Malware Classification with Deep Neural Networks.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SWAMI KARUPPASWAMI THUNNAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout\n",
    "from keras.utils.np_utils import to_categorical\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the dataset\n",
    "dataset = pd.read_csv(\"dataset/dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let us have a look at the head of our dataset\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get independent and dependent variables\n",
    "X = dataset.iloc[:, :-1]\n",
    "y = dataset.iloc[:, -1]\n",
    "y = to_categorical(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Describe our model\n",
    "classifier = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add our layers\n",
    "classifier.add(Dense(output_dim=1000, init=\"uniform\", activation=\"relu\", input_dim=1858))\n",
    "classifier.add(Dropout(0.2))\n",
    "classifier.add(Dense(output_dim=750, activation=\"relu\"))\n",
    "classifier.add(Dropout(0.2))\n",
    "classifier.add(Dense(output_dim=500, activation=\"relu\"))\n",
    "classifier.add(Dropout(0.2))\n",
    "classifier.add(Dense(output_dim=8, activation=\"softmax\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier.compile(optimizer=\"adam\", loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "call_back = keras.callbacks.TensorBoard(log_dir='./event_graph', histogram_freq=0, write_graph=True, write_images=True)\n",
    "history = classifier.fit(X, y, batch_size=10, epochs=150, callbacks=[call_back])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.plot(history.history[\"acc\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.plot(history.history[\"loss\"])\n",
    "\n",
    "classifier.save(\"adam.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Our Machine Learning Models to judge the important features\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Recursive feature elimination\n",
    "from sklearn.feature_selection import RFE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lg = LogisticRegression()\n",
    "dtree = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier_list = [lg, dtree]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "_X = dataset.iloc[:, :-1]\n",
    "_y = dataset.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = LabelEncoder()\n",
    "_y = encoder.fit_transform(_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfe_results = []\n",
    "for classifier in classifier_list:\n",
    "    rfe = RFE(classifier, 20, verbose=1)\n",
    "    fit = rfe.fit(_X, _y)\n",
    "    rfe_results.append(fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "headers = list(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for result in rfe_results:\n",
    "    print(str(result))\n",
    "    for i, j in zip(result.support_, headers):\n",
    "        if i:\n",
    "            print(j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfe_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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