{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "complete-wealth",
   "metadata": {},
   "outputs": [
    {
     "ename": "ContextualVersionConflict",
     "evalue": "(anyio 2.2.0 (/opt/anaconda3/lib/python3.9/site-packages), Requirement.parse('anyio<4,>=3.0.0'), {'starlette'})",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mContextualVersionConflict\u001b[0m                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/01/vtqqk20n4gq6wxn80d0ly7v80000gn/T/ipykernel_18714/2264163249.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mgradio\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mgr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmath\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msqrt\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/gradio/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgradio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatic\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mButton\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMarkdown\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mcurrent_pkg_version\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpkg_resources\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"gradio\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mversion\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0m__version__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcurrent_pkg_version\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/pkg_resources/__init__.py\u001b[0m in \u001b[0;36mrequire\u001b[0;34m(self, *requirements)\u001b[0m\n\u001b[1;32m    884\u001b[0m         \u001b[0mincluded\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meven\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mwere\u001b[0m \u001b[0malready\u001b[0m \u001b[0mactivated\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mthis\u001b[0m \u001b[0mworking\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    885\u001b[0m         \"\"\"\n\u001b[0;32m--> 886\u001b[0;31m         \u001b[0mneeded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresolve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparse_requirements\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequirements\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    887\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mdist\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mneeded\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/pkg_resources/__init__.py\u001b[0m in \u001b[0;36mresolve\u001b[0;34m(self, requirements, env, installer, replace_conflicting, extras)\u001b[0m\n\u001b[1;32m    775\u001b[0m                 \u001b[0;31m# Oops, the \"best\" so far conflicts with a dependency\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    776\u001b[0m                 \u001b[0mdependent_req\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequired_by\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 777\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mVersionConflict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdependent_req\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    778\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    779\u001b[0m             \u001b[0;31m# push the new requirements onto the stack\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mContextualVersionConflict\u001b[0m: (anyio 2.2.0 (/opt/anaconda3/lib/python3.9/site-packages), Requirement.parse('anyio<4,>=3.0.0'), {'starlette'})"
     ]
    }
   ],
   "source": [
    "import gradio as gr\n",
    "\n",
    "\n",
    "import pandas as pd\n",
    "from math import sqrt;\n",
    "from sklearn import preprocessing\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression;\n",
    "from sklearn.metrics import accuracy_score, r2_score, confusion_matrix, mean_absolute_error, mean_squared_error, f1_score, log_loss\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "import joblib\n",
    " #load packages for ANN\n",
    "import tensorflow as tf\n",
    "    \n",
    "def malware_detection_DL (results, malicious_traffic, benign_traffic):\n",
    "    malicious_dataset = pd.read_csv(malicious_traffic)  #Importing Datasets \n",
    "    benign_dataset = pd.read_csv(benign_traffic)\n",
    "    # Removing duplicated rows from benign_dataset (5380 rows removed)\n",
    "    benign_dataset = benign_dataset[benign_dataset.duplicated(keep=False) == False]\n",
    "    # Combining both datasets together\n",
    "    all_flows = pd.concat([malicious_dataset, benign_dataset])\n",
    "    # Reducing the size of the dataset to reduce the amount of time taken in training models\n",
    "    reduced_dataset = all_flows.sample(38000)\n",
    "    #dataset with columns with nan values dropped\n",
    "    df = reduced_dataset.drop(reduced_dataset.columns[np.isnan(reduced_dataset).any()], axis=1)\n",
    "    #### Isolating independent and dependent variables for training dataset\n",
    "    reduced_y = df['isMalware']\n",
    "    reduced_x = df.drop(['isMalware'], axis=1);\n",
    "    # Splitting datasets into training and test data\n",
    "    x_train, x_test, y_train, y_test = train_test_split(reduced_x, reduced_y, test_size=0.2, random_state=42)\n",
    "   \n",
    "    #scale data between 0 and 1\n",
    "    min_max_scaler = preprocessing.MinMaxScaler()\n",
    "    x_scale = min_max_scaler.fit_transform(reduced_x)\n",
    "    # Splitting datasets into training and test data\n",
    "    x_train, x_test, y_train, y_test = train_test_split(x_scale, reduced_y, test_size=0.2, random_state=42)\n",
    "    #type of layers in ann model is sequential, dense and uses relu activation \n",
    "    ann = tf.keras.models.Sequential()\n",
    "    model = tf.keras.Sequential([\n",
    "        tf.keras.layers.Dense(32, activation ='relu', input_shape=(373,)),\n",
    "        tf.keras.layers.Dense(32, activation = 'relu'),\n",
    "        tf.keras.layers.Dense(1, activation = 'sigmoid'),\n",
    "    ])\n",
    "    \n",
    "    \n",
    "    model.compile(optimizer ='adam', \n",
    "        loss = 'binary_crossentropy',\n",
    "        metrics = ['accuracy'])\n",
    "        #model.fit(x_train, y_train, batch_size=32, epochs = 150, validation_data=(x_test, y_test))\n",
    "        #does not output epochs and gives evalutaion of validation data and history of losses and accuracy\n",
    "    history = model.fit(x_train, y_train, batch_size=32, epochs = 150,verbose=0, validation_data=(x_test, y_test))\n",
    "    _, accuracy = model.evaluate(x_train, y_train)\n",
    "        #return history.history\n",
    "    if results==\"Accuracy\":\n",
    "        #summarize history for accuracy\n",
    "        plt.plot(history.history['accuracy'])\n",
    "        plt.plot(history.history['val_accuracy'])\n",
    "        plt.title('model accuracy')\n",
    "        plt.ylabel('accuracy')\n",
    "        plt.xlabel('epoch')\n",
    "        plt.legend(['train', 'test'], loc='upper left')\n",
    "        return plt.show()\n",
    "    else:\n",
    "        # summarize history for loss\n",
    "        plt.plot(history.history['loss'])\n",
    "        plt.plot(history.history['val_loss'])\n",
    "        plt.title('model loss')\n",
    "        plt.ylabel('loss')\n",
    "        plt.xlabel('epoch')\n",
    "        plt.legend(['train', 'test'], loc='upper left')\n",
    "        return plt.show()\n",
    "    \n",
    "    \n",
    "    \n",
    "iface = gr.Interface(\n",
    "    malware_detection_DL, [gr.inputs.Dropdown([\"Accuracy\",\"Loss\"], label=\"Result Type\"),\n",
    "                                     gr.inputs.Dropdown([\"malicious_flows.csv\"], label = \"Malicious traffic in .csv\"), gr.inputs.Dropdown([\"sample_benign_flows.csv\"], label=\"Benign Traffic in .csv\")\n",
    "                          ], \"plot\",\n",
    "    \n",
    "    \n",
    ")\n",
    "\n",
    "iface.launch()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "curious-detector",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b934bff",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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