWZtqegoMDy8nKtMGd7mZYHdiUhN9eC+fm2fPUaF4QBAAAAlE379u2rZsAOBoMuuEZs3ry5VMsFAoFSr0NdwtVNvEWLFq4SXlbVqlWzpKRks5Sdd0kHyiqQnGyJiYnuANDeqmBv27bNHc2jgg0/og3D72jD8DvaMOJR3AVsdfXesGGDG1N9+OGH26xZs2K+juOOO87uuOMO++233+zRRx8t1/YqzJsuQIwFgkF34CglJWWv/aeoCwEbfkUbht/RhuF3tGHEo7hLdo0bN7aRI0falVdeab1797a2bdvGfB21a9d2XQRq1arlwjgAAAAAAIHw3ug7WgXcdNNNrov4eeedV+Z1FGzMtPxpd5qtT4/ptgESaNTEkkaOsWCd/809EOujzllZWZaWlkYXcfgSbRh+RxuG39GGEY/irov43qbZw3/99Vc3e/jLL7/s9eYAAAAAACqJuOsivrd99tlndsEFF9jw4cNtn3328XpzAAAAAACVBBXsPdS/f393AQAAAACgKAK2xwINGpr97wxhQOzaVv3SndsdAAAAQGwQsL2UlGRJQ4Z5ugmI/3NhAwAAAKgYBGwPZReGrGZaLWZgBgAAAIA4wCRnHuIMaQAAAAAQPwjYHgoEGHwNAAAAAPGCLuIeqpEQtEDWFgt5uRGIi3HWgZRUrzcDAAAAqPII2F7Ky7O82TPNMjOqfENE2WcKTxwyjIANAAAAVAIEbI+FN2SYrU/3ejMAAAAAAOXEGGwAAAAAAGKAgF3OWcDXrFkTi/cBAAAAAOBzVTJgL1q0yHr16lXu9dxxxx320ksvxWSbAAAAAAD+ViUDdqxs3rzZ600AAAAAAFQScRGw33//fevTp4+1b9/epkyZYt27d7c5c+bsUKVetWqVtWrV6k/Lrlu3znr37m1PPvmku/7tt9/akCFD7Mgjj3TrGzt2rBUWFrr7tPyNN95oHTt2tHvvvddee+01e+ihh+yWW26pwFcLAAAAAKiMfD+LuALyqFGjXLA+6qij7LbbbrP169eXatk//vjDzj//fDv99NNt6NCh7rYrr7zSRowYYaeccoqtXLnSBg8ebJ9++ql169bN3Z+cnGyffPKJC90K7c2aNbMrrrhir75GAAAAAEDl5/uAvXDhQjv00EPtmGOOcdevueYae+GFF3a7XF5enl188cWuKn3hhRdGb585c6btu+++lpWVZZmZmVa7dm3bsGFD9P5+/fpZUlJSzLY/FArpn5itD1VLIBRyk+3l5OS4nxVJB5lyc3MtISHBXQC/oQ3D72jD8DvaMPwkNTW1agRsjYNu1KhR9HqtWrWsTp06u10uIyPDDjjgAFedViVbQVq++uorGzZsmPvAK7jn5+fvEFwaNGgQs20vKCiwvLxcK8zZHrN1ompJyM21YH6+LV+9xoVdAAAAALGn4cNVImA3bdrUFixYEL2ukLFlyxZr3ry5C7A7m5BMoVzValWx77rrLrv11ltt7dq1dsMNN9iLL75oBx54oHvcySefvMNygUAgZtterVo1S0pKNkupHrN1omoJJCdbYmKitWzZ0pMK9rZt29zRPCrY8CPaMPyONgy/ow0jHvk+YPfs2dPGjx9vb731lpusbPr06e7DqiCsrt1Lliyxww8/3GbNmvWncCv/+te/rH///nbqqae66reWq169uluHJkr76aefdgjqRamr+NatW8u1/cFgUP+Uax2ougLBoGuzKSkpFf7c+ozoQsCGX9GG4Xe0YfgdbRjxyPfJrmbNmm4mb126du0anfG7cePGNnLkSDdpmYJ327ZtS1xele7hw4e72cFbtGhh5557rg0aNMhNmPbee+9Z3759bfny5SUuq5nLdR7sa6+9dq++RgAAAABA5RcIV3S/0gqgicsUjv/yl79YZVawMdPyp91ptj7d602BTwUaNbGkkWMsWKduhT+3DmZpMsC0tDS6iMOXaMPwO9ow/I42jHjk+wo2AAAAAACVAQEbAAAAAIAY8P0kZyXRxGQAAAAAAFSkuAzYfhJo0NAsdmf+QhUTqN/Q600AAAAA8H8I2F5KSrKkIcM83QTEx7mwAQAAAHiPgO2h7MKQ1UyrxQzMAAAAABAHmOTMQ3F4hjQAAAAAqLII2AAAAAAAxAABGwAAAACAGCBgAwAAAAAQAwRsAAAAAABigIANAAAAAEAMELABAAAAAIgBAjYAAAAAADFAwAYAAAAAIAYI2AAAAAAAELABAAAAAKgcqGADAAAAABADBGwAAAAAAGKAgA0AAAAAQAwQsAEAAAAAiAECNgAAAAAAMUDABgAAAAAgBgjYAAAAAADEAAEbAAAAAIAYqBaLlWDP/fTTT7Z9+3YLBjnGAf8KhUK0YfgabRh+RxuG39GG4RdJSUnWqlWr3T6OdOchwjX8jjYMv6MNw+9ow/A72jDiTSAcDoe93ggAAAAAAPyOCjYAAAAAADFAwAYAAAAAIAYI2AAAAAAAxAABGwAAAACAGCBgAwAAAAAQAwRsAAAAAABigIANAAAAAEAMELABAAAAAIgBAjYAAAAAADFAwAYAAAAAIAYI2AAAAAAAxAABu4KFQiGbNm2adevWzdq0aWMXXHCBrVy5sqI3AyjRQw89ZEOHDt3hth9++MGGDBni2muvXr1s1qxZe9ymd7cOoDw2b95sN9xwg3Xv3t3atWtnZ555pi1ZsiR6/2effWYDBw60I444wvr27WtvvPHGDsvn5ubazTffbF26dLG2bdva6NGjbePGjTs8ZnfrAMojMzPTrrnmGjvyyCNdG7zwwgtt+fLl0fv5Owy/+PXXX10bnjNnTvQ22i+qGgJ2Bbv//vtt9uzZduutt9qzzz7rwsn5559veXl5Fb0pwA6efvppmzp16g63bdq0yc477zzbd9997aWXXrLLLrvM7rrrLvd7adt0adYBlMeoUaPs66+/trvvvtu1q4MPPtiGDx9uK1ascCHloosucgeA9IVv8ODBdu2117rAHHHTTTfZxx9/bNOnT7cnnnjCLTdixIjo/aVZB1Ae+rv4+++/28MPP2wvvviiVa9e3c4991zLycnh7zB8Iz8/366++mrbtm1b9Da+R6BKCqPC5Obmhtu2bRt++umno7f98ccf4datW4dfe+013gl4Yu3ateGLLroo3KZNm3Dfvn3DQ4YMid734IMPhrt27RrOz8+P3jZ58uRwnz59St2md7cOoDx+++238IEHHhhesmRJ9LZQKBQ+9thjw1OnTg2PGzcuPGjQoB2WGTVqVHjYsGHR9n/QQQeFP/jgg+j9K1ascOv86quv3PXdrQMoj82bN7v29NNPP0Vv++GHH1wb/Oabb/g7DN/Q/+1nn322a7svvfSSu43vEaiKqGBXoB9//NGys7NdN8SIWrVq2SGHHGJffPFFRW4KELV06VJLTEy0uXPnuu6vRambbadOnaxatWrR29SF8bfffrMNGzaUqk3vbh1AedStW9dV/Q4//PDobYFAwF22bNni2l/R9hlpf19++aUOMLufkdsi9t9/f2vUqNEObXhX6wDKo3bt2jZ58mQ78MAD3XUNT5g5c6Y1btzYDjjgAP4Owxf09/K5556z22+/fYfb+R6BqoiAXYHWrl3rfjZp0mSH2/fZZ5/ofUBF05hodY1t3rz5n+5Tu9SXvOLtVdLT00vVpne3DqA8dECnR48elpSUFL3t7bffdt1t1aV7Z+0v0vV23bp1LqQnJyfvcRuOrAOIlXHjxrmDORrjP378eEtNTeXvMCo9HczUsJnrr7/+T98H+B6BqoiAXYH0ZUyKfhEUfbHTJDtAZbN9+/YS26uozZamTe9uHUAsffXVVzZ27Fjr06eP9ezZs8T2F7mueQLUhovfX5o2XHQdQKycc845bh6B/v37u3HZ6mHE32FUdprHQhObnXTSSX+6j/aLquh/fTax12nSksgXssjvoi9xKSkpvAOodNROiweISOhQZaU0bXp36wBi5d1333UT7GgmcU2kFwnKxdtf5LraaEnts3gb3t06gFhRl3BR9fqbb76xp556ir/DqNReeeUV1w38tddeK/F+vkegKqKCXYEi3WbWr1+/w+26rvF+QGWjbrEltVdRmy1Nm97dOoBYUBC54oor7JhjjrEHH3ww2ktCbbSk9qeDO2lpaa596jRfxQN00Ta8u3UA5aEx1+oSXlBQEL0tGAy6sK12xt9hVGbqcaHTzKnHkKrYusiNN97ozihC+0VVRMCuQAcddJDVrFnTFi1atMO4lX//+9/WsWPHitwUoFTULjWRU2FhYfS2zz//3E0CVb9+/VK16d2tAyivyGnizjrrLHeqrqLduTt06GCLFy/e4fFqf6pyK8S0b9/enVouMtlZ5DyuGpsdacO7WwdQHprsUaeaK3raN53uSH9HW7Zsyd9hVGrqLTRv3jxXyY5cRKc6VE8MvkegSvJ6GvOq5u677w536tQp/O6777rTcOg0LzpdUV5entebBoTHjBmzw2m6NmzYEO7YsaO7/ZdffnGn3Tj88MPDc+bMKXWbLs06gLLSKbUOPfTQ8GWXXRZev379DpctW7aEf/75Z3f/nXfeGV62bFn40UcfDR9yyCHhTz/9NLoOnSKpV69e4c8//9ydFumUU07Z4XNQmnUA5XH++ee7v5uLFy92p+tSm9TfzdWrV/N3GL5T9DRdfI9AVRTQP16H/KpEVTxVWObMmeMmftCRvRtuuMH+8pe/eL1pgF133XW2evVqe/LJJ6N749tvv3VHoVVNadiwoQ0bNsyGDBmyR216d+sAykrdwadMmVLifaeeeqo7ZcyHH35od955pzs1nNqlupKfcMIJ0cdt27bNJkyY4GYfl+7du7vZcDW7eMTu1gGUR1ZWljtVl+YR0O/qNaG/x3/729/c/fwdhp+0atXKJk6caAMHDnTXab+oagjYAAAAAADEAIPHAAAAAACIAQI2AAAAAAAxQMAGAAAAACAGCNgAAAAAAMQAARsAAAAAgBggYAMAAAAAEAMEbAAAAAAAYoCADQAAAABADBCwAQDYQ0OHDrVDDjnEvvvuuxLv79Wrl1133XUVsl/1PHq+yqagoMBtW9u2ba1du3b2+eefm1/fa10AACgNAjYAAGVQWFhoY8eOtby8PPZfCT766CN7+eWX7dxzz7WHHnrIDj/8cPYTACDuEbABACiDtLQ0++WXX+y+++5j/5Vg8+b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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Total unique families: 580\n", "Top 5 families:\n", " sfone: 4,729 samples (8.3%)\n", " wacatac: 4,694 samples (8.2%)\n", " upatre: 3,901 samples (6.8%)\n", " wabot: 3,673 samples (6.4%)\n", " small: 3,339 samples (5.8%)\n", "\n", "Top 5 families cover 35.5% of malware samples\n", "Top 20 families cover 69.4% of malware samples\n" ] } ], "source": [ "# --- Top malware families ---\n", "if \"family\" in meta.columns:\n", " families = meta[meta[\"label_int\"] == 1][\"family\"].copy()\n", " families = families.replace([\"\", \"nan\", \"NaN\", \"None\"], pd.NA).dropna()\n", " \n", " n_unique = families.nunique()\n", " top_n = 20\n", " top_families = families.value_counts().head(top_n)\n", " \n", " fig, ax = plt.subplots(figsize=(10, 6))\n", " ax.barh(range(len(top_families)), top_families.values[::-1], color=\"#f44336\", alpha=0.8)\n", " ax.set_yticks(range(len(top_families)))\n", " ax.set_yticklabels(top_families.index[::-1], fontsize=9)\n", " ax.set_xlabel(\"Number of samples\")\n", " ax.set_title(f\"Top {top_n} malware families (out of {n_unique} unique families)\")\n", " ax.grid(axis=\"x\", alpha=0.3)\n", " sns.despine(left=True)\n", " plt.tight_layout()\n", " plt.show()\n", " \n", " print(f\"\\nTotal unique families: {n_unique}\")\n", " print(f\"Top 5 families:\")\n", " for fam, cnt in top_families.head(5).items():\n", " print(f\" {fam}: {cnt:,} samples ({100*cnt/len(families):.1f}%)\")\n", " \n", " # Family concentration\n", " top5_share = top_families.head(5).sum() / len(families)\n", " top20_share = top_families.sum() / len(families)\n", " print(f\"\\nTop 5 families cover {100*top5_share:.1f}% of malware samples\")\n", " print(f\"Top 20 families cover {100*top20_share:.1f}% of malware samples\")\n", "else:\n", " print(\"No family column found in metadata.\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Family-label consistency check:\n", " Consistent: 134,421 / 134,428 (99.99%)\n", " Inconsistent: 7 rows (likely borderline/ambiguous samples)\n" ] } ], "source": [ "# --- Family-label consistency check ---\n", "# In BODMAS: empty family = benign, non-empty family = malware\n", "if \"family\" in meta.columns:\n", " family_empty = meta[\"family\"].isna() | (meta[\"family\"] == \"\") | (meta[\"family\"] == \"nan\")\n", " benign_mask = (y == 0)\n", " \n", " consistent = (family_empty == benign_mask).sum()\n", " total_rows = len(meta)\n", " inconsistent = total_rows - consistent\n", " \n", " print(f\"Family-label consistency check:\")\n", " print(f\" Consistent: {consistent:,} / {total_rows:,} ({100*consistent/total_rows:.2f}%)\")\n", " if inconsistent > 0:\n", " print(f\" Inconsistent: {inconsistent} rows (likely borderline/ambiguous samples)\")\n", " else:\n", " print(f\" Perfect consistency: empty family == benign label for all rows\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Which features matter most?\n", "\n", "We have 2,326 features, but they do not all contribute\n", "equally to classification. We want to find the **Top-10most discriminative features**.\n", "\n", "We use a **Random Forest** with 100 trees. Each tree is a flowchart that asks\n", "questions like \"is feature X greater than 0.5?\" and splits samples into branches.\n", "To classify a new sample, all trees vote and the majority wins.\n", "\n", "**Why Random Forest?** It needs no normalisation, handles sparse features well,\n", "and provides a built-in measure of **feature importance** (Gini impurity reduction).\n", "\n", "### Experiment setup\n", "\n", "- **Balanced sample**: 50,000 benign + 50,000 malware (100k total)\n", "- **Split**: 80% training / 20% test (unseen data for evaluation)\n", "- **100 trees** with no depth limit\n", "\n", "**Note:** Because BODMAS is moderately imbalanced (57/43), we use *balanced\n", "sampling* — equal numbers from each class — to prevent the model from being\n", "biased towards the majority class." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training set: 80,000 samples\n", "Test set: 20,000 samples\n", " (balanced: 40,000 benign + 40,000 malware in train)\n", "\n", "Test accuracy: 99.36%\n", "(Out of 20,000 unseen samples, 19,872 classified correctly)\n", "CPU times: total: 4min 24s\n", "Wall time: 42.3 s\n" ] } ], "source": [ "%%time\n", "\n", "N_PER_CLASS = 50_000\n", "\n", "benign_idx = np.where(y == 0)[0]\n", "malware_idx = np.where(y == 1)[0]\n", "\n", "rng = np.random.default_rng(42)\n", "b_sample = rng.choice(benign_idx, size=min(N_PER_CLASS, len(benign_idx)), replace=False)\n", "m_sample = rng.choice(malware_idx, size=min(N_PER_CLASS, len(malware_idx)), replace=False)\n", "use_idx = np.concatenate([b_sample, m_sample])\n", "\n", "X_use = np.array(X[use_idx]) # read selected rows from memmap into RAM\n", "y_use = np.concatenate([np.zeros(len(b_sample), dtype=int),\n", " np.ones(len(m_sample), dtype=int)])\n", "\n", "# stratify keeps the same benign/malware ratio in both sets\n", "X_tr, X_te, y_tr, y_te = train_test_split(\n", " X_use, y_use, test_size=0.2, stratify=y_use, random_state=42\n", ")\n", "print(f\"Training set: {X_tr.shape[0]:,} samples\")\n", "print(f\"Test set: {X_te.shape[0]:,} samples\")\n", "print(f\" (balanced: {(y_tr==0).sum():,} benign + {(y_tr==1).sum():,} malware in train)\")\n", "\n", "rf = RandomForestClassifier(\n", " n_estimators=100,\n", " max_depth=None,\n", " n_jobs=-1,\n", " random_state=42,\n", ")\n", "rf.fit(X_tr, y_tr)\n", "\n", "acc = rf.score(X_te, y_te)\n", "n_test = X_te.shape[0]\n", "print(f\"\\nTest accuracy: {acc:.2%}\")\n", "print(f\"(Out of {n_test:,} unseen samples, {int(acc*n_test):,} classified correctly)\")" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "importances = rf.feature_importances_\n", "top_k = 10\n", "top_idx = np.argsort(importances)[::-1][:top_k]\n", "\n", "fig, ax = plt.subplots(figsize=(10, 4))\n", "ax.barh(range(top_k), importances[top_idx][::-1], color=\"darkorange\")\n", "ax.set_yticks(range(top_k))\n", "ax.set_yticklabels([f\"Feature {i}\" for i in top_idx[::-1]], fontsize=9)\n", "ax.set_xlabel(\"Gini importance (higher = more useful for classification)\", fontsize=\"10\")\n", "ax.set_title(f\"Top {top_k} most important features — model accuracy: {acc:.1%}\")\n", "plt.tight_layout()\n", "\n", "for rank, (bar, idx) in enumerate(zip(ax.patches, top_idx[::-1])):\n", " ax.text(bar.get_width() + 0.0005, bar.get_y() + bar.get_height()/2,\n", " f\"{importances[idx]:.4f}\", va=\"center\", fontsize=8)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### How to read the feature importance chart\n", "\n", "Each bar represents a feature. The longer the bar, the more useful that feature\n", "was for separating benign from malware.\n", "\n", "BODMAS uses LIEF-based feature schema. Feature indices map\n", "to specific groups:\n", "\n", "| Feature indices | What they represent | Why they matter |\n", "|-----------------|---------------------|------------------|\n", "| **~2,077-2,325** | **Byte-entropy histogram** bins. Entropy measures how \"random\" the data looks. | Packed or encrypted malware has high, uniform entropy — very different from legitimate software. |\n", "| **~497-511** | **Windows API import flags**: whether the file imports specific OS functions. | Functions like `VirtualAlloc`, `CreateRemoteThread` are used for code injection — common in malware. |\n", "| **~0-60** | **PE header fields**: file size, number of sections, subsystem, compilation timestamp. | Anomalous values indicate hand-crafted or obfuscated executables — typical of malware. |\n", "\n", "The model does not \"know\" what these features mean — it only sees numbers. But\n", "the fact that the most important features correspond to known malware indicators\n", "confirms the model is learning real patterns." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## How good is the model? (and where does it fail)\n", "\n", "Saying \"the model is 98% accurate\" is a good start, but in cybersecurity,\n", "the two types of error have very different costs:\n", "\n", "- **False positive** (benign software flagged as malware): the SOC analyst wastes\n", " time investigating a false alarm. Annoying, but not dangerous.\n", "- **False negative** (malware classified as benign): the malware slips through\n", " and can infect the network. Potentially catastrophic.\n", "\n", "We use three complementary tools:\n", "\n", "### 1. Classification Report — Precision and Recall\n", "\n", "- **Precision** (malware class): of all samples flagged as malware, how many actually are?\n", "- **Recall** (malware class): of all real malware, how many did the model catch?\n", "- **F1-score**: harmonic mean of precision and recall.\n", "\n", "### 2. Confusion matrix — the 4 possible outcomes\n", "\n", "| | Predicted Benign | Predicted Malware |\n", "|--|------------------|-------------------|\n", "| **Actually Benign** | True Negative | False Positive (false alarm) |\n", "| **Actually Malware** | False Negative (missed!) | True Positive |\n", "\n", "### 3. ROC curve and AUC\n", "\n", "The model outputs a **probability** of being malware. The ROC curve shows\n", "the trade-off between catching malware (True Positive Rate) and generating\n", "false alarms (False Positive Rate) as we move the decision threshold.\n", "\n", "- **AUC = 1.0** = perfect model\n", "- **AUC = 0.5** = random classifier\n", "- **AUC > 0.95** = excellent model" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=== Classification Report ===\n", "\n", " precision recall f1-score support\n", "\n", " Benign 0.99 1.00 0.99 10000\n", " Malware 1.00 0.99 0.99 10000\n", "\n", " accuracy 0.99 20000\n", " macro avg 0.99 0.99 0.99 20000\n", "weighted avg 0.99 0.99 0.99 20000\n", "\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "AUC = 0.9996\n" ] } ], "source": [ "y_pred = rf.predict(X_te) # hard predictions: 0 or 1\n", "y_prob = rf.predict_proba(X_te)[:, 1] # probability of being malware (0.0-1.0)\n", "\n", "print(\"=== Classification Report ===\\n\")\n", "print(classification_report(y_te, y_pred, target_names=[\"Benign\", \"Malware\"]))\n", "\n", "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", "\n", "# Left: confusion matrix\n", "cm = confusion_matrix(y_te, y_pred)\n", "disp = ConfusionMatrixDisplay(cm, display_labels=[\"Benign\", \"Malware\"])\n", "disp.plot(ax=axes[0], cmap=\"Blues\", values_format=\",\")\n", "axes[0].set_title(\"Confusion Matrix\")\n", "axes[0].set_xlabel(\"Model prediction\")\n", "axes[0].set_ylabel(\"True label\")\n", "\n", "# Right: ROC curve\n", "fpr, tpr, _ = roc_curve(y_te, y_prob)\n", "roc_auc = roc_auc_score(y_te, y_prob)\n", "axes[1].plot(fpr, tpr, lw=2, color=\"steelblue\",\n", " label=f\"Random Forest (AUC = {roc_auc:.4f})\")\n", "axes[1].plot([0, 1], [0, 1], \"k--\", lw=1, label=\"Random classifier (AUC = 0.5)\")\n", "axes[1].set_xlabel(\"False Positive Rate (false alarms)\")\n", "axes[1].set_ylabel(\"True Positive Rate (malware caught)\")\n", "axes[1].set_title(\"ROC Curve\")\n", "axes[1].legend(loc=\"lower right\")\n", "axes[1].fill_between(fpr, tpr, alpha=0.1, color=\"steelblue\")\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "print(f\"AUC = {roc_auc:.4f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Operational implications — how to use this in practice\n", "\n", "The classification threshold (default 0.5) can be tuned depending on context:\n", "\n", "| Threshold | What changes | When to use it |\n", "|-----------|--------------|----------------|\n", "| **Low** (e.g. 0.2) | Catches nearly all malware, but generates more false alarms | Critical infrastructure (hospitals, defence) |\n", "| **Default** (0.5) | Balanced trade-off | General-purpose EDR products |\n", "| **High** (e.g. 0.9) | Almost zero false alarms, but lets more malware through | App stores, user-facing alerts |\n", "\n", "### BODMAS-specific operational note\n", "\n", "Because BODMAS includes **timestamps**, it enables a more realistic evaluation:\n", "train on samples from earlier months and test on later months. This simulates\n", "how the model would perform in production against *new, unseen malware variants*\n", "— a critical test for real-world deployment where malware evolves continuously\n", "(concept drift).\n", "\n", "### What the AUC tells us\n", "\n", "An AUC near 0.99 means the model separates benign from malware excellently\n", "using **static features alone**. In a real deployment, these scores are combined\n", "with dynamic analysis (sandboxing) and threat-intelligence feeds." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.4" } }, "nbformat": 4, "nbformat_minor": 4 }