{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d26578b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1623118 entries, 0 to 1623117\n",
      "Data columns (total 14 columns):\n",
      " #   Column                      Non-Null Count    Dtype  \n",
      "---  ------                      --------------    -----  \n",
      " 0   IPV4_SRC_ADDR               1623118 non-null  object \n",
      " 1   L4_SRC_PORT                 1623118 non-null  int64  \n",
      " 2   IPV4_DST_ADDR               1623118 non-null  object \n",
      " 3   L4_DST_PORT                 1623118 non-null  int64  \n",
      " 4   PROTOCOL                    1623118 non-null  int64  \n",
      " 5   L7_PROTO                    1623118 non-null  float64\n",
      " 6   IN_BYTES                    1623118 non-null  int64  \n",
      " 7   OUT_BYTES                   1623118 non-null  int64  \n",
      " 8   IN_PKTS                     1623118 non-null  int64  \n",
      " 9   OUT_PKTS                    1623118 non-null  int64  \n",
      " 10  TCP_FLAGS                   1623118 non-null  int64  \n",
      " 11  FLOW_DURATION_MILLISECONDS  1623118 non-null  int64  \n",
      " 12  Label                       1623118 non-null  int64  \n",
      " 13  Attack                      1623118 non-null  object \n",
      "dtypes: float64(1), int64(10), object(3)\n",
      "memory usage: 173.4+ MB\n",
      "Basic Info:\n",
      " None\n",
      "First 5 rows:\n",
      "    IPV4_SRC_ADDR  L4_SRC_PORT  IPV4_DST_ADDR  L4_DST_PORT  PROTOCOL  L7_PROTO  \\\n",
      "0  149.171.126.0        62073     59.166.0.5        56082         6       0.0   \n",
      "1  149.171.126.2        32284     59.166.0.5         1526         6       0.0   \n",
      "2  149.171.126.0           21     59.166.0.1        21971         6       1.0   \n",
      "3     59.166.0.1        23800  149.171.126.0        46893         6       0.0   \n",
      "4     59.166.0.5        63062  149.171.126.2           21         6       1.0   \n",
      "\n",
      "   IN_BYTES  OUT_BYTES  IN_PKTS  OUT_PKTS  TCP_FLAGS  \\\n",
      "0      9672        416       11         8         25   \n",
      "1      1776        104        6         2         25   \n",
      "2      1842       1236       26        22         25   \n",
      "3       528       8824       10        12         27   \n",
      "4      1786       2340       32        34         25   \n",
      "\n",
      "   FLOW_DURATION_MILLISECONDS  Label  Attack  \n",
      "0                          15      0  Benign  \n",
      "1                           0      0  Benign  \n",
      "2                        1111      0  Benign  \n",
      "3                         124      0  Benign  \n",
      "4                        1459      0  Benign  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder, RobustScaler, MinMaxScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, f1_score, classification_report\n",
    "from sklearn.model_selection import ParameterGrid\n",
    "\n",
    "\n",
    "\n",
    "###################################\n",
    "#\n",
    "#\n",
    "#\n",
    "#     FILE PATH\n",
    "#\n",
    "#\n",
    "#\n",
    "####################################\n",
    "############################################################\n",
    "df = pd.read_csv(\"data/NF-UNSW-NB15.csv\")\n",
    "############################################################\n",
    "print(\"Basic Info:\\n\", df.info())\n",
    "print(\"First 5 rows:\\n\", df.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba4189da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IPV4_SRC_ADDR                 0\n",
      "L4_SRC_PORT                   0\n",
      "IPV4_DST_ADDR                 0\n",
      "L4_DST_PORT                   0\n",
      "PROTOCOL                      0\n",
      "L7_PROTO                      0\n",
      "IN_BYTES                      0\n",
      "OUT_BYTES                     0\n",
      "IN_PKTS                       0\n",
      "OUT_PKTS                      0\n",
      "TCP_FLAGS                     0\n",
      "FLOW_DURATION_MILLISECONDS    0\n",
      "Label                         0\n",
      "Attack                        0\n",
      "dtype: int64\n",
      "Attack\n",
      "Benign            1550712\n",
      "Exploits            24736\n",
      "Fuzzers             19463\n",
      "Reconnaissance      12291\n",
      "Generic              5570\n",
      "DoS                  5051\n",
      "Analysis             1995\n",
      "Backdoor             1782\n",
      "Shellcode            1365\n",
      "Worms                 153\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative values in IN_BYTES: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative values in OUT_BYTES: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative values in IN_PKTS: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative values in OUT_PKTS: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative values in FLOW_DURATION_MILLISECONDS: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            L4_SRC_PORT  L4_DST_PORT  PROTOCOL  L7_PROTO  \\\n",
      "L4_SRC_PORT                    1.000000     0.030727 -0.116602 -0.008694   \n",
      "L4_DST_PORT                    0.030727     1.000000 -0.156159  0.133112   \n",
      "PROTOCOL                      -0.116602    -0.156159  1.000000 -0.112017   \n",
      "L7_PROTO                      -0.008694     0.133112 -0.112017  1.000000   \n",
      "IN_BYTES                      -0.001544    -0.017243 -0.019492  0.002343   \n",
      "OUT_BYTES                      0.000475    -0.039697 -0.075392  0.075795   \n",
      "IN_PKTS                        0.000056     0.003157 -0.126623  0.167771   \n",
      "OUT_PKTS                       0.001323    -0.034713 -0.115350  0.136146   \n",
      "TCP_FLAGS                      0.020061     0.295358 -0.490953  0.241509   \n",
      "FLOW_DURATION_MILLISECONDS    -0.002213    -0.045799 -0.031195 -0.032112   \n",
      "Label                         -0.048061    -0.129065  0.284834  0.017352   \n",
      "\n",
      "                            IN_BYTES  OUT_BYTES   IN_PKTS  OUT_PKTS  \\\n",
      "L4_SRC_PORT                -0.001544   0.000475  0.000056  0.001323   \n",
      "L4_DST_PORT                -0.017243  -0.039697  0.003157 -0.034713   \n",
      "PROTOCOL                   -0.019492  -0.075392 -0.126623 -0.115350   \n",
      "L7_PROTO                    0.002343   0.075795  0.167771  0.136146   \n",
      "IN_BYTES                    1.000000   0.033575  0.692714  0.107772   \n",
      "OUT_BYTES                   0.033575   1.000000  0.650213  0.972302   \n",
      "IN_PKTS                     0.692714   0.650213  1.000000  0.750462   \n",
      "OUT_PKTS                    0.107772   0.972302  0.750462  1.000000   \n",
      "TCP_FLAGS                   0.037695   0.158826  0.266027  0.239804   \n",
      "FLOW_DURATION_MILLISECONDS  0.015489   0.236040  0.191523  0.247819   \n",
      "Label                       0.044234  -0.031368 -0.029655 -0.047894   \n",
      "\n",
      "                            TCP_FLAGS  FLOW_DURATION_MILLISECONDS     Label  \n",
      "L4_SRC_PORT                  0.020061                   -0.002213 -0.048061  \n",
      "L4_DST_PORT                  0.295358                   -0.045799 -0.129065  \n",
      "PROTOCOL                    -0.490953                   -0.031195  0.284834  \n",
      "L7_PROTO                     0.241509                   -0.032112  0.017352  \n",
      "IN_BYTES                     0.037695                    0.015489  0.044234  \n",
      "OUT_BYTES                    0.158826                    0.236040 -0.031368  \n",
      "IN_PKTS                      0.266027                    0.191523 -0.029655  \n",
      "OUT_PKTS                     0.239804                    0.247819 -0.047894  \n",
      "TCP_FLAGS                    1.000000                    0.070228 -0.079583  \n",
      "FLOW_DURATION_MILLISECONDS   0.070228                    1.000000 -0.023638  \n",
      "Label                       -0.079583                   -0.023638  1.000000  \n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(df.isnull().sum())\n",
    "print(df['Attack'].value_counts())\n",
    "sns.countplot(data=df, x='Attack', order=df['Attack'].value_counts().index)\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('Distribution of Attack Categories')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "non_negative_cols = [\n",
    "    'IN_BYTES', 'OUT_BYTES', 'IN_PKTS', 'OUT_PKTS',\n",
    "    'FLOW_DURATION_MILLISECONDS'\n",
    "]\n",
    "for col in non_negative_cols:\n",
    "    neg_count = (df[col] < 0).sum()\n",
    "    print(f\"Negative values in {col}: {neg_count}\")\n",
    "    sns.boxplot(x=df[col])\n",
    "    plt.title(f'Boxplot of {col}')\n",
    "    plt.show()\n",
    "\n",
    "df.describe()\n",
    "\n",
    "correlation_matrix = df.corr(numeric_only=True)\n",
    "print(correlation_matrix)\n",
    "plt.figure(figsize=(12, 10))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\", linewidths=0.5)\n",
    "plt.title('Correlation Heatmap')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e21b3329",
   "metadata": {},
   "outputs": [],
   "source": [
    "def common_prep(df):\n",
    "    \"\"\"\n",
    "    Drops universally useless or leak-prone columns,\n",
    "    e.g. OUT_BYTES (and any others you decide).\n",
    "    \"\"\"\n",
    "    df = df.copy()\n",
    "    df.drop(columns=['OUT_BYTES'], inplace=True, errors='ignore')\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2291c7dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_ip_freq_pct(df_tr, df_use):\n",
    "    \"\"\"\n",
    "    Compute raw-IP frequency percentage on df_tr,\n",
    "    then map it onto df_use, unseen→0.\n",
    "    \"\"\"\n",
    "    n = len(df_tr)\n",
    "    counts = df_tr['IPV4_SRC_ADDR'].value_counts().to_dict()\n",
    "    df_use['SRC_IP_FREQ_PCT'] = (\n",
    "        df_use['IPV4_SRC_ADDR']\n",
    "           .map(counts).fillna(0)\n",
    "           .div(n)\n",
    "    )\n",
    "    counts = df_tr['IPV4_DST_ADDR'].value_counts().to_dict()\n",
    "    df_use['DST_IP_FREQ_PCT'] = (\n",
    "        df_use['IPV4_DST_ADDR']\n",
    "           .map(counts).fillna(0)\n",
    "           .div(n)\n",
    "    )\n",
    "    return df_use\n",
    "def add_subnet_freq_pct(df_tr, df_use):\n",
    "    \"\"\"\n",
    "    Compute /24-subnet frequency percentage on df_tr,\n",
    "    then map it onto df_use, unseen→0.\n",
    "    \"\"\"\n",
    "    n = len(df_tr)\n",
    "\n",
    "    tr_src_sub = df_tr['IPV4_SRC_ADDR'].str.rsplit('.', n=1).str[0]\n",
    "    tr_dst_sub = df_tr['IPV4_DST_ADDR'].str.rsplit('.', n=1).str[0]\n",
    "\n",
    "    src_counts = tr_src_sub.value_counts().to_dict()\n",
    "    dst_counts = tr_dst_sub.value_counts().to_dict()\n",
    "\n",
    "    df_use['SRC_SUBNET_FREQ_PCT'] = (\n",
    "        df_use['IPV4_SRC_ADDR']\n",
    "           .str.rsplit('.', n=1).str[0]\n",
    "           .map(src_counts).fillna(0)\n",
    "           .div(n)\n",
    "    )\n",
    "    df_use['DST_SUBNET_FREQ_PCT'] = (\n",
    "        df_use['IPV4_DST_ADDR']\n",
    "           .str.rsplit('.', n=1).str[0]\n",
    "           .map(dst_counts).fillna(0)\n",
    "           .div(n)\n",
    "    )\n",
    "    return df_use\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19f6eca8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_raw_all(df):\n",
    "    df1 = common_prep(df)\n",
    "    df1.drop(columns=['IPV4_SRC_ADDR','IPV4_DST_ADDR'], inplace=True)\n",
    "    return df1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ea79c5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "def make_ip_octets_and_freqs(df):\n",
    "    df1 = common_prep(df)\n",
    "\n",
    "    for ip in ['IPV4_SRC_ADDR','IPV4_DST_ADDR']:\n",
    "        for i in range(4):\n",
    "            df1[f\"{ip}_octet{i+1}\"] = (\n",
    "                df1[ip].str.split('.', expand=True)[i].astype(int)\n",
    "            )\n",
    "    df1 = add_ip_freq_pct(df, df1)\n",
    "    df1.drop(columns=['IPV4_SRC_ADDR','IPV4_DST_ADDR'], inplace=True)\n",
    "    return df1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f19c88c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_subnet_freqs_only(df):\n",
    "    df1 = common_prep(df)\n",
    "    df1 = add_subnet_freq_pct(df, df1)\n",
    "    df1.drop(columns=['IPV4_SRC_ADDR','IPV4_DST_ADDR'], inplace=True)\n",
    "    return df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "03f24be8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_no_ip_raw_numeric(df):\n",
    "    df1 = common_prep(df)\n",
    "    df1.drop(columns=['IPV4_SRC_ADDR','IPV4_DST_ADDR'], inplace=True)\n",
    "    return df1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f247fa7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_transformed_numeric(df):\n",
    "    df1 = common_prep(df)\n",
    "    nums = ['IN_BYTES','IN_PKTS','OUT_PKTS','FLOW_DURATION_MILLISECONDS']\n",
    "    for c in nums:\n",
    "        df1[c+'_log'] = np.log1p(df1[c])\n",
    "    from sklearn.preprocessing import RobustScaler\n",
    "    rsc = RobustScaler().fit(df1[nums + [c+'_log' for c in nums]])\n",
    "    df1[nums + [c+'_log' for c in nums]] = rsc.transform(df1[nums + [c+'_log' for c in nums]])\n",
    "    df1.drop(columns=nums, inplace=True)\n",
    "    df1.drop(columns=['IPV4_SRC_ADDR','IPV4_DST_ADDR'], inplace=True)\n",
    "    return df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "38cb310a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_balanced(df):\n",
    "    benign = df[df['Label']==0]\n",
    "    attack = df[df['Label']==1]\n",
    "    benign_down = benign.sample(n=len(attack), random_state=42)\n",
    "    return pd.concat([benign_down, attack]).sample(frac=1, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4a50c97",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp, df_test = train_test_split(\n",
    "    df,\n",
    "    test_size=0.25,\n",
    "    random_state=42,\n",
    "    stratify=df['Label']\n",
    ")\n",
    "df_train, df_val = train_test_split(\n",
    "    df_temp,\n",
    "    test_size=1/3,\n",
    "    random_state=42,\n",
    "    stratify=df_temp['Label']\n",
    ")\n",
    "\n",
    "builders = {\n",
    "    'raw_all_features':        make_raw_all,\n",
    "    'ip_octets_and_freqs':     make_ip_octets_and_freqs,\n",
    "    'subnet_freqs_only':       make_subnet_freqs_only,\n",
    "    'no_ip_raw_numeric':       make_no_ip_raw_numeric,\n",
    "}\n",
    "\n",
    "data_versions_train = {\n",
    "    name: fn(df_train) for name, fn in builders.items()\n",
    "}\n",
    "data_versions_val = {\n",
    "    name: fn(df_val) for name, fn in builders.items()\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc1e2882",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ac631dd6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Builders: ['raw_all_features', 'ip_octets_and_freqs', 'subnet_freqs_only', 'no_ip_raw_numeric']\n"
     ]
    }
   ],
   "source": [
    "print(\"Builders:\", list(builders.keys()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e04aaaad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- raw_all_features ---\n",
      "Accuracy : 0.9889\n",
      "Macro F1 : 0.9349\n",
      "ROC AUC  : 0.9958\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.9940    0.9944    0.9942    387679\n",
      "           1     0.8791    0.8723    0.8757     18101\n",
      "\n",
      "    accuracy                         0.9889    405780\n",
      "   macro avg     0.9366    0.9333    0.9349    405780\n",
      "weighted avg     0.9889    0.9889    0.9889    405780\n",
      "\n",
      "--- ip_octets_and_freqs ---\n",
      "Accuracy : 0.9913\n",
      "Macro F1 : 0.9489\n",
      "ROC AUC  : 0.9968\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.9954    0.9955    0.9954    387679\n",
      "           1     0.9031    0.9016    0.9023     18101\n",
      "\n",
      "    accuracy                         0.9913    405780\n",
      "   macro avg     0.9493    0.9485    0.9489    405780\n",
      "weighted avg     0.9913    0.9913    0.9913    405780\n",
      "\n",
      "--- subnet_freqs_only ---\n",
      "Accuracy : 0.9891\n",
      "Macro F1 : 0.9357\n",
      "ROC AUC  : 0.9961\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.9941    0.9945    0.9943    387679\n",
      "           1     0.8818    0.8725    0.8771     18101\n",
      "\n",
      "    accuracy                         0.9891    405780\n",
      "   macro avg     0.9379    0.9335    0.9357    405780\n",
      "weighted avg     0.9890    0.9891    0.9891    405780\n",
      "\n",
      "--- no_ip_raw_numeric ---\n",
      "Accuracy : 0.9889\n",
      "Macro F1 : 0.9349\n",
      "ROC AUC  : 0.9958\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.9940    0.9944    0.9942    387679\n",
      "           1     0.8791    0.8723    0.8757     18101\n",
      "\n",
      "    accuracy                         0.9889    405780\n",
      "   macro avg     0.9366    0.9333    0.9349    405780\n",
      "weighted avg     0.9889    0.9889    0.9889    405780\n",
      "\n",
      "\n",
      "Summary table:\n",
      "                     accuracy  f1_macro   roc_auc\n",
      "version                                          \n",
      "raw_all_features     0.988950  0.934938  0.995813\n",
      "ip_octets_and_freqs  0.991293  0.948884  0.996792\n",
      "subnet_freqs_only    0.989095  0.935711  0.996110\n",
      "no_ip_raw_numeric    0.988950  0.934938  0.995813\n"
     ]
    }
   ],
   "source": [
    "X_train_versions = {}\n",
    "y_train_versions = {}\n",
    "X_val_versions   = {}\n",
    "y_val_versions   = {}\n",
    "\n",
    "for name, dft in data_versions_train.items():\n",
    "    X_train_versions[name] = dft.drop(columns=[\"Attack\", \"Label\"])\n",
    "    y_train_versions[name] = dft[\"Label\"]\n",
    "\n",
    "for name, dfv in data_versions_val.items():\n",
    "    X_val_versions[name] = dfv.drop(columns=[\"Attack\", \"Label\"])\n",
    "    y_val_versions[name] = dfv[\"Label\"]\n",
    "\n",
    "models = {}\n",
    "for version in X_train_versions:\n",
    "    rf = RandomForestClassifier(\n",
    "        random_state=42,\n",
    "        n_jobs=-1         \n",
    "    )\n",
    "    rf.fit(\n",
    "        X_train_versions[version],\n",
    "        y_train_versions[version]\n",
    "    )\n",
    "    models[version] = rf\n",
    "\n",
    "from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, classification_report\n",
    "import pandas as pd\n",
    "\n",
    "results = []\n",
    "\n",
    "for version, rf in models.items():\n",
    "    Xvl = X_val_versions[version]\n",
    "    yvl = y_val_versions[version]\n",
    "    \n",
    "    y_pred  = rf.predict(Xvl)\n",
    "    y_proba = rf.predict_proba(Xvl)[:, 1]\n",
    "    \n",
    "    acc  = accuracy_score(yvl, y_pred)\n",
    "    f1m  = f1_score(yvl, y_pred, average=\"macro\")\n",
    "    auc  = roc_auc_score(yvl, y_proba)\n",
    "    \n",
    "    print(f\"--- {version} ---\")\n",
    "    print(f\"Accuracy : {acc:.4f}\")\n",
    "    print(f\"Macro F1 : {f1m:.4f}\")\n",
    "    print(f\"ROC AUC  : {auc:.4f}\")\n",
    "    print(classification_report(yvl, y_pred, digits=4))\n",
    "    \n",
    "    results.append({\n",
    "        \"version\":  version,\n",
    "        \"accuracy\": acc,\n",
    "        \"f1_macro\": f1m,\n",
    "        \"roc_auc\":  auc\n",
    "    })\n",
    "\n",
    "df_results = pd.DataFrame(results).set_index(\"version\")\n",
    "print(\"\\nSummary table:\")\n",
    "print(df_results)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6eb76b48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "raw_all_features     Train AUC: 1.0000, Val AUC: 0.9958\n",
      "ip_octets_and_freqs  Train AUC: 1.0000, Val AUC: 0.9968\n",
      "subnet_freqs_only    Train AUC: 1.0000, Val AUC: 0.9961\n",
      "no_ip_raw_numeric    Train AUC: 1.0000, Val AUC: 0.9958\n",
      "transformed_numeric  Train AUC: 1.0000, Val AUC: 0.9925\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, roc_auc_score\n",
    "\n",
    "for version, rf in models.items():\n",
    "    Xtr, ytr = X_train_versions[version], y_train_versions[version]\n",
    "    Xvl, yvl = X_val_versions[version],   y_val_versions[version]\n",
    "\n",
    "    # Train‐set performance\n",
    "    ytr_pred  = rf.predict(Xtr)\n",
    "    ytr_proba = rf.predict_proba(Xtr)[:,1]\n",
    "    train_acc = accuracy_score(ytr, ytr_pred)\n",
    "    train_auc = roc_auc_score(ytr, ytr_proba)\n",
    "\n",
    "    # Validation‐set performance\n",
    "    yvl_pred  = rf.predict(Xvl)\n",
    "    yvl_proba = rf.predict_proba(Xvl)[:,1]\n",
    "    val_acc   = accuracy_score(yvl, yvl_pred)\n",
    "    val_auc   = roc_auc_score(yvl, yvl_proba)\n",
    "\n",
    "    print(f\"{version:20s} Train AUC: {train_auc:.4f}, Val AUC: {val_auc:.4f}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "06a0ba86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min_samples_leaf =    1 → ROC AUC = 0.9977\n",
      "min_samples_leaf =    2 → ROC AUC = 0.9977\n",
      "min_samples_leaf =    5 → ROC AUC = 0.9977\n",
      "min_samples_leaf =   10 → ROC AUC = 0.9977\n",
      "min_samples_leaf =   20 → ROC AUC = 0.9977\n",
      "min_samples_leaf =   50 → ROC AUC = 0.9977\n",
      "min_samples_leaf =  100 → ROC AUC = 0.9977\n",
      "min_samples_leaf =  200 → ROC AUC = 0.9976\n",
      "min_samples_leaf =  500 → ROC AUC = 0.9972\n",
      "min_samples_leaf = 1000 → ROC AUC = 0.9967\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "\n",
    "df_temp, df_test = train_test_split(\n",
    "    df, test_size=0.25, random_state=42, stratify=df['Label']\n",
    ")\n",
    "df_train, df_val = train_test_split(\n",
    "    df_temp, test_size=1/3, random_state=42, stratify=df_temp['Label']\n",
    ")\n",
    "\n",
    "df_train_subnet = make_subnet_freqs_only(df_train)\n",
    "df_val_subnet   = make_subnet_freqs_only(df_val)\n",
    "\n",
    "X_train = df_train_subnet.drop(columns=[\"Attack\", \"Label\"])\n",
    "y_train = df_train_subnet[\"Label\"]\n",
    "X_val   = df_val_subnet.drop(columns=[\"Attack\", \"Label\"])\n",
    "y_val   = df_val_subnet[\"Label\"]\n",
    "\n",
    "base_params = {\n",
    "    \"n_estimators\":   200,\n",
    "    \"max_depth\":      12,\n",
    "    \"max_leaf_nodes\": 50,\n",
    "    \"n_jobs\":        -1,\n",
    "    \"random_state\":  42,\n",
    "}\n",
    "\n",
    "leaf_counts = [1, 2, 5, 10, 20, 50, 100, 200, 500, 1000]\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "for leaf in leaf_counts:\n",
    "    params = {**base_params, \"min_samples_leaf\": leaf}\n",
    "    rf = RandomForestClassifier(**params)\n",
    "    rf.fit(X_train, y_train)\n",
    "\n",
    "    y_proba = rf.predict_proba(X_val)[:, 1]\n",
    "    fpr, tpr, _ = roc_curve(y_val, y_proba)\n",
    "    auc = roc_auc_score(y_val, y_proba)\n",
    "\n",
    "    plt.plot(fpr, tpr, label=f\"leaf={leaf}, AUC={auc:.3f}\")\n",
    "    print(f\"min_samples_leaf = {leaf:4d} → ROC AUC = {auc:.4f}\")\n",
    "\n",
    "plt.plot([0, 1], [0, 1], \"k--\", label=\"chance\")\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"ROC Curves for subnet_freqs_only (10 leaf counts)\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d51ddb89",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df_temp, df_test = train_test_split(\n",
    "    df, test_size=0.25, random_state=42, stratify=df['Label']\n",
    ")\n",
    "df_train, df_val = train_test_split(\n",
    "    df_temp, test_size=1/3, random_state=42, stratify=df_temp['Label']\n",
    ")\n",
    "\n",
    "df_tr = make_subnet_freqs_only(df_train)\n",
    "df_vl = make_subnet_freqs_only(df_val)\n",
    "X_tr = df_tr.drop(columns=[\"Attack\",\"Label\"])\n",
    "y_tr = df_tr[\"Label\"]\n",
    "X_vl = df_vl.drop(columns=[\"Attack\",\"Label\"])\n",
    "y_vl = df_vl[\"Label\"]\n",
    "\n",
    "depths = [4, 8, 12, 16, 20]\n",
    "leaf_nodes = [10, 25, 50, 100, 200]\n",
    "\n",
    "results = []\n",
    "for depth in depths:\n",
    "    for leaves in leaf_nodes:\n",
    "        rf = RandomForestClassifier(\n",
    "            n_estimators=200,\n",
    "            max_depth=depth,\n",
    "            max_leaf_nodes=leaves,\n",
    "            n_jobs=-1,\n",
    "            random_state=42\n",
    "        )\n",
    "        rf.fit(X_tr, y_tr)\n",
    "        y_proba = rf.predict_proba(X_vl)[:,1]\n",
    "        auc = roc_auc_score(y_vl, y_proba)\n",
    "        results.append({\n",
    "            \"max_depth\":      depth,\n",
    "            \"max_leaf_nodes\": leaves,\n",
    "            \"roc_auc\":        auc\n",
    "        })\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "763d59fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    max_depth  max_leaf_nodes   roc_auc\n",
      "24         20             200  0.997948\n",
      "19         16             200  0.997945\n",
      "14         12             200  0.997882\n",
      "23         20             100  0.997870\n",
      "18         16             100  0.997869\n",
      "13         12             100  0.997828\n",
      "17         16              50  0.997715\n",
      "22         20              50  0.997715\n",
      "12         12              50  0.997710\n",
      "9           8             200  0.997559\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_grid = pd.DataFrame(results)\n",
    "pivot = df_grid.pivot(\n",
    "    index=\"max_depth\",\n",
    "    columns=\"max_leaf_nodes\",\n",
    "    values=\"roc_auc\"\n",
    ")\n",
    "\n",
    "print(df_grid.sort_values(\"roc_auc\", ascending=False).head(10))\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(pivot, annot=True, fmt=\".4f\", cmap=\"viridis\")\n",
    "plt.title(\"Validation ROC AUC\\n(subnet_freqs_only)\")\n",
    "plt.ylabel(\"max_depth\")\n",
    "plt.xlabel(\"max_leaf_nodes\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "440d31dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.9889\n",
      "Test ROC AUC  : 0.9978\n",
      "\n",
      "Test Classification Report:\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.9941    0.9942    0.9942    387678\n",
      "           1     0.8760    0.8737    0.8749     18102\n",
      "\n",
      "    accuracy                         0.9889    405780\n",
      "   macro avg     0.9351    0.9340    0.9345    405780\n",
      "weighted avg     0.9888    0.9889    0.9888    405780\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_train_subnet = make_subnet_freqs_only(df_train)\n",
    "df_test_subnet  = make_subnet_freqs_only(df_test)\n",
    "\n",
    "X_train = df_train_subnet.drop(columns=[\"Attack\",\"Label\"])\n",
    "y_train = df_train_subnet[\"Label\"]\n",
    "\n",
    "X_test  = df_test_subnet.drop(columns=[\"Attack\",\"Label\"])\n",
    "y_test  = df_test_subnet[\"Label\"]\n",
    "\n",
    "best_params = {\n",
    "    \"n_estimators\":     200,\n",
    "    \"max_depth\":        12,\n",
    "    \"max_leaf_nodes\":   200,\n",
    "    \"min_samples_leaf\": 100,\n",
    "    \"n_jobs\":          -1,\n",
    "    \"random_state\":    42\n",
    "}\n",
    "rf_test = RandomForestClassifier(**best_params)\n",
    "\n",
    "rf_test.fit(X_train, y_train)\n",
    "\n",
    "y_pred  = rf_test.predict(X_test)\n",
    "y_proba = rf_test.predict_proba(X_test)[:, 1]\n",
    "\n",
    "test_acc = accuracy_score(y_test, y_pred)\n",
    "test_auc = roc_auc_score(y_test, y_proba)\n",
    "\n",
    "print(f\"Test Accuracy: {test_acc:.4f}\")\n",
    "print(f\"Test ROC AUC  : {test_auc:.4f}\\n\")\n",
    "print(\"Test Classification Report:\\n\")\n",
    "print(classification_report(y_test, y_pred, digits=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "242e99a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✔ Wrote features + labels to subnet_freqs_only_full.csv\n",
      "✔ Wrote predictions to subnet_freqs_only_predictions.csv\n"
     ]
    }
   ],
   "source": [
    "df_subnet_full = make_subnet_freqs_only(df)\n",
    "X_subnet_full  = df_subnet_full.drop(columns=[\"Attack\",\"Label\"])\n",
    "y_subnet_full  = df_subnet_full[\"Label\"]\n",
    "\n",
    "rf_final = RandomForestClassifier(\n",
    "    n_estimators=200,\n",
    "    max_depth=12,\n",
    "    max_leaf_nodes=200,\n",
    "    min_samples_leaf=100,\n",
    "    n_jobs=-1,\n",
    "    random_state=42\n",
    ")\n",
    "rf_final.fit(X_subnet_full, y_subnet_full)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "6d62e150",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training set classification report:\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.9938    0.9945    0.9942   1550712\n",
      "           1     0.8813    0.8670    0.8741     72406\n",
      "\n",
      "    accuracy                         0.9889   1623118\n",
      "   macro avg     0.9375    0.9308    0.9341   1623118\n",
      "weighted avg     0.9888    0.9889    0.9888   1623118\n",
      "\n"
     ]
    }
   ],
   "source": [
    "train_preds = rf_final.predict(X_subnet_full)\n",
    "train_acc = accuracy_score(y_subnet_full, train_preds)\n",
    "print(\"\\nTraining set classification report:\\n\")\n",
    "print(classification_report(y_subnet_full, train_preds, digits=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "f3c4b91a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✔ Wrote features + labels to subnet_freqs_only_full.csv\n",
      "✔ Wrote predictions to subnet_freqs_only_predictions.csv\n"
     ]
    }
   ],
   "source": [
    "df_subnet_full.to_csv(\"subnet_freqs_only_full.csv\", index=False)\n",
    "print(\"✔ Wrote features + labels to subnet_freqs_only_full.csv\")\n",
    "\n",
    "y_pred  = rf_final.predict(X_subnet_full)\n",
    "y_proba = rf_final.predict_proba(X_subnet_full)[:, 1]\n",
    "\n",
    "df_out = df_subnet_full.copy()\n",
    "df_out[\"pred_label\"] = y_pred\n",
    "df_out[\"pred_proba\"] = y_proba\n",
    "\n",
    "df_out.to_csv(\"subnet_freqs_only_predictions.csv\", index=False)\n",
    "print(\"✔ Wrote predictions to subnet_freqs_only_predictions.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "bdd75335",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open(\"rf_final_model.pkl\", \"wb\") as f_model:\n",
    "    pickle.dump(rf_final, f_model)\n",
    "\n",
    "with open(\"subnet_freqs_only_predictions.pkl\", \"wb\") as f_df:\n",
    "    pickle.dump(df_out, f_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "2bc9da43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded RF: RandomForestClassifier(max_depth=12, max_leaf_nodes=200, min_samples_leaf=100,\n",
      "                       n_estimators=200, n_jobs=-1, random_state=42)\n",
      "n_estimators: 200\n"
     ]
    }
   ],
   "source": [
    "with open(\"rf_final_model.pkl\", \"rb\") as f:\n",
    "    rf_loaded = pickle.load(f)\n",
    "\n",
    "print(\"Loaded RF:\", rf_loaded)\n",
    "print(\"n_estimators:\", rf_loaded.n_estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "895bfb29",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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