model release

.gitattributes

@@ -27,9 +27,11 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.tar.* filter=lfs diff=lfs merge=lfs -text
 *.tar filter=lfs diff=lfs merge=lfs -text
 *.tflite filter=lfs diff=lfs merge=lfs -text
+*.torch filter=lfs diff=lfs merge=lfs -text
 *.tgz filter=lfs diff=lfs merge=lfs -text
 *.wasm filter=lfs diff=lfs merge=lfs -text
 *.xz filter=lfs diff=lfs merge=lfs -text
+*.xgboost filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text

load_models.ipynb

@@ -0,0 +1,1049 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "ROOT = \"..\"\n",
+    "sys.path.append(ROOT)\n",
+    "from src.data_utils import load_data, commands_to_loader\n",
+    "\n",
+    "import json\n",
+    "import pickle\n",
+    "from time import time\n",
+    "from tqdm import tqdm\n",
+    "from typing import Union, List, Optional\n",
+    "\n",
+    "import math\n",
+    "import torch\n",
+    "from torch import nn\n",
+    "from torch.nn import functional as F\n",
+    "\n",
+    "import numpy as np\n",
+    "from xgboost import XGBClassifier\n",
+    "\n",
+    "from src.preprocessors import CommandTokenizer, OneHotCustomVectorizer\n",
+    "from nltk.tokenize import wordpunct_tokenize\n",
+    "TOKENIZER = wordpunct_tokenize\n",
+    "\n",
+    "from sklearn.metrics import roc_curve, accuracy_score, f1_score\n",
+    "from sklearn.utils import shuffle\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "SEED = 33\n",
+    "VOCAB_SIZE = 4096\n",
+    "EMBEDDED_DIM = 64\n",
+    "DROPOUT = 0.5\n",
+    "MAX_LEN = 128\n",
+    "\n",
+    "def lit_ckpt_to_torch(ckpt: str):\n",
+    "    \"\"\"\n",
+    "    Convert a lightning checkpoint to a torch state dict\n",
+    "    \"\"\"\n",
+    "    state_dict = torch.load(ckpt, map_location='cpu')['state_dict']\n",
+    "    \n",
+    "    for k, v in dict(state_dict).items():\n",
+    "        # lightning introduced model. prefix\n",
+    "        if k.startswith('model.'):\n",
+    "            state_dict[k[len('model.'):]] = v\n",
+    "            del state_dict[k]\n",
+    "\n",
+    "    return state_dict\n",
+    "\n",
+    "def load_torch(path: str, model: nn.Module):\n",
+    "    if path.endswith('.ckpt'):\n",
+    "        state_dict = lit_ckpt_to_torch(path)\n",
+    "        model.load_state_dict(state_dict)\n",
+    "        torch.save(model.state_dict(), path.replace('.ckpt', '.torch'))\n",
+    "    elif path.endswith('.torch'):\n",
+    "        state_dict = torch.load(path, map_location='cpu')\n",
+    "        model.load_state_dict(state_dict)\n",
+    "    else:\n",
+    "        raise ValueError('Unknown model format')\n",
+    "    return model\n",
+    "\n",
+    "def load_xgb(path: str):\n",
+    "    if path.endswith('.pickle'):\n",
+    "        with open(path, 'rb') as f:\n",
+    "            model = pickle.load(f)\n",
+    "        model.save_model(path.replace('.pickle', '.xgboost'))\n",
+    "    elif path.endswith('.xgboost'):\n",
+    "        model = XGBClassifier(n_estimators=100, max_depth=10, random_state=SEED)\n",
+    "        model.load_model(path)\n",
+    "    else:\n",
+    "        raise ValueError('Unknown model format')\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "*_, X_test_baseline_cmds = load_data(ROOT, seed=SEED)\n",
+    "\n",
+    "# each set has randomly subsampled 50000 commands from the original malicious commands\n",
+    "\n",
+    "# orig test\n",
+    "X_test_malicious_cmds_path = os.path.join(ROOT, \"data\", \"X_test_malicious_cmd_orig.json\")\n",
+    "with open(X_test_malicious_cmds_path, 'r') as f:\n",
+    "    X_test_malicious_cmds = json.load(f)\n",
+    "\n",
+    "X_test_cmds_orig = X_test_malicious_cmds + X_test_baseline_cmds\n",
+    "y_test_orig = np.array([1]*len(X_test_malicious_cmds) + [0]*len(X_test_baseline_cmds))\n",
+    "X_test_cmds_orig, y_test_orig = shuffle(X_test_cmds_orig, y_test_orig, random_state=SEED)\n",
+    "\n",
+    "# adversarial test\n",
+    "X_test_malicious_cmds_adv_path = os.path.join(ROOT, \"data\", \"X_test_malicious_cmd_adv.json\")\n",
+    "with open(X_test_malicious_cmds_adv_path, 'r') as f:\n",
+    "    X_test_malicious_cmds_adv = json.load(f)\n",
+    "\n",
+    "X_test_cmds_adv = X_test_malicious_cmds_adv + X_test_baseline_cmds\n",
+    "y_test_adv = np.array([1]*len(X_test_malicious_cmds_adv) + [0]*len(X_test_baseline_cmds))\n",
+    "X_test_cmds_adv, y_test_adv = shuffle(X_test_cmds_adv, y_test_adv, random_state=SEED)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TABULAR MODELS\n",
+    "\n",
+    "oh_tokenizer_path_orig = \"./quasarnix_tokenizer_data_train_onehot_orig.pickle\"\n",
+    "with open(oh_tokenizer_path_orig, 'rb') as f:\n",
+    "    oh_tokenizer_orig = pickle.load(f)\n",
+    "\n",
+    "oh_tokenizer_path_adv = \"./quasarnix_tokenizer_data_train_onehot_adv.pickle\"\n",
+    "with open(oh_tokenizer_path_adv, 'rb') as f:\n",
+    "    oh_tokenizer_adv = pickle.load(f)\n",
+    "\n",
+    "oh_tokenizer_path_full = \"./quasarnix_tokenizer_data_full_onehot_adv.pickle\"\n",
+    "with open(oh_tokenizer_path_full, 'rb') as f:\n",
+    "    oh_tokenizer_full = pickle.load(f)\n",
+    "\n",
+    "xgb_model_path_orig = './quasarnix_model_data_train_xgb_orig.xgboost'\n",
+    "xgb_model_path_adv = './quasarnix_model_data_train_xgb_adv.xgboost'\n",
+    "xgb_model_path_full = './quasarnix_model_data_full_xgb_adv.xgboost'\n",
+    "\n",
+    "mlp_model_path_orig = './quasarnix_model_data_train_mlp_orig.torch'\n",
+    "mlp_model_path_adv = './quasarnix_model_data_train_mlp_adv.torch'\n",
+    "mlp_model_path_full = './quasarnix_model_data_full_mlp_adv.torch'\n",
+    "\n",
+    "# SEQUENTIAL EMBEDDING MODELS\n",
+    "\n",
+    "vocab_path_orig = \"./quasarnix_tokenizer_data_train_vocab_orig.json\"\n",
+    "tokenizer_orig = CommandTokenizer(tokenizer_fn=TOKENIZER, vocab_size=VOCAB_SIZE, max_len=MAX_LEN)\n",
+    "tokenizer_orig.load_vocab(vocab_path_orig)\n",
+    "\n",
+    "vocab_path_adv = \"./quasarnix_tokenizer_data_train_vocab_adv.json\"\n",
+    "tokenizer_adv = CommandTokenizer(tokenizer_fn=TOKENIZER, vocab_size=VOCAB_SIZE, max_len=MAX_LEN)\n",
+    "tokenizer_adv.load_vocab(vocab_path_adv)\n",
+    "\n",
+    "vocab_path_full = \"./quasarnix_tokenizer_data_full_vocab_adv.json\"\n",
+    "tokenizer_full = CommandTokenizer(tokenizer_fn=TOKENIZER, vocab_size=VOCAB_SIZE, max_len=MAX_LEN)\n",
+    "tokenizer_full.load_vocab(vocab_path_full)\n",
+    "\n",
+    "cnn_model_path_orig = './quasarnix_model_data_train_cnn_orig.torch'\n",
+    "cnn_model_path_adv = './quasarnix_model_data_train_cnn_adv.torch'\n",
+    "cnn_model_path_full = './quasarnix_model_data_full_cnn_adv.torch'\n",
+    "\n",
+    "transformer_model_path_orig = './quasarnix_model_data_train_transformer_orig.torch'\n",
+    "transformer_model_path_adv = './quasarnix_model_data_train_transformer_adv.torch'\n",
+    "transformer_model_path_full = './quasarnix_model_data_full_transformer_adv.torch'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_preds(\n",
+    "    model: Union[XGBClassifier, nn.Module],\n",
+    "    X_cmds: List[str],\n",
+    "    y: np.ndarray,\n",
+    "    tokenizer: Union[OneHotCustomVectorizer, CommandTokenizer],\n",
+    "    threshold: Optional[float] = None\n",
+    "):\n",
+    "    now = time()\n",
+    "    if isinstance(model, XGBClassifier):\n",
+    "        print(f\"[*] Working on {len(X_cmds)} samples\", end='\\r')\n",
+    "        X_encoded = tokenizer.transform(X_cmds)\n",
+    "        y_pred = model.predict_proba(X_encoded)[:, 1]\n",
+    "    elif isinstance(model, nn.Module):\n",
+    "        print(f\"[*] Building DataLoader for {len(X_cmds)} samples\", end='\\r')\n",
+    "        loader = commands_to_loader(X_cmds, tokenizer, y=y, workers=4, batch_size=256)\n",
+    "        model = model.to('cuda')\n",
+    "        model.eval()\n",
+    "        with torch.no_grad():\n",
+    "            y_pred = []\n",
+    "            for (x, _) in tqdm(loader, desc=\"[*] Predicting\", total=math.ceil(len(X_cmds)/256)):\n",
+    "                x = x.to('cuda')\n",
+    "                y_pred.append(model(x).cpu().numpy())\n",
+    "            y_pred = np.concatenate(y_pred).flatten()\n",
+    "    else:\n",
+    "        raise ValueError(\"Unknown model type\")\n",
+    "    \n",
+    "    if threshold is not None:\n",
+    "        y_pred = y_pred > threshold\n",
+    "        acc = accuracy_score(y, y_pred > 0.5)\n",
+    "        f1 = f1_score(y, y_pred > 0.5)\n",
+    "        print(f\"[!] Accuracy: {acc*100:.3f}%, F1: {f1*100:.3f}% | Took {time()-now:.2f}s\")\n",
+    "        \n",
+    "    return y_pred, y\n",
+    "\n",
+    "def score_both_sets(model, tokenizer):\n",
+    "    print(\"Original Test Set:\")\n",
+    "    y_pred_orig, y_true_orig = get_preds(model, X_test_cmds_orig, y_test_orig, tokenizer, threshold=0.5)\n",
+    "    print(\"\\nAdversarial Test Set:\")\n",
+    "    y_pred_adv, y_true_adv = get_preds(model, X_test_cmds_adv, y_test_adv, tokenizer, threshold=0.5)\n",
+    "    return y_pred_orig, y_true_orig, y_pred_adv, y_true_adv\n",
+    "\n",
+    "def plot_roc(y_true, y_pred, ax):\n",
+    "    fpr, tpr, _ = roc_curve(y_true, y_pred)\n",
+    "    ax.plot(fpr, tpr)\n",
+    "    return ax"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Gradient Boosted Decision Trees (GBDT) with XGBoost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Test Set:\n",
+      "[!] Accuracy: 99.968%, F1: 99.968% | Took 25.27s\n",
+      "\n",
+      "Adversarial Test Set:\n",
+      "[!] Accuracy: 83.418%, F1: 80.123% | Took 31.22s\n"
+     ]
+    }
+   ],
+   "source": [
+    "xgb_orig = load_xgb(xgb_model_path_orig)\n",
+    "_ = score_both_sets(xgb_orig, oh_tokenizer_orig)\n",
+    "\n",
+    "# see params\n",
+    "# print(xgb_full_adv.get_booster().get_dump()[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Test Set:\n",
+      "[*] Working on 470129 samples\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[!] Accuracy: 99.954%, F1: 99.954% | Took 24.01s\n",
+      "\n",
+      "Adversarial Test Set:\n",
+      "[!] Accuracy: 99.975%, F1: 99.975% | Took 32.38s\n"
+     ]
+    }
+   ],
+   "source": [
+    "xgb_adv = load_xgb(xgb_model_path_adv)\n",
+    "_ = score_both_sets(xgb_adv, oh_tokenizer_adv)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Test Set:\n",
+      "[*] Working on 470129 samples\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[!] Accuracy: 100.000%, F1: 100.000% | Took 27.10s\n",
+      "\n",
+      "Adversarial Test Set:\n",
+      "[!] Accuracy: 100.000%, F1: 100.000% | Took 31.26s\n"
+     ]
+    }
+   ],
+   "source": [
+    "xgb_full_adv = load_xgb(xgb_model_path_full)\n",
+    "_ = score_both_sets(xgb_full_adv, oh_tokenizer_full)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tabular Fully Connected Neural Network (aka MLP) with PyTorch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Test Set:\n",
+      "[*] Building DataLoader for 470129 samples\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] Predicting: 100%|██████████| 1837/1837 [00:10<00:00, 181.18it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[!] Accuracy: 99.930%, F1: 99.930% | Took 34.68s\n",
+      "\n",
+      "Adversarial Test Set:\n",
+      "[*] Building DataLoader for 470129 samples\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] Predicting: 100%|██████████| 1837/1837 [00:10<00:00, 170.18it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[!] Accuracy: 89.179%, F1: 87.868% | Took 42.86s\n"
+     ]
+    }
+   ],
+   "source": [
+    "class SimpleMLP(nn.Module):\n",
+    "    def __init__(self, input_dim, output_dim, hidden_dim=[32], dropout=None):\n",
+    "        if isinstance(hidden_dim, int):\n",
+    "            hidden_dim = [hidden_dim]\n",
+    "        \n",
+    "        super().__init__()\n",
+    "        layers = []\n",
+    "        prev_dim = input_dim\n",
+    "        \n",
+    "        # Dynamically create hidden layers based on hidden_dim\n",
+    "        for h_dim in hidden_dim:\n",
+    "            layers.append(nn.Linear(prev_dim, h_dim))\n",
+    "            layers.append(nn.ReLU())\n",
+    "            if dropout:\n",
+    "                layers.append(nn.Dropout(dropout))\n",
+    "            prev_dim = h_dim\n",
+    "        \n",
+    "        layers.append(nn.Linear(prev_dim, output_dim))\n",
+    "        self.model = nn.Sequential(*layers)\n",
+    "    \n",
+    "    def forward(self, x):\n",
+    "        return self.model(x)\n",
+    "\n",
+    "\n",
+    "mlp_orig = SimpleMLP(\n",
+    "    input_dim=VOCAB_SIZE,\n",
+    "    output_dim=1,\n",
+    "    hidden_dim=[64, 32],\n",
+    "    dropout=DROPOUT\n",
+    ") # 264 K params\n",
+    "\n",
+    "\n",
+    "mlp_orig = load_torch(mlp_model_path_orig, mlp_orig)\n",
+    "_ = score_both_sets(mlp_orig, oh_tokenizer_orig)\n",
+    "\n",
+    "# see params\n",
+    "# mlp_orig.state_dict()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Test Set:\n",
+      "[*] Building DataLoader for 470129 samples\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] Predicting: 100%|██████████| 1837/1837 [00:11<00:00, 158.51it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[!] Accuracy: 99.943%, F1: 99.943% | Took 34.79s\n",
+      "\n",
+      "Adversarial Test Set:\n",
+      "[*] Building DataLoader for 470129 samples\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] Predicting: 100%|██████████| 1837/1837 [00:11<00:00, 157.97it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[!] Accuracy: 99.968%, F1: 99.968% | Took 44.49s\n"
+     ]
+    }
+   ],
+   "source": [
+    "mlp_adv = SimpleMLP(\n",
+    "    input_dim=VOCAB_SIZE,\n",
+    "    output_dim=1,\n",
+    "    hidden_dim=[64, 32],\n",
+    "    dropout=DROPOUT\n",
+    ") # 264 K params\n",
+    "\n",
+    "mlp_adv = load_torch(mlp_model_path_adv, mlp_adv)\n",
+    "_ = score_both_sets(mlp_adv, oh_tokenizer_adv)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Test Set:\n",
+      "[*] Building DataLoader for 470129 samples\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] Predicting: 100%|██████████| 1837/1837 [00:11<00:00, 155.91it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[!] Accuracy: 99.999%, F1: 99.999% | Took 35.86s\n",
+      "\n",
+      "Adversarial Test Set:\n",
+      "[*] Building DataLoader for 470129 samples\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] Predicting: 100%|██████████| 1837/1837 [00:10<00:00, 169.64it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[!] Accuracy: 99.999%, F1: 99.999% | Took 43.86s\n"
+     ]
+    }
+   ],
+   "source": [
+    "mlp_full_adv = SimpleMLP(\n",
+    "    input_dim=VOCAB_SIZE,\n",
+    "    output_dim=1,\n",
+    "    hidden_dim=[64, 32],\n",
+    "    dropout=DROPOUT\n",
+    ") # 264 K params\n",
+    "\n",
+    "mlp_full_adv = load_torch(mlp_model_path_full, mlp_full_adv)\n",
+    "_ = score_both_sets(mlp_full_adv, oh_tokenizer_full)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1D Convolutional Neural Network with PyTorch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Test Set:\n",
+      "[*] Building DataLoader for 470129 samples\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] Predicting: 100%|██████████| 1837/1837 [00:02<00:00, 644.75it/s] \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[!] Accuracy: 97.619%, F1: 97.561% | Took 12.85s\n",
+      "\n",
+      "Adversarial Test Set:\n",
+      "[*] Building DataLoader for 470129 samples\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] Predicting: 100%|██████████| 1837/1837 [00:02<00:00, 716.00it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[!] Accuracy: 77.520%, F1: 71.001% | Took 15.09s\n"
+     ]
+    }
+   ],
+   "source": [
+    "class CNN1DGroupedModel(nn.Module):\n",
+    "    def __init__(self, vocab_size, embed_dim, num_channels, kernel_sizes, mlp_hidden_dims, output_dim, dropout=None):\n",
+    "        super().__init__()\n",
+    "        \n",
+    "        self.embedding = nn.Embedding(vocab_size, embed_dim)\n",
+    "        self.grouped_convs = nn.ModuleList([nn.Conv1d(embed_dim, num_channels, kernel) for kernel in kernel_sizes])\n",
+    "        \n",
+    "        mlp_input_dim = num_channels * len(kernel_sizes)\n",
+    "        self.mlp = SimpleMLP(input_dim=mlp_input_dim, output_dim=output_dim, hidden_dim=mlp_hidden_dims, dropout=dropout)\n",
+    "\n",
+    "    @staticmethod\n",
+    "    def conv_and_pool(x, conv):\n",
+    "        conv_out = conv(x)\n",
+    "        pooled = F.max_pool1d(conv_out, conv_out.size(2)).squeeze(2)\n",
+    "        return pooled\n",
+    "    \n",
+    "    def forward(self, x):\n",
+    "        x = self.embedding(x).transpose(1, 2)\n",
+    "        conv_outputs = [self.conv_and_pool(x, conv) for conv in self.grouped_convs]\n",
+    "\n",
+    "        x = torch.cat(conv_outputs, dim=1)\n",
+    "        return self.mlp(x)\n",
+    "\n",
+    "\n",
+    "cnn_orig = CNN1DGroupedModel(\n",
+    "    vocab_size=VOCAB_SIZE,\n",
+    "    embed_dim=EMBEDDED_DIM,\n",
+    "    num_channels=32,\n",
+    "    kernel_sizes=[2, 3, 4, 5],\n",
+    "    mlp_hidden_dims=[64, 32],\n",
+    "    output_dim=1,\n",
+    "    dropout=DROPOUT\n",
+    ") # 301 K params\n",
+    "\n",
+    "cnn_orig = load_torch(cnn_model_path_orig, cnn_orig)\n",
+    "_ = score_both_sets(cnn_orig, tokenizer_orig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Test Set:\n",
+      "[*] Building DataLoader for 470129 samples\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] Predicting: 100%|██████████| 1837/1837 [00:03<00:00, 605.99it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[!] Accuracy: 99.992%, F1: 99.992% | Took 13.84s\n",
+      "\n",
+      "Adversarial Test Set:\n",
+      "[*] Building DataLoader for 470129 samples\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] Predicting: 100%|██████████| 1837/1837 [00:02<00:00, 653.34it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[!] Accuracy: 99.995%, F1: 99.995% | Took 14.61s\n"
+     ]
+    }
+   ],
+   "source": [
+    "cnn_adv = CNN1DGroupedModel(\n",
+    "    vocab_size=VOCAB_SIZE,\n",
+    "    embed_dim=EMBEDDED_DIM,\n",
+    "    num_channels=32,\n",
+    "    kernel_sizes=[2, 3, 4, 5],\n",
+    "    mlp_hidden_dims=[64, 32],\n",
+    "    output_dim=1,\n",
+    "    dropout=DROPOUT\n",
+    ") # 301 K params\n",
+    "\n",
+    "cnn_adv = load_torch(cnn_model_path_adv, cnn_adv)\n",
+    "_ = score_both_sets(cnn_adv, tokenizer_adv)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Test Set:\n",
+      "[*] Building DataLoader for 470129 samples\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] Predicting: 100%|██████████| 1837/1837 [00:02<00:00, 736.91it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[!] Accuracy: 99.999%, F1: 99.999% | Took 12.76s\n",
+      "\n",
+      "Adversarial Test Set:\n",
+      "[*] Building DataLoader for 470129 samples\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] Predicting: 100%|██████████| 1837/1837 [00:02<00:00, 625.40it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[!] Accuracy: 99.999%, F1: 99.999% | Took 14.06s\n"
+     ]
+    }
+   ],
+   "source": [
+    "cnn_full_adv = CNN1DGroupedModel(\n",
+    "    vocab_size=VOCAB_SIZE,\n",
+    "    embed_dim=EMBEDDED_DIM,\n",
+    "    num_channels=32,\n",
+    "    kernel_sizes=[2, 3, 4, 5],\n",
+    "    mlp_hidden_dims=[64, 32],\n",
+    "    output_dim=1,\n",
+    "    dropout=DROPOUT\n",
+    ") # 301 K params\n",
+    "\n",
+    "cnn_full_adv = load_torch(cnn_model_path_full, cnn_full_adv)\n",
+    "_ = score_both_sets(cnn_full_adv, tokenizer_full)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Transformer Encoder for Classification"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class PositionalEncoding(nn.Module):\n",
+    "    def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 5000):\n",
+    "        super().__init__()\n",
+    "        self.dropout = nn.Dropout(p=dropout)\n",
+    "\n",
+    "        # Initialize pe with shape [1, max_len, d_model] for broadcasting\n",
+    "        pe = torch.zeros(1, max_len, d_model)\n",
+    "        position = torch.arange(max_len).unsqueeze(1)\n",
+    "        div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))\n",
+    "        pe[0, :, 0::2] = torch.sin(position * div_term)\n",
+    "        pe[0, :, 1::2] = torch.cos(position * div_term)\n",
+    "        self.register_buffer('pe', pe)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        \"\"\"\n",
+    "        Args:\n",
+    "            x: Tensor, shape [batch_size, seq_len, embedding_dim]\n",
+    "        \"\"\"\n",
+    "        # Use broadcasting to add positional encoding\n",
+    "        x = x + self.pe[:, :x.size(1), :]\n",
+    "        return self.dropout(x)\n",
+    "\n",
+    "class BaseTransformerEncoder(nn.Module):\n",
+    "    def __init__(self, vocab_size, d_model, nhead, num_layers, dim_feedforward, max_len, dropout=None):\n",
+    "        super(BaseTransformerEncoder, self).__init__()\n",
+    "        \n",
+    "        assert d_model % nhead == 0, \"nheads must divide evenly into d_model\"\n",
+    "        self.embedding = nn.Embedding(vocab_size, d_model)\n",
+    "        self.pos_encoder = PositionalEncoding(d_model, dropout, max_len=max_len)\n",
+    "        encoder_layer = nn.TransformerEncoderLayer(\n",
+    "            d_model,\n",
+    "            nhead,\n",
+    "            dim_feedforward,\n",
+    "            dropout,\n",
+    "            norm_first=True,\n",
+    "            batch_first=True\n",
+    "        )\n",
+    "        self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers)\n",
+    "\n",
+    "    def encode(self, src, src_mask=None, src_key_padding_mask=None):\n",
+    "        src = self.embedding(src) * math.sqrt(self.embedding.embedding_dim)\n",
+    "        src = self.pos_encoder(src)\n",
+    "        return self.transformer_encoder(src, mask=src_mask, src_key_padding_mask=src_key_padding_mask)\n",
+    "\n",
+    "class CLSTransformerEncoder(BaseTransformerEncoder):\n",
+    "    def __init__(self, mlp_hidden_dims, output_dim, *args, **kwargs):\n",
+    "        kwargs[\"max_len\"] += 1 # to account for CLS token\n",
+    "        super(CLSTransformerEncoder, self).__init__(*args, **kwargs)\n",
+    "        self.cls_token = nn.Parameter(torch.randn(1, 1, self.embedding.embedding_dim))\n",
+    "        self.decoder = SimpleMLP(input_dim=self.embedding.embedding_dim, output_dim=output_dim, hidden_dim=mlp_hidden_dims, dropout=kwargs.get(\"dropout\"))\n",
+    "\n",
+    "    def forward(self, src, src_mask=None, src_key_padding_mask=None):\n",
+    "        # Embed the src token indices\n",
+    "        src = self.embedding(src) * math.sqrt(self.embedding.embedding_dim)\n",
+    "        \n",
+    "        # Repeat the cls_token for every item in the batch and concatenate it to src\n",
+    "        cls_tokens = self.cls_token.repeat(src.size(0), 1, 1)\n",
+    "        src = torch.cat([cls_tokens, src], dim=1)\n",
+    "        \n",
+    "        # Add positional encoding\n",
+    "        src = self.pos_encoder(src)\n",
+    "        \n",
+    "        # Pass through transformer encoder\n",
+    "        output = self.transformer_encoder(src, mask=src_mask, src_key_padding_mask=src_key_padding_mask)\n",
+    "        \n",
+    "        # Extract the encoding corresponding to the cls_token\n",
+    "        output = output[:, 0, :]  # [B, E]\n",
+    "        \n",
+    "        return self.decoder(output)\n",
+    "\n",
+    "\n",
+    "class MeanTransformerEncoder(BaseTransformerEncoder):\n",
+    "    def __init__(self, mlp_hidden_dims, output_dim, *args, **kwargs):\n",
+    "        super(MeanTransformerEncoder, self).__init__(*args, **kwargs)\n",
+    "        self.decoder = SimpleMLP(input_dim=self.embedding.embedding_dim, output_dim=output_dim, hidden_dim=mlp_hidden_dims, dropout=kwargs.get(\"dropout\"))\n",
+    "\n",
+    "    def forward(self, src, src_mask=None, src_key_padding_mask=None):\n",
+    "        output = self.encode(src, src_mask, src_key_padding_mask)\n",
+    "        output = output.mean(dim=1)\n",
+    "        return self.decoder(output)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Test Set:\n",
+      "[*] Building DataLoader for 470129 samples\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/dtrizna/.local/lib/python3.10/site-packages/torch/nn/modules/transformer.py:286: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.norm_first was True\n",
+      "  warnings.warn(f\"enable_nested_tensor is True, but self.use_nested_tensor is False because {why_not_sparsity_fast_path}\")\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] Predicting: 100%|██████████| 1837/1837 [00:04<00:00, 387.08it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[!] Accuracy: 99.696%, F1: 99.696% | Took 15.01s\n",
+      "\n",
+      "Adversarial Test Set:\n",
+      "[*] Building DataLoader for 470129 samples\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] Predicting: 100%|██████████| 1837/1837 [00:07<00:00, 234.15it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[!] Accuracy: 86.388%, F1: 84.245% | Took 20.70s\n"
+     ]
+    }
+   ],
+   "source": [
+    "transformer_orig = CLSTransformerEncoder(\n",
+    "    vocab_size=VOCAB_SIZE,\n",
+    "    d_model=EMBEDDED_DIM,\n",
+    "    nhead=4,\n",
+    "    num_layers=2,\n",
+    "    dim_feedforward=128,\n",
+    "    max_len=128,\n",
+    "    dropout=DROPOUT,\n",
+    "    mlp_hidden_dims=[64, 32],\n",
+    "    output_dim=1\n",
+    ") #  335 K params\n",
+    "\n",
+    "transformer_orig = load_torch(transformer_model_path_orig, transformer_orig)\n",
+    "_ = score_both_sets(transformer_orig, tokenizer_orig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Test Set:\n",
+      "[*] Building DataLoader for 470129 samples\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/dtrizna/.local/lib/python3.10/site-packages/torch/nn/modules/transformer.py:286: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.norm_first was True\n",
+      "  warnings.warn(f\"enable_nested_tensor is True, but self.use_nested_tensor is False because {why_not_sparsity_fast_path}\")\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] Predicting: 100%|██████████| 1837/1837 [00:05<00:00, 307.70it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[!] Accuracy: 99.749%, F1: 99.750% | Took 17.17s\n",
+      "\n",
+      "Adversarial Test Set:\n",
+      "[*] Building DataLoader for 470129 samples\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] Predicting: 100%|██████████| 1837/1837 [00:05<00:00, 330.66it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[!] Accuracy: 99.749%, F1: 99.750% | Took 18.48s\n"
+     ]
+    }
+   ],
+   "source": [
+    "transformer_adv = CLSTransformerEncoder(\n",
+    "    vocab_size=VOCAB_SIZE,\n",
+    "    d_model=EMBEDDED_DIM,\n",
+    "    nhead=4,\n",
+    "    num_layers=2,\n",
+    "    dim_feedforward=128,\n",
+    "    max_len=128,\n",
+    "    dropout=DROPOUT,\n",
+    "    mlp_hidden_dims=[64, 32],\n",
+    "    output_dim=1\n",
+    ") #  335 K params\n",
+    "\n",
+    "transformer_adv = load_torch(transformer_model_path_adv, transformer_adv)\n",
+    "_ = score_both_sets(transformer_adv, tokenizer_adv)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Test Set:\n",
+      "[*] Building DataLoader for 470129 samples\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/dtrizna/.local/lib/python3.10/site-packages/torch/nn/modules/transformer.py:286: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.norm_first was True\n",
+      "  warnings.warn(f\"enable_nested_tensor is True, but self.use_nested_tensor is False because {why_not_sparsity_fast_path}\")\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] Predicting: 100%|██████████| 1837/1837 [00:05<00:00, 334.43it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[!] Accuracy: 99.997%, F1: 99.997% | Took 15.61s\n",
+      "\n",
+      "Adversarial Test Set:\n",
+      "[*] Building DataLoader for 470129 samples\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] Predicting: 100%|██████████| 1837/1837 [00:05<00:00, 334.75it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[!] Accuracy: 99.997%, F1: 99.997% | Took 18.08s\n"
+     ]
+    }
+   ],
+   "source": [
+    "transformer_full_adv = CLSTransformerEncoder(\n",
+    "    vocab_size=VOCAB_SIZE,\n",
+    "    d_model=EMBEDDED_DIM,\n",
+    "    nhead=4,\n",
+    "    num_layers=2,\n",
+    "    dim_feedforward=128,\n",
+    "    max_len=128,\n",
+    "    dropout=DROPOUT,\n",
+    "    mlp_hidden_dims=[64, 32],\n",
+    "    output_dim=1\n",
+    ") #  335 K params\n",
+    "\n",
+    "transformer_full_adv = load_torch(transformer_model_path_full, transformer_full_adv)\n",
+    "_ = score_both_sets(transformer_full_adv, tokenizer_full)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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