Malware_CNN.ipynb

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  "metadata": {
    "colab": {
      "name": "Malware_CNN.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "-qVZj7_FPi05",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import keras\n",
        "from keras.models import Sequential, Input, Model\n",
        "from keras.layers import Dense, Dropout, Flatten\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "from sklearn.utils import class_weight\n",
        "from sklearn import metrics\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from keras_preprocessing.image import ImageDataGenerator\n",
        "from sklearn.model_selection import train_test_split\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wtloPCLJ37SZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Converter for binaries to .pngs\n",
        "# Place root directory inside the braces\n",
        "\n",
        "## This function allows us to process our hexadecimal files into png images##\n",
        "def convertAndSave(array,name):\n",
        "    print('Processing '+name)\n",
        "    if array.shape[1]!=16: #If not hexadecimal\n",
        "        assert(False)\n",
        "    b=int((array.shape[0]*16)**(0.5))\n",
        "    b=2**(int(log(b)/log(2))+1)\n",
        "    a=int(array.shape[0]*16/b)\n",
        "    array=array[:a*b//16,:]\n",
        "    array=np.reshape(array,(a,b))\n",
        "    im = Image.fromarray(np.uint8(array))\n",
        "    im.save(root+'\\\\'+name+'.png', \"PNG\")\n",
        "    return im\n",
        "\n",
        "#Get the list of files\n",
        "files=os.listdir(root)\n",
        "print('files : ',files)\n",
        "#We will process files one by one.\n",
        "for counter, name in enumerate(files):\n",
        "        #We only process .bytes files from our folder.\n",
        "        if '.bytes' != name[-6:]:\n",
        "            continue\n",
        "        f=open(root+'/'+name)\n",
        "        array=[]\n",
        "        for line in f:\n",
        "            xx=line.split()\n",
        "            if len(xx)!=17:\n",
        "                continue\n",
        "            array.append([int(i,16) if i!='??' else 0 for i in xx[1:] ])\n",
        "        plt.imshow(convertAndSave(np.array(array),name))\n",
        "        del array\n",
        "        f.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DvJ5Z0KWIAY4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_class = 25"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FfuFWMfdQ8wH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "300cdcba-7039-46f7-a9d6-60d2854540b2"
      },
      "source": [
        "DATA_DIR = 'malimg_paper_dataset_imgs/'\n",
        "batches = ImageDataGenerator().flow_from_directory(directory=DATA_DIR,target_size=(64,64),batch_size=10000)\n",
        "imgs,labels = next(batches)\n",
        "\n",
        "X_train,X_test,y_train,y_test = train_test_split(imgs/255.,labels,test_size=0.3)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 9339 images belonging to 25 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sE8ATs9hSJNd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def malware_model():\n",
        "  Malware_model = Sequential()\n",
        "  Malware_model.add(Conv2D(30, kernel_size=(3, 3), activation='relu', input_shape=(64, 64, 3)))\n",
        "  Malware_model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "  Malware_model.add(Conv2D(15, (3,3), activation='relu'))\n",
        "  Malware_model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "  Malware_model.add(Dropout(rate=0.25))\n",
        "  Malware_model.add(Flatten())\n",
        "  Malware_model.add(Dense(128,activation='relu'))\n",
        "  Malware_model.add(Dropout(rate=0.25))\n",
        "  Malware_model.add(Dense(50,activation='relu'))\n",
        "  Malware_model.add(Dense(num_class,activation='softmax'))\n",
        "  Malware_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics = ['accuracy'])\n",
        "  return Malware_model"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fm5JokyMKK7j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "outputId": "8630a1df-2d73-4ef0-b4b1-243b6126de20"
      },
      "source": [
        "Malware_model = malware_model()\n",
        "Malware_model.summary()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_7 (Conv2D)            (None, 62, 62, 30)        840       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_7 (MaxPooling2 (None, 31, 31, 30)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_8 (Conv2D)            (None, 29, 29, 15)        4065      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_8 (MaxPooling2 (None, 14, 14, 15)        0         \n",
            "_________________________________________________________________\n",
            "dropout_7 (Dropout)          (None, 14, 14, 15)        0         \n",
            "_________________________________________________________________\n",
            "flatten_4 (Flatten)          (None, 2940)              0         \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 128)               376448    \n",
            "_________________________________________________________________\n",
            "dropout_8 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 50)                6450      \n",
            "_________________________________________________________________\n",
            "dense_12 (Dense)             (None, 25)                1275      \n",
            "=================================================================\n",
            "Total params: 389,078\n",
            "Trainable params: 389,078\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z-IOkvGuImKi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "outputId": "d3f4c84b-becf-48df-acee-53aba3ccb245"
      },
      "source": [
        "# Ensure that the y_train is not one-hot encoded as the class_weights uses y_train to adjust the weight scheme because of unbalanced classes\n",
        "\n",
        "y_train_new = np.argmax(y_train,axis=1)\n",
        "\n",
        "# Deal with unbalanced data\n",
        "class_weights = class_weight.compute_class_weight('balanced',np.unique(y_train_new),y_train_new)\n",
        "\n",
        "Malware_model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10,  class_weight=class_weights)\n",
        "scores = Malware_model.evaluate(X_test, y_test)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 6537 samples, validate on 2802 samples\n",
            "Epoch 1/10\n",
            "6537/6537 [==============================] - 31s 5ms/step - loss: 1.4229 - accuracy: 0.5659 - val_loss: 0.6029 - val_accuracy: 0.8419\n",
            "Epoch 2/10\n",
            "6537/6537 [==============================] - 31s 5ms/step - loss: 0.4616 - accuracy: 0.8695 - val_loss: 0.2340 - val_accuracy: 0.9254\n",
            "Epoch 3/10\n",
            "6537/6537 [==============================] - 31s 5ms/step - loss: 0.2556 - accuracy: 0.9220 - val_loss: 0.1836 - val_accuracy: 0.9483\n",
            "Epoch 4/10\n",
            "6537/6537 [==============================] - 31s 5ms/step - loss: 0.1929 - accuracy: 0.9408 - val_loss: 0.1441 - val_accuracy: 0.9561\n",
            "Epoch 5/10\n",
            "6537/6537 [==============================] - 31s 5ms/step - loss: 0.1548 - accuracy: 0.9503 - val_loss: 0.1329 - val_accuracy: 0.9590\n",
            "Epoch 6/10\n",
            "6537/6537 [==============================] - 31s 5ms/step - loss: 0.1318 - accuracy: 0.9564 - val_loss: 0.1231 - val_accuracy: 0.9618\n",
            "Epoch 7/10\n",
            "6537/6537 [==============================] - 31s 5ms/step - loss: 0.1201 - accuracy: 0.9616 - val_loss: 0.1197 - val_accuracy: 0.9622\n",
            "Epoch 8/10\n",
            "6537/6537 [==============================] - 31s 5ms/step - loss: 0.1121 - accuracy: 0.9651 - val_loss: 0.1128 - val_accuracy: 0.9643\n",
            "Epoch 9/10\n",
            "6537/6537 [==============================] - 31s 5ms/step - loss: 0.0940 - accuracy: 0.9694 - val_loss: 0.1166 - val_accuracy: 0.9629\n",
            "Epoch 10/10\n",
            "6537/6537 [==============================] - 31s 5ms/step - loss: 0.0892 - accuracy: 0.9711 - val_loss: 0.1148 - val_accuracy: 0.9611\n",
            "2802/2802 [==============================] - 4s 1ms/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hB3w62NXKhfA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2433768b-f205-4ac0-d2a3-f8d478f1b7ae"
      },
      "source": [
        "print('Final CNN accuracy: ', scores[1])"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Final CNN accuracy:  0.9610992074012756\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ayAx7bVWKm9a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = Malware_model.predict_classes(X_test, verbose=0)\n",
        "y_test2 = np.argmax(y_test, axis=1)\n",
        "c_matrix = metrics.confusion_matrix(y_test2, y_pred)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "74Gcrb9LK9BJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def confusion_matrix(confusion_matrix, class_names, figsize = (10,7), fontsize=14):\n",
        "    \"\"\"Prints a confusion matrix, as returned by sklearn.metrics.confusion_matrix, as a heatmap.\n",
        "    \n",
        "    Arguments\n",
        "    ---------\n",
        "    confusion_matrix: numpy.ndarray\n",
        "        The numpy.ndarray object returned from a call to sklearn.metrics.confusion_matrix. \n",
        "        Similarly constructed ndarrays can also be used.\n",
        "    class_names: list\n",
        "        An ordered list of class names, in the order they index the given confusion matrix.\n",
        "    figsize: tuple\n",
        "        A 2-long tuple, the first value determining the horizontal size of the ouputted figure,\n",
        "        the second determining the vertical size. Defaults to (10,7).\n",
        "    fontsize: int\n",
        "        Font size for axes labels. Defaults to 14.\n",
        "    \"\"\"\n",
        "    df_cm = pd.DataFrame(\n",
        "        confusion_matrix, index=class_names, columns=class_names, \n",
        "    )\n",
        "    fig = plt.figure(figsize=figsize)\n",
        "    try:\n",
        "        heatmap = sns.heatmap(df_cm, annot=True, fmt=\"d\")\n",
        "    except ValueError:\n",
        "        raise ValueError(\"Confusion matrix values must be integers.\")\n",
        "    heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=fontsize)\n",
        "    heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=fontsize)\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "63wTr-urLG6l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "outputId": "1392c335-4843-4afa-de25-35e37b2cb7cc"
      },
      "source": [
        "class_names= batches.class_indices.keys()\n",
        "confusion_matrix(c_matrix, class_names, figsize = (20,7), fontsize=14)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}