LSTM_DGA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from keras.models import Sequential, load_model\n",
    "from keras.layers.core import Dense, Dropout, Activation\n",
    "from keras.layers import Embedding\n",
    "#from keras.layers.recurrent import LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "legit_Domains = pd.read_csv('DGA/top-1m.csv/top-1m.csv')\n",
    "DGA_domains = "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm (LSTM)                 (None, 64)                23808     \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 64)               256       \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " dense (Dense)               (None, 10)                650       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 24,714\n",
      "Trainable params: 24,586\n",
      "Non-trainable params: 128\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Embedding(maxFeatures, 128, input_length=maxlen))\n",
    "model.add(layers.LSTM(128))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(layers.Dense(1))\n",
    "model.add(Activation('sigmoid'))\n",
    "model.compile(loss = 'binary_crossentropy', optimizer='rmsprop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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