MultiClassification Model.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pgDsv6TISNPy",
        "outputId": "264e70e0-0158-429e-de80-943ea678b278"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/externals/six.py:31: FutureWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
            "  \"(https://pypi.org/project/six/).\", FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.neighbors.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.neighbors. Anything that cannot be imported from sklearn.neighbors is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd \n",
        "import numpy as np\n",
        "from tensorflow import keras\n",
        "import tensorflow as tf\n",
        "import re\n",
        "import nltk \n",
        "import warnings\n",
        "import pickle\n",
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from functools import partial\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.svm import SVC\n",
        "from imblearn.over_sampling import SMOTE \n",
        "from collections import Counter\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from sklearn.model_selection import train_test_split,GridSearchCV\n",
        "from sklearn import preprocessing\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from collections import defaultdict\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Embedding,SimpleRNN,Dense,LSTM,Dropout,Conv1D,MaxPooling1D,Flatten,BatchNormalization,Bidirectional,Lambda\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from keras.models import load_model\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from keras.regularizers import l1,l2\n",
        "from sklearn.metrics import classification_report,confusion_matrix,precision_recall_curve, roc_curve\n",
        "warnings.filterwarnings('ignore')\n",
        "nltk.download('punkt') #this statement should be executed only in Colab "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZRye4z57bpPI"
      },
      "outputs": [],
      "source": [
        "import zipfile\n",
        "with zipfile.ZipFile(\"/content/drive/MyDrive/DataSets/Tokenized/Tokenized.zip\", 'r') as zip_ref:\n",
        "    zip_ref.extractall(\"/content/drive/MyDrive/DataSets/Tokenized/Extracted files\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uXVnSsJIIpw1"
      },
      "source": [
        "# Get the 4 kinds of SySeVR project"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U7tyWeA_SdY9"
      },
      "outputs": [],
      "source": [
        "p = '/content/drive/MyDrive/DataSets/Tokenized/Extracted files/VUL//'\n",
        "Dataset_API=pd.read_csv(p + 'API_function_call-processed.csv')\n",
        "Dataset_Arithmetic_Expression=pd.read_csv( p + 'Arithmetic_expression-processed.csv')\n",
        "Dataset_Array_Usage=pd.read_csv(p + 'Array_usage-processed.csv')\n",
        "Dataset_Pointer_Usage=pd.read_csv(p + 'Pointer_usage-processed.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "CeIdQwAhZa6v",
        "outputId": "0de373db-b2e2-44f8-b7aa-c549241b9900"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>tokenized</th>\n",
              "      <th>cwe</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>void FUNC1( ( ) wchar_t * VAR1 ; wchar_t * VAR...</td>\n",
              "      <td>CWE121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>void FUNC1( ( ) wchar_t * VAR1 ; wchar_t * VAR...</td>\n",
              "      <td>CWE121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>void FUNC1( ( ) wchar_t * VAR1 ; wchar_t * VAR...</td>\n",
              "      <td>CWE121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>void FUNC1( ( int * VAR1 ) int VAR2 [ NUMBER ]...</td>\n",
              "      <td>CWE122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>void FUNC1( ( ) int * VAR1 ; VAR1 = NULL ; VAR...</td>\n",
              "      <td>CWE122</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0                                          tokenized     cwe\n",
              "0           0  void FUNC1( ( ) wchar_t * VAR1 ; wchar_t * VAR...  CWE121\n",
              "1           1  void FUNC1( ( ) wchar_t * VAR1 ; wchar_t * VAR...  CWE121\n",
              "2           2  void FUNC1( ( ) wchar_t * VAR1 ; wchar_t * VAR...  CWE121\n",
              "3           3  void FUNC1( ( int * VAR1 ) int VAR2 [ NUMBER ]...  CWE122\n",
              "4           4  void FUNC1( ( ) int * VAR1 ; VAR1 = NULL ; VAR...  CWE122"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Dataset_API.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3z6FlKHG1h4J"
      },
      "source": [
        "# Check if all datasets' columns name are the **same** "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lYgal29iTOB-",
        "outputId": "dec1fdf0-3817-48cd-fd6f-388b05ff6283"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Index(['Unnamed: 0', 'tokenized', 'cwe'], dtype='object')\n",
            "Index(['Unnamed: 0', 'tokenized', 'cwe'], dtype='object')\n",
            "Index(['Unnamed: 0', 'tokenized', 'cwe'], dtype='object')\n",
            "Index(['Unnamed: 0', 'tokenized', 'cwe'], dtype='object')\n"
          ]
        }
      ],
      "source": [
        "print(Dataset_API.columns)\n",
        "print(Dataset_Arithmetic_Expression.columns)\n",
        "print(Dataset_Array_Usage.columns)\n",
        "print(Dataset_Pointer_Usage.columns)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DmO8Ydr_I5-G"
      },
      "source": [
        "# Merge all datasets into one"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yv8Kb5rDTaXu"
      },
      "outputs": [],
      "source": [
        "Dataset_4_kind=pd.concat([Dataset_API,Dataset_Arithmetic_Expression,\n",
        "                          Dataset_Array_Usage,Dataset_Pointer_Usage]\n",
        "                         ,ignore_index=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PDE-BckqTw3m",
        "outputId": "7e29e446-ffda-4474-c689-d0f2b4ff96b9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "CWE121    4930\n",
              "CWE122    4232\n",
              "CWE134    2477\n",
              "CWE124    1947\n",
              "CWE127    1721\n",
              "CWE194    1416\n",
              "CWE195    1270\n",
              "CWE78     1223\n",
              "CWE126    1082\n",
              "CWE690     905\n",
              "CWE789     892\n",
              "CWE590     725\n",
              "CWE680     672\n",
              "CWE114     625\n",
              "CWE190     545\n",
              "CWE319     478\n",
              "CWE761     449\n",
              "CWE666     400\n",
              "CWE369     335\n",
              "CWE427     299\n",
              "CWE457     298\n",
              "CWE400     265\n",
              "CWE758     186\n",
              "CWE401     186\n",
              "CWE415     183\n",
              "CWE197     178\n",
              "CWE90      173\n",
              "CWE506     166\n",
              "CWE191     159\n",
              "CWE416     150\n",
              "CWE617     132\n",
              "CWE426     128\n",
              "CWE476     119\n",
              "CWE253     118\n",
              "CWE252     118\n",
              "CWE591      95\n",
              "CWE675      90\n",
              "CWE404      84\n",
              "CWE244      83\n",
              "CWE377      71\n",
              "CWE469      70\n",
              "CWE259      69\n",
              "CWE467      60\n",
              "CWE773      60\n",
              "CWE510      53\n",
              "CWE325      51\n",
              "CWE464      50\n",
              "CWE256      49\n",
              "CWE605      41\n",
              "CWE321      40\n",
              "Name: cwe, dtype: int64"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "classes = 50\n",
        "Dataset_4_kind[Dataset_4_kind.columns[2]].value_counts()[:classes]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bNq4_mqJV4Ll"
      },
      "outputs": [],
      "source": [
        "unwanted_class=list(dict(Dataset_4_kind[Dataset_4_kind.columns[2]].value_counts()[classes:]).keys())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_G7VwU8SXsrW"
      },
      "outputs": [],
      "source": [
        "for index,row in Dataset_4_kind.iterrows():\n",
        "    if row[Dataset_4_kind.columns[2]] in unwanted_class:\n",
        "        Dataset_4_kind.drop(index,inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "54bSTwVcJEky"
      },
      "source": [
        "# Use simple method to process text **Tokenizer**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TqGAY6wwX4GF"
      },
      "outputs": [],
      "source": [
        "vocab_size = 10000\n",
        "embedding_dim = 400\n",
        "max_length = 300\n",
        "trunc_type='post'\n",
        "oov_tok = \"<OOV>\"\n",
        "\n",
        "tokenizer = Tokenizer(num_words = vocab_size, oov_token=oov_tok)\n",
        "tokenizer.fit_on_texts(list(Dataset_4_kind[Dataset_4_kind.columns[1]]))\n",
        "word_index = tokenizer.word_index\n",
        "\n",
        "sequences = tokenizer.texts_to_sequences(list(Dataset_4_kind[Dataset_4_kind.columns[1]]))\n",
        "X_padded = pad_sequences(sequences,maxlen=max_length, truncating=trunc_type)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z_MUWyLsJseq"
      },
      "source": [
        "### The y in our dataset is text (e.g. CWE199).\n",
        "\n",
        "### So we need to convert it to one hot vector using **to_categorical** function\n",
        "\n",
        "### But we can't convert it directly because **to_categorical** take only numbers, And we convert it to number using **LabelEncoder()**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l7250T-oYby9"
      },
      "outputs": [],
      "source": [
        "label_encoder = preprocessing.LabelEncoder()\n",
        "y = label_encoder.fit_transform(Dataset_4_kind[Dataset_4_kind.columns[2]])\n",
        "y_categorical = to_categorical(y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ftpQuT_BJScW",
        "outputId": "ee4d8be1-4d9f-4d15-cca8-7a52bc4d2d24"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['CWE114', 'CWE121', 'CWE122', 'CWE124', 'CWE126', 'CWE127',\n",
              "       'CWE134', 'CWE190', 'CWE191', 'CWE194', 'CWE195', 'CWE197',\n",
              "       'CWE244', 'CWE252', 'CWE253', 'CWE256', 'CWE259', 'CWE319',\n",
              "       'CWE321', 'CWE325', 'CWE369', 'CWE377', 'CWE400', 'CWE401',\n",
              "       'CWE404', 'CWE415', 'CWE416', 'CWE426', 'CWE427', 'CWE457',\n",
              "       'CWE464', 'CWE467', 'CWE469', 'CWE476', 'CWE506', 'CWE510',\n",
              "       'CWE590', 'CWE591', 'CWE605', 'CWE617', 'CWE666', 'CWE675',\n",
              "       'CWE680', 'CWE690', 'CWE758', 'CWE761', 'CWE773', 'CWE78',\n",
              "       'CWE789', 'CWE90'], dtype=object)"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "label_encoder.classes_  #Index of CWE78 is 13 so the predict should be also 13 "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dpyuqHdF3LQH"
      },
      "source": [
        "# Solve Imblanced issue using data augmentation on SMOTE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Y9voC8B8Y-9-"
      },
      "outputs": [],
      "source": [
        "sm = SMOTE(random_state=42)\n",
        "X_res, y_res = sm.fit_resample(X_padded, y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qydB6NjZZfv9",
        "outputId": "71046b09-4523-4e9e-bcf4-c23551d02521"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Counter({0: 4930,\n",
              "         1: 4930,\n",
              "         2: 4930,\n",
              "         3: 4930,\n",
              "         4: 4930,\n",
              "         5: 4930,\n",
              "         6: 4930,\n",
              "         7: 4930,\n",
              "         8: 4930,\n",
              "         9: 4930,\n",
              "         10: 4930,\n",
              "         11: 4930,\n",
              "         12: 4930,\n",
              "         13: 4930,\n",
              "         14: 4930,\n",
              "         15: 4930,\n",
              "         16: 4930,\n",
              "         17: 4930,\n",
              "         18: 4930,\n",
              "         19: 4930,\n",
              "         20: 4930,\n",
              "         21: 4930,\n",
              "         22: 4930,\n",
              "         23: 4930,\n",
              "         24: 4930,\n",
              "         25: 4930,\n",
              "         26: 4930,\n",
              "         27: 4930,\n",
              "         28: 4930,\n",
              "         29: 4930,\n",
              "         30: 4930,\n",
              "         31: 4930,\n",
              "         32: 4930,\n",
              "         33: 4930,\n",
              "         34: 4930,\n",
              "         35: 4930,\n",
              "         36: 4930,\n",
              "         37: 4930,\n",
              "         38: 4930,\n",
              "         39: 4930,\n",
              "         40: 4930,\n",
              "         41: 4930,\n",
              "         42: 4930,\n",
              "         43: 4930,\n",
              "         44: 4930,\n",
              "         45: 4930,\n",
              "         46: 4930,\n",
              "         47: 4930,\n",
              "         48: 4930,\n",
              "         49: 4930})"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "classes = np.argmax(y_res, axis = 1)\n",
        "Counter(classes)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X5Sacpm9a0qP"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X_res,y_res,test_size=0.12)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-WoDLxqKAPo-"
      },
      "outputs": [],
      "source": [
        "class myCallback(tf.keras.callbacks.Callback):\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "    if(logs.get('accuracy')>=0.99):\n",
        "      print(\"\\nReached 99.0% accuracy so cancelling training!\")\n",
        "      self.model.stop_training = True\n",
        "callbacks = myCallback()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZxV2ytFJa71C",
        "outputId": "9c06acff-4467-481d-fcd9-ba7435cf6741"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "6548/6548 [==============================] - 264s 38ms/step - loss: 1.4685 - accuracy: 0.5955 - val_loss: 0.3584 - val_accuracy: 0.8930\n",
            "Epoch 2/50\n",
            "6548/6548 [==============================] - 250s 38ms/step - loss: 0.3209 - accuracy: 0.9038 - val_loss: 0.2619 - val_accuracy: 0.9226\n",
            "Epoch 3/50\n",
            "6548/6548 [==============================] - 257s 39ms/step - loss: 0.2468 - accuracy: 0.9246 - val_loss: 0.2249 - val_accuracy: 0.9323\n",
            "Epoch 4/50\n",
            "6548/6548 [==============================] - 256s 39ms/step - loss: 0.2081 - accuracy: 0.9353 - val_loss: 0.2018 - val_accuracy: 0.9386\n",
            "Epoch 5/50\n",
            "6548/6548 [==============================] - 287s 44ms/step - loss: 0.1874 - accuracy: 0.9401 - val_loss: 0.1810 - val_accuracy: 0.9427\n",
            "Epoch 6/50\n",
            "6548/6548 [==============================] - 279s 43ms/step - loss: 0.1730 - accuracy: 0.9454 - val_loss: 0.1745 - val_accuracy: 0.9458\n",
            "Epoch 7/50\n",
            "6548/6548 [==============================] - 274s 42ms/step - loss: 0.1547 - accuracy: 0.9501 - val_loss: 0.1827 - val_accuracy: 0.9436\n",
            "Epoch 8/50\n",
            "6548/6548 [==============================] - 284s 43ms/step - loss: 0.1468 - accuracy: 0.9521 - val_loss: 0.1504 - val_accuracy: 0.9524\n",
            "Epoch 9/50\n",
            "6548/6548 [==============================] - 263s 40ms/step - loss: 0.1385 - accuracy: 0.9547 - val_loss: 0.1505 - val_accuracy: 0.9516\n",
            "Epoch 10/50\n",
            "6548/6548 [==============================] - 257s 39ms/step - loss: 0.1340 - accuracy: 0.9551 - val_loss: 0.1533 - val_accuracy: 0.9513\n",
            "Epoch 11/50\n",
            "6548/6548 [==============================] - 271s 41ms/step - loss: 0.1338 - accuracy: 0.9558 - val_loss: 0.1478 - val_accuracy: 0.9526\n",
            "Epoch 12/50\n",
            "6548/6548 [==============================] - 265s 40ms/step - loss: 0.1263 - accuracy: 0.9579 - val_loss: 0.1502 - val_accuracy: 0.9526\n",
            "Epoch 13/50\n",
            "6548/6548 [==============================] - 265s 40ms/step - loss: 0.1262 - accuracy: 0.9582 - val_loss: 0.1462 - val_accuracy: 0.9520\n",
            "Epoch 14/50\n",
            "6548/6548 [==============================] - 261s 40ms/step - loss: 0.1200 - accuracy: 0.9597 - val_loss: 0.1306 - val_accuracy: 0.9569\n",
            "Epoch 15/50\n",
            "6548/6548 [==============================] - 268s 41ms/step - loss: 0.1189 - accuracy: 0.9601 - val_loss: 0.1297 - val_accuracy: 0.9588\n",
            "Epoch 16/50\n",
            "6548/6548 [==============================] - 268s 41ms/step - loss: 0.1158 - accuracy: 0.9607 - val_loss: 0.1409 - val_accuracy: 0.9545\n",
            "Epoch 17/50\n",
            "6548/6548 [==============================] - 267s 41ms/step - loss: 0.1153 - accuracy: 0.9608 - val_loss: 0.1352 - val_accuracy: 0.9555\n",
            "Epoch 18/50\n",
            "6548/6548 [==============================] - 261s 40ms/step - loss: 0.1180 - accuracy: 0.9602 - val_loss: 0.1337 - val_accuracy: 0.9560\n",
            "Epoch 19/50\n",
            "6548/6548 [==============================] - 260s 40ms/step - loss: 0.1159 - accuracy: 0.9610 - val_loss: 0.1338 - val_accuracy: 0.9560\n",
            "Epoch 20/50\n",
            "6548/6548 [==============================] - 266s 41ms/step - loss: 0.1156 - accuracy: 0.9607 - val_loss: 0.1474 - val_accuracy: 0.9519\n",
            "Epoch 21/50\n",
            "6548/6548 [==============================] - 262s 40ms/step - loss: 0.1185 - accuracy: 0.9600 - val_loss: 0.1300 - val_accuracy: 0.9582\n",
            "\n"
          ]
        }
      ],
      "source": [
        "model=Sequential()\n",
        "model.add(Embedding(vocab_size,embedding_dim,input_length=max_length))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv1D(filters=64, kernel_size=3,activation='relu'))\n",
        "model.add(MaxPooling1D(pool_size=5))\n",
        "model.add(Conv1D(filters=128, kernel_size=3,activation='relu'))\n",
        "model.add(MaxPooling1D(pool_size=3))\n",
        "model.add(LSTM(100,return_sequences=True))\n",
        "model.add(BatchNormalization())\n",
        "model.add(LSTM(10))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(100,activation='relu'))\n",
        "model.add(Dense(50,activation='softmax'))\n",
        "model.compile(loss='categorical_crossentropy',optimizer=\"adam\",metrics=['accuracy'])\n",
        "model.fit(X_train,y_train,batch_size=32,epochs=50,validation_data=(X_test,y_test),callbacks=[callbacks])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "L7xo6tdtTIyr",
        "outputId": "cefb9498-2907-4a60-fd04-be10af8a5b28"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00       730\n",
            "           1       0.89      0.91      0.90       772\n",
            "           2       0.95      0.92      0.93       769\n",
            "           3       0.91      0.89      0.90       719\n",
            "           4       0.95      0.94      0.94       742\n",
            "           5       0.94      0.95      0.94       795\n",
            "           6       0.92      0.88      0.90       724\n",
            "           7       0.97      0.88      0.92       776\n",
            "           8       0.88      0.99      0.93       753\n",
            "           9       0.99      0.79      0.88       795\n",
            "          10       0.82      0.99      0.90       766\n",
            "          11       1.00      0.99      0.99       734\n",
            "          12       1.00      0.98      0.99       718\n",
            "          13       0.99      0.98      0.98       721\n",
            "          14       0.98      0.99      0.99       735\n",
            "          15       0.98      0.98      0.98       755\n",
            "          16       0.98      0.99      0.99       752\n",
            "          17       0.99      0.97      0.98       715\n",
            "          18       1.00      1.00      1.00       756\n",
            "          19       0.99      0.99      0.99       742\n",
            "          20       0.90      0.86      0.88       735\n",
            "          21       0.97      0.99      0.98       713\n",
            "          22       0.98      0.98      0.98       734\n",
            "          23       0.94      0.93      0.93       765\n",
            "          24       0.97      0.99      0.98       697\n",
            "          25       0.91      0.94      0.92       737\n",
            "          26       0.97      0.97      0.97       714\n",
            "          27       0.93      0.97      0.95       697\n",
            "          28       0.85      0.96      0.90       751\n",
            "          29       0.95      0.93      0.94       783\n",
            "          30       1.00      0.99      0.99       764\n",
            "          31       1.00      1.00      1.00       725\n",
            "          32       1.00      1.00      1.00       697\n",
            "          33       0.94      0.91      0.92       740\n",
            "          34       0.99      0.97      0.98       730\n",
            "          35       0.98      0.99      0.99       738\n",
            "          36       0.91      0.94      0.93       752\n",
            "          37       0.99      0.99      0.99       732\n",
            "          38       1.00      0.99      1.00       773\n",
            "          39       0.85      0.89      0.87       691\n",
            "          40       1.00      0.99      0.99       726\n",
            "          41       0.97      0.97      0.97       676\n",
            "          42       0.98      1.00      0.99       771\n",
            "          43       0.96      0.90      0.93       773\n",
            "          44       0.98      0.97      0.98       691\n",
            "          45       0.97      0.97      0.97       723\n",
            "          46       1.00      1.00      1.00       719\n",
            "          47       0.95      0.91      0.93       727\n",
            "          48       0.99      0.99      0.99       740\n",
            "          49       1.00      0.99      0.99       792\n",
            "\n",
            "    accuracy                           0.96     36975\n",
            "   macro avg       0.96      0.96      0.96     36975\n",
            "weighted avg       0.96      0.96      0.96     36975\n",
            "\n"
          ]
        }
      ],
      "source": [
        "y_test_ = np.argmax(y_test, axis=1)\n",
        "y_pred = model.predict(X_test)\n",
        "y_pred_ = np.argmax(y_pred, axis=1)\n",
        "print(classification_report(y_test_,y_pred_))\n",
        "confusion_mc = confusion_matrix(y_test_, y_pred_)\n",
        "df_cm = pd.DataFrame(confusion_mc)\n",
        "plt.figure(figsize=(30,30))\n",
        "sns.heatmap(df_cm, annot=True)\n",
        "plt.title('Accuracy:{0:.3f}'.format(accuracy_score(y_test_,y_pred_)))\n",
        "plt.ylabe='True label'\n",
        "plt.xlabel='Predicted label'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ndgukT7k8Wzv"
      },
      "outputs": [],
      "source": [
        "keywords = [\"alignas\", \"alignof\", \"and\", \"and_eq\", \"asm\", \"atomic_cancel\", \"atomic_commit\", \n",
        "            \"atomic_noexcept\", \"auto\", \"bitand\", \"bitor\", \"bool\", \"break\", \"case\", \"catch\", \n",
        "            \"char\", \"char8_t\", \"char16_t\", \"char32_t\", \"class\", \"compl\", \"concept\", \"const\", \n",
        "            \"consteval\", \"constexpr\", \"constinit\", \"const_cast\", \"continue\", \"co_await\", \n",
        "            \"co_return\", \"co_yield\", \"decltype\", \"default\", \"delete\", \"do\", \"double\", \"dynamic_cast\", \n",
        "            \"else\", \"enum\", \"explicit\", \"export\", \"extern\", \"false\", \"float\", \"for\", \"friend\", \"goto\", \n",
        "            \"if\", \"inline\", \"int\", \"long\", \"mutable\", \"namespace\", \"new\", \"noexcept\", \"not\", \"not_eq\", \n",
        "            \"nullptr\", \"operator\", \"or\", \"or_eq\", \"private\", \"protected\", \"public\", \"reflexpr\", \n",
        "            \"register\", \"reinterpret_cast\", \"requires\", \"return\", \"short\", \"signed\", \"sizeof\", \"static\", \n",
        "            \"static_assert\", \"static_cast\", \"struct\", \"switch\", \"synchronized\", \"template\", \"this\", \n",
        "            \"thread_local\", \"throw\", \"true\", \"try\", \"typedef\", \"typeid\", \"typename\", \"union\", \"unsigned\", \n",
        "            \"using\", \"virtual\", \"void\", \"volatile\", \"wchar_t\", \"while\", \"xor\", \"xor_eq\", \"NULL\"]\n",
        "puncs = '~`!@#$%^&*()-+={[]}|\\\\;:\\'\\\"<,>.?/'\n",
        "puncs = list(puncs)\n",
        "\n",
        "l_funcs = ['StrNCat', 'getaddrinfo', '_ui64toa', 'fclose', 'pthread_mutex_lock', 'gets_s', 'sleep', \n",
        "           '_ui64tot', 'freopen_s', '_ui64tow', 'send', 'lstrcat', 'HMAC_Update', '__fxstat', 'StrCatBuff', \n",
        "           '_mbscat', '_mbstok_s', '_cprintf_s', 'ldap_search_init_page', 'memmove_s', 'ctime_s', 'vswprintf', \n",
        "           'vswprintf_s', '_snwprintf', '_gmtime_s', '_tccpy', '*RC6*', '_mbslwr_s', 'random', \n",
        "           '__wcstof_internal', '_wcslwr_s', '_ctime32_s', 'wcsncat*', 'MD5_Init', '_ultoa', \n",
        "           'snprintf', 'memset', 'syslog', '_vsnprintf_s', 'HeapAlloc', 'pthread_mutex_destroy', \n",
        "           'ChangeWindowMessageFilter', '_ultot', 'crypt_r', '_strupr_s_l', 'LoadLibraryExA', '_strerror_s', \n",
        "           'LoadLibraryExW', 'wvsprintf', 'MoveFileEx', '_strdate_s', 'SHA1', 'sprintfW', 'StrCatNW', \n",
        "           '_scanf_s_l', 'pthread_attr_init', '_wtmpnam_s', 'snscanf', '_sprintf_s_l', 'dlopen', \n",
        "           'sprintfA', 'timed_mutex', 'OemToCharA', 'ldap_delete_ext', 'sethostid', 'popen', 'OemToCharW', \n",
        "           '_gettws', 'vfork', '_wcsnset_s_l', 'sendmsg', '_mbsncat', 'wvnsprintfA', 'HeapFree', '_wcserror_s', \n",
        "           'realloc', '_snprintf*', 'wcstok', '_strncat*', 'StrNCpy', '_wasctime_s', 'push*', '_lfind_s', \n",
        "           'CC_SHA512', 'ldap_compare_ext_s', 'wcscat_s', 'strdup', '_chsize_s', 'sprintf_s', 'CC_MD4_Init', \n",
        "           'wcsncpy', '_wfreopen_s', '_wcsupr_s', '_searchenv_s', 'ldap_modify_ext_s', '_wsplitpath', \n",
        "           'CC_SHA384_Final', 'MD2', 'RtlCopyMemory', 'lstrcatW', 'MD4', 'MD5', '_wcstok_s_l', '_vsnwprintf_s', \n",
        "           'ldap_modify_s', 'strerror', '_lsearch_s', '_mbsnbcat_s', '_wsplitpath_s', 'MD4_Update', '_mbccpy_s', \n",
        "           '_strncpy_s_l', '_snprintf_s', 'CC_SHA512_Init', 'fwscanf_s', '_snwprintf_s', 'CC_SHA1', 'swprintf', \n",
        "           'fprintf', 'EVP_DigestInit_ex', 'strlen', 'SHA1_Init', 'strncat', '_getws_s', 'CC_MD4_Final', \n",
        "           'wnsprintfW', 'lcong48', 'lrand48', 'write', 'HMAC_Init', '_wfopen_s', 'wmemchr', '_tmakepath', \n",
        "           'wnsprintfA', 'lstrcpynW', 'scanf_s', '_mbsncpy_s_l', '_localtime64_s', 'fstream.open', '_wmakepath', \n",
        "           'Connection.open', '_tccat', 'valloc', 'setgroups', 'unlink', 'fstream.put', 'wsprintfA', '*SHA1*', \n",
        "           '_wsearchenv_s', 'ualstrcpyA', 'CC_MD5_Update', 'strerror_s', 'HeapCreate', 'ualstrcpyW', '__xstat', \n",
        "           '_wmktemp_s', 'StrCatChainW', 'ldap_search_st', '_mbstowcs_s_l', 'ldap_modify_ext', '_mbsset_s', \n",
        "           'strncpy_s', 'move', 'execle', 'StrCat', 'xrealloc', 'wcsncpy_s', '_tcsncpy*', 'execlp', \n",
        "           'RIPEMD160_Final', 'ldap_search_s', 'EnterCriticalSection', '_wctomb_s_l', 'fwrite', '_gmtime64_s', \n",
        "           'sscanf_s', 'wcscat', '_strupr_s', 'wcrtomb_s', 'VirtualLock', 'ldap_add_ext_s', '_mbscpy', \n",
        "           '_localtime32_s', 'lstrcpy', '_wcsncpy*', 'CC_SHA1_Init', '_getts', '_wfopen', '__xstat64', \n",
        "           'strcoll', '_fwscanf_s_l', '_mbslwr_s_l', 'RegOpenKey', 'makepath', 'seed48', 'CC_SHA256', \n",
        "           'sendto', 'execv', 'CalculateDigest', 'memchr', '_mbscpy_s', '_strtime_s', 'ldap_search_ext_s', \n",
        "           '_chmod', 'flock', '__fxstat64', '_vsntprintf', 'CC_SHA256_Init', '_itoa_s', '__wcserror_s', \n",
        "           '_gcvt_s', 'fstream.write', 'sprintf', 'recursive_mutex', 'strrchr', 'gethostbyaddr', '_wcsupr_s_l', \n",
        "           'strcspn', 'MD5_Final', 'asprintf', '_wcstombs_s_l', '_tcstok', 'free', 'MD2_Final', 'asctime_s', \n",
        "           '_alloca', '_wputenv_s', '_wcsset_s', '_wcslwr_s_l', 'SHA1_Update', 'filebuf.sputc', 'filebuf.sputn', \n",
        "           'SQLConnect', 'ldap_compare', 'mbstowcs_s', 'HMAC_Final', 'pthread_condattr_init', '_ultow_s', 'rand', \n",
        "           'ofstream.put', 'CC_SHA224_Final', 'lstrcpynA', 'bcopy', 'system', 'CreateFile*', 'wcscpy_s', \n",
        "           '_mbsnbcpy*', 'open', '_vsnwprintf', 'strncpy', 'getopt_long', 'CC_SHA512_Final', '_vsprintf_s_l', \n",
        "           'scanf', 'mkdir', '_localtime_s', '_snprintf', '_mbccpy_s_l', 'memcmp', 'final', '_ultoa_s', \n",
        "           'lstrcpyW', 'LoadModule', '_swprintf_s_l', 'MD5_Update', '_mbsnset_s_l', '_wstrtime_s', '_strnset_s', \n",
        "           'lstrcpyA', '_mbsnbcpy_s', 'mlock', 'IsBadHugeWritePtr', 'copy', '_mbsnbcpy_s_l', 'wnsprintf', \n",
        "           'wcscpy', 'ShellExecute', 'CC_MD4', '_ultow', '_vsnwprintf_s_l', 'lstrcpyn', 'CC_SHA1_Final', \n",
        "           'vsnprintf', '_mbsnbset_s', '_i64tow', 'SHA256_Init', 'wvnsprintf', 'RegCreateKey', 'strtok_s', \n",
        "           '_wctime32_s', '_i64toa', 'CC_MD5_Final', 'wmemcpy', 'WinExec', 'CreateDirectory*', \n",
        "           'CC_SHA256_Update', '_vsnprintf_s_l', 'jrand48', 'wsprintf', 'ldap_rename_ext_s', 'filebuf.open', \n",
        "           '_wsystem', 'SHA256_Update', '_cwscanf_s', 'wsprintfW', '_sntscanf', '_splitpath', 'fscanf_s', \n",
        "           'strpbrk', 'wcstombs_s', 'wscanf', '_mbsnbcat_s_l', 'strcpynA', 'pthread_cond_init', 'wcsrtombs_s', \n",
        "           '_wsopen_s', 'CharToOemBuffA', 'RIPEMD160_Update', '_tscanf', 'HMAC', 'StrCCpy', 'Connection.connect', \n",
        "           'lstrcatn', '_mbstok', '_mbsncpy', 'CC_SHA384_Update', 'create_directories', 'pthread_mutex_unlock', \n",
        "           'CFile.Open', 'connect', '_vswprintf_s_l', '_snscanf_s_l', 'fputc', '_wscanf_s', '_snprintf_s_l', \n",
        "           'strtok', '_strtok_s_l', 'lstrcatA', 'snwscanf', 'pthread_mutex_init', 'fputs', 'CC_SHA384_Init', \n",
        "           '_putenv_s', 'CharToOemBuffW', 'pthread_mutex_trylock', '__wcstoul_internal', '_memccpy', \n",
        "           '_snwprintf_s_l', '_strncpy*', 'wmemset', 'MD4_Init', '*RC4*', 'strcpyW', '_ecvt_s', 'memcpy_s', \n",
        "           'erand48', 'IsBadHugeReadPtr', 'strcpyA', 'HeapReAlloc', 'memcpy', 'ldap_rename_ext', 'fopen_s', \n",
        "           'srandom', '_cgetws_s', '_makepath', 'SHA256_Final', 'remove', '_mbsupr_s', 'pthread_mutexattr_init', \n",
        "           '__wcstold_internal', 'StrCpy', 'ldap_delete', 'wmemmove_s', '_mkdir', 'strcat', '_cscanf_s_l', \n",
        "           'StrCAdd', 'swprintf_s', '_strnset_s_l', 'close', 'ldap_delete_ext_s', 'ldap_modrdn', 'strchr', \n",
        "           '_gmtime32_s', '_ftcscat', 'lstrcatnA', '_tcsncat', 'OemToChar', 'mutex', 'CharToOem', 'strcpy_s', \n",
        "           'lstrcatnW', '_wscanf_s_l', '__lxstat64', 'memalign', 'MD2_Init', 'StrCatBuffW', 'StrCpyN', 'CC_MD5', \n",
        "           'StrCpyA', 'StrCatBuffA', 'StrCpyW', 'tmpnam_r', '_vsnprintf', 'strcatA', 'StrCpyNW', '_mbsnbset_s_l', \n",
        "           'EVP_DigestInit', '_stscanf', 'CC_MD2', '_tcscat', 'StrCpyNA', 'xmalloc', '_tcslen', '*MD4*', \n",
        "           'vasprintf', 'strxfrm', 'chmod', 'ldap_add_ext', 'alloca', '_snscanf_s', 'IsBadWritePtr', 'swscanf_s', \n",
        "           'wmemcpy_s', '_itoa', '_ui64toa_s', 'EVP_DigestUpdate', '__wcstol_internal', '_itow', 'StrNCatW', \n",
        "           'strncat_s', 'ualstrcpy', 'execvp', '_mbccat', 'EVP_MD_CTX_init', 'assert', 'ofstream.write', \n",
        "           'ldap_add', '_sscanf_s_l', 'drand48', 'CharToOemW', 'swscanf', '_itow_s', 'RIPEMD160_Init', \n",
        "           'CopyMemory', 'initstate', 'getpwuid', 'vsprintf', '_fcvt_s', 'CharToOemA', 'setuid', 'malloc', \n",
        "           'StrCatNA', 'strcat_s', 'srand', 'getwd', '_controlfp_s', 'olestrcpy', '__wcstod_internal', \n",
        "           '_mbsnbcat', 'lstrncat', 'des_*', 'CC_SHA224_Init', 'set*', 'vsprintf_s', 'SHA1_Final', '_umask_s', \n",
        "           'gets', 'setstate', 'wvsprintfW', 'LoadLibraryEx', 'ofstream.open', 'calloc', '_mbstrlen', \n",
        "           '_cgets_s', '_sopen_s', 'IsBadStringPtr', 'wcsncat_s', 'add*', 'nrand48', 'create_directory', \n",
        "           'ldap_search_ext', '_i64toa_s', '_ltoa_s', '_cwscanf_s_l', 'wmemcmp', '__lxstat', 'lstrlen', \n",
        "           'pthread_condattr_destroy', '_ftcscpy', 'wcstok_s', '__xmknod', 'pthread_attr_destroy', 'sethostname', \n",
        "           '_fscanf_s_l', 'StrCatN', 'RegEnumKey', '_tcsncpy', 'strcatW', 'AfxLoadLibrary', 'setenv', 'tmpnam', \n",
        "           '_mbsncat_s_l', '_wstrdate_s', '_wctime64_s', '_i64tow_s', 'CC_MD4_Update', 'ldap_add_s', '_umask', \n",
        "           'CC_SHA1_Update', '_wcsset_s_l', '_mbsupr_s_l', 'strstr', '_tsplitpath', 'memmove', '_tcscpy', \n",
        "           'vsnprintf_s', 'strcmp', 'wvnsprintfW', 'tmpfile', 'ldap_modify', '_mbsncat*', 'mrand48', 'sizeof', \n",
        "           'StrCatA', '_ltow_s', '*desencrypt*', 'StrCatW', '_mbccpy', 'CC_MD2_Init', 'RIPEMD160', 'ldap_search', \n",
        "           'CC_SHA224', 'mbsrtowcs_s', 'update', 'ldap_delete_s', 'getnameinfo', '*RC5*', '_wcsncat_s_l', \n",
        "           'DriverManager.getConnection', 'socket', '_cscanf_s', 'ldap_modrdn_s', '_wopen', 'CC_SHA256_Final', \n",
        "           '_snwprintf*', 'MD2_Update', 'strcpy', '_strncat_s_l', 'CC_MD5_Init', 'mbscpy', 'wmemmove', \n",
        "           'LoadLibraryW', '_mbslen', '*alloc', '_mbsncat_s', 'LoadLibraryA', 'fopen', 'StrLen', 'delete', \n",
        "           '_splitpath_s', 'CreateFileTransacted*', 'MD4_Final', '_open', 'CC_SHA384', 'wcslen', 'wcsncat', \n",
        "           '_mktemp_s', 'pthread_mutexattr_destroy', '_snwscanf_s', '_strset_s', '_wcsncpy_s_l', 'CC_MD2_Final', \n",
        "           '_mbstok_s_l', 'wctomb_s', 'MySQL_Driver.connect', '_snwscanf_s_l', '*_des_*', 'LoadLibrary', \n",
        "           '_swscanf_s_l', 'ldap_compare_s', 'ldap_compare_ext', '_strlwr_s', 'GetEnvironmentVariable', \n",
        "           'cuserid', '_mbscat_s', 'strspn', '_mbsncpy_s', 'ldap_modrdn2', 'LeaveCriticalSection', 'CopyFile', \n",
        "           'getpwd', 'sscanf', 'creat', 'RegSetValue', 'ldap_modrdn2_s', 'CFile.Close', '*SHA_1*', \n",
        "           'pthread_cond_destroy', 'CC_SHA512_Update', '*RC2*', 'StrNCatA', '_mbsnbcpy', '_mbsnset_s', \n",
        "           'crypt', 'excel', '_vstprintf', 'xstrdup', 'wvsprintfA', 'getopt', 'mkstemp', '_wcsnset_s', \n",
        "           '_stprintf', '_sntprintf', 'tmpfile_s', 'OpenDocumentFile', '_mbsset_s_l', '_strset_s_l', \n",
        "           '_strlwr_s_l', 'ifstream.open', 'xcalloc', 'StrNCpyA', '_wctime_s', 'CC_SHA224_Update', '_ctime64_s', \n",
        "           'MoveFile', 'chown', 'StrNCpyW', 'IsBadReadPtr', '_ui64tow_s', 'IsBadCodePtr', 'getc', \n",
        "           'OracleCommand.ExecuteOracleScalar', 'AccessDataSource.Insert', 'IDbDataAdapter.FillSchema', \n",
        "           'IDbDataAdapter.Update', 'GetWindowText*', 'SendMessage', 'SqlCommand.ExecuteNonQuery', 'streambuf.sgetc', \n",
        "           'streambuf.sgetn', 'OracleCommand.ExecuteScalar', 'SqlDataSource.Update', '_Read_s', 'IDataAdapter.Fill', \n",
        "           '_wgetenv', '_RecordsetPtr.Open*', 'AccessDataSource.Delete', 'Recordset.Open*', 'filebuf.sbumpc', 'DDX_*', \n",
        "           'RegGetValue', 'fstream.read*', 'SqlCeCommand.ExecuteResultSet', 'SqlCommand.ExecuteXmlReader', 'main', \n",
        "           'streambuf.sputbackc', 'read', 'm_lpCmdLine', 'CRichEditCtrl.Get*', 'istream.putback', \n",
        "           'SqlCeCommand.ExecuteXmlReader', 'SqlCeCommand.BeginExecuteXmlReader', 'filebuf.sgetn', \n",
        "           'OdbcDataAdapter.Update', 'filebuf.sgetc', 'SQLPutData', 'recvfrom', 'OleDbDataAdapter.FillSchema', \n",
        "           'IDataAdapter.FillSchema', 'CRichEditCtrl.GetLine', 'DbDataAdapter.Update', 'SqlCommand.ExecuteReader', \n",
        "           'istream.get', 'ReceiveFrom', '_main', 'fgetc', 'DbDataAdapter.FillSchema', 'kbhit', 'UpdateCommand.Execute*', \n",
        "           'Statement.execute', 'fgets', 'SelectCommand.Execute*', 'getch', 'OdbcCommand.ExecuteNonQuery', \n",
        "           'CDaoQueryDef.Execute', 'fstream.getline', 'ifstream.getline', 'SqlDataAdapter.FillSchema', \n",
        "           'OleDbCommand.ExecuteReader', 'Statement.execute*', 'SqlCeCommand.BeginExecuteNonQuery', \n",
        "           'OdbcCommand.ExecuteScalar', 'SqlCeDataAdapter.Update', 'sendmessage', 'mysqlpp.DBDriver', 'fstream.peek', \n",
        "           'Receive', 'CDaoRecordset.Open', 'OdbcDataAdapter.FillSchema', '_wgetenv_s', 'OleDbDataAdapter.Update', \n",
        "           'readsome', 'SqlCommand.BeginExecuteXmlReader', 'recv', 'ifstream.peek', '_Main', '_tmain', '_Readsome_s', \n",
        "           'SqlCeCommand.ExecuteReader', 'OleDbCommand.ExecuteNonQuery', 'fstream.get', 'IDbCommand.ExecuteScalar', \n",
        "           'filebuf.sputbackc', 'IDataAdapter.Update', 'streambuf.sbumpc', 'InsertCommand.Execute*', 'RegQueryValue', \n",
        "           'IDbCommand.ExecuteReader', 'SqlPipe.ExecuteAndSend', 'Connection.Execute*', 'getdlgtext', 'ReceiveFromEx', \n",
        "           'SqlDataAdapter.Update', 'RegQueryValueEx', 'SQLExecute', 'pread', 'SqlCommand.BeginExecuteReader', 'AfxWinMain', \n",
        "           'getchar', 'istream.getline', 'SqlCeDataAdapter.Fill', 'OleDbDataReader.ExecuteReader', 'SqlDataSource.Insert', \n",
        "           'istream.peek', 'SendMessageCallback', 'ifstream.read*', 'SqlDataSource.Select', 'SqlCommand.ExecuteScalar', \n",
        "           'SqlDataAdapter.Fill', 'SqlCommand.BeginExecuteNonQuery', 'getche', 'SqlCeCommand.BeginExecuteReader', 'getenv', \n",
        "           'streambuf.snextc', 'Command.Execute*', '_CommandPtr.Execute*', 'SendNotifyMessage', 'OdbcDataAdapter.Fill', \n",
        "           'AccessDataSource.Update', 'fscanf', 'QSqlQuery.execBatch', 'DbDataAdapter.Fill', 'cin', \n",
        "           'DeleteCommand.Execute*', 'QSqlQuery.exec', 'PostMessage', 'ifstream.get', 'filebuf.snextc', \n",
        "           'IDbCommand.ExecuteNonQuery', 'Winmain', 'fread', 'getpass', 'GetDlgItemTextCCheckListBox.GetCheck', \n",
        "           'DISP_PROPERTY_EX', 'pread64', 'Socket.Receive*', 'SACommand.Execute*', 'SQLExecDirect', \n",
        "           'SqlCeDataAdapter.FillSchema', 'DISP_FUNCTION', 'OracleCommand.ExecuteNonQuery', 'CEdit.GetLine', \n",
        "           'OdbcCommand.ExecuteReader', 'CEdit.Get*', 'AccessDataSource.Select', 'OracleCommand.ExecuteReader', \n",
        "           'OCIStmtExecute', 'getenv_s', 'DB2Command.Execute*', 'OracleDataAdapter.FillSchema', 'OracleDataAdapter.Fill', \n",
        "           'CComboBox.Get*', 'SqlCeCommand.ExecuteNonQuery', 'OracleCommand.ExecuteOracleNonQuery', 'mysqlpp.Query', \n",
        "           'istream.read*', 'CListBox.GetText', 'SqlCeCommand.ExecuteScalar', 'ifstream.putback', 'readlink', \n",
        "           'CHtmlEditCtrl.GetDHtmlDocument', 'PostThreadMessage', 'CListCtrl.GetItemText', 'OracleDataAdapter.Update', \n",
        "           'OleDbCommand.ExecuteScalar', 'stdin', 'SqlDataSource.Delete', 'OleDbDataAdapter.Fill', 'fstream.putback', \n",
        "           'IDbDataAdapter.Fill', '_wspawnl', 'fwprintf', 'sem_wait', '_unlink', 'ldap_search_ext_sW', 'signal', 'PQclear', \n",
        "           'PQfinish', 'PQexec', 'PQresultStatus']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GSJ2rt5vru9U"
      },
      "outputs": [],
      "source": [
        "def symbolic_tokenize(code):\n",
        "    tokens = nltk.word_tokenize(code)\n",
        "    c_tokens = []\n",
        "    for t in tokens:\n",
        "        if t.strip() != '':\n",
        "            c_tokens.append(t.strip())\n",
        "    f_count = 1\n",
        "    var_count = 1\n",
        "    symbol_table = {}\n",
        "    final_tokens = []\n",
        "    for idx in range(len(c_tokens)):\n",
        "        t = c_tokens[idx]\n",
        "        if t in keywords:\n",
        "            final_tokens.append(t)\n",
        "        elif t in puncs:\n",
        "            final_tokens.append(t)\n",
        "        elif t in l_funcs:\n",
        "            final_tokens.append(t)\n",
        "        elif c_tokens[idx + 1] == '(':\n",
        "            if t in keywords:\n",
        "                final_tokens.append(t + '(')\n",
        "            else:\n",
        "                if t not in symbol_table.keys():\n",
        "                    symbol_table[t] = \"FUNC\" + str(f_count)\n",
        "                    f_count += 1\n",
        "                final_tokens.append(symbol_table[t] + '(')\n",
        "            idx += 1\n",
        "        \n",
        "        elif t.endswith('('):\n",
        "            t = t[:-1]\n",
        "            if t in keywords:\n",
        "                final_tokens.append(t + '(')\n",
        "            else:\n",
        "                if t not in symbol_table.keys():\n",
        "                    symbol_table[t] = \"FUNC\" + str(f_count)\n",
        "                    f_count += 1\n",
        "                final_tokens.append(symbol_table[t] + '(')\n",
        "        elif t.endswith('()'):\n",
        "            t = t[:-2]\n",
        "            if t in keywords:\n",
        "                final_tokens.append(t + '()')\n",
        "            else:\n",
        "                if t not in symbol_table.keys():\n",
        "                    symbol_table[t] = \"FUNC\" + str(f_count)\n",
        "                    f_count += 1\n",
        "                final_tokens.append(symbol_table[t] + '()')\n",
        "        elif re.match(\"^\\\"*\\\"$\", t) is not None:\n",
        "            final_tokens.append(\"STRING\")\n",
        "        elif re.match(\"^[0-9]+(\\.[0-9]+)?$\", t) is not None:\n",
        "            final_tokens.append(\"NUMBER\")\n",
        "        elif re.match(\"^[0-9]*(\\.[0-9]+)$\", t) is not None:\n",
        "            final_tokens.append(\"NUMBER\")\n",
        "        else:\n",
        "            if t not in symbol_table.keys():\n",
        "                symbol_table[t] = \"VAR\" + str(var_count)\n",
        "                var_count += 1\n",
        "            final_tokens.append(symbol_table[t])\n",
        "    return ' '.join(final_tokens)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O6rrBM7eK3Np"
      },
      "source": [
        "### Now we should test our model by take a random source code "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5_qqnw1Y43nn"
      },
      "outputs": [],
      "source": [
        "open_file = open('/content/drive/MyDrive/Junior Colab/Test casses/Dataset_Array_usage_VUL.pkl', \"rb\")\n",
        "source_codes = pickle.load(open_file)\n",
        "open_file.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uY5Wa4zPrzsk"
      },
      "outputs": [],
      "source": [
        "code =  source_codes[random.randint(0,len(source_codes))]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "APKWPxw66gRe",
        "outputId": "ce062f1b-4b54-435e-96ba-8cdc28dcf42f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "void CWE590_Free_Memory_Not_on_Heap__free_wchar_t_declare_32_bad()\n",
            "wchar_t * data ;\n",
            "wchar_t dataBuffer [ 100 ] ;\n",
            "wmemset ( dataBuffer , L 'A' , 100 - 1\n",
            "dataBuffer [ 100 - 1 ] = L '\\0'\n",
            "data = dataBuffer;\n",
            "* dataPtr1 = data;\n",
            "printWLine ( data );\n",
            "void printWLine (const wchar_t * line)\n",
            "if ( line != NULL )\n",
            "wprintf ( L \"%ls\\n\" , line )\n",
            "free ( data );\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(code)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AWU51377vZkT"
      },
      "outputs": [],
      "source": [
        "code_after_cleaning = symbolic_tokenize(code)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GENzCMzSu6_D"
      },
      "outputs": [],
      "source": [
        "sequence = tokenizer.texts_to_sequences([code_after_cleaning])\n",
        "sequence_padded = pad_sequences(sequence,maxlen=max_length, truncating=trunc_type)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZU3E-OodvWK9"
      },
      "outputs": [],
      "source": [
        "y_hat=loaded_Model.predict(sequence_padded)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5YhIpY1nzJ97"
      },
      "outputs": [],
      "source": [
        "index=np.argmax(y_hat)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "n9KwpXLXFyvU",
        "outputId": "3fc06eb8-0a3d-45d1-dd8a-cffe0f054d0d"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'CWE590'"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "label_encoder.classes_[index]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4kd1ocvtLOqe"
      },
      "source": [
        "# Congatulations! 🎉🎉🎉 our model make a good predict "
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "SySeVR_dataset_50_classes.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.8.11"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}