WSensing 2021 - Classification of Denial of Service Attacks on Wi-Fi-based Unmanned Aerial Vehicle.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# WSensing 2021 - Classification of Denial of Service Attacks on Wi-Fi-based Unmanned Aerial Vehicle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import tree, preprocessing, linear_model\n",
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "import graphviz\n",
    "import pydotplus\n",
    "\n",
    "seed = 11\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Protocol</th>\n",
       "      <th>Bytes</th>\n",
       "      <th>TimeDelta</th>\n",
       "      <th>DestPort</th>\n",
       "      <th>SrcPort</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>udp</td>\n",
       "      <td>106</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>14550.0</td>\n",
       "      <td>14551.0</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>udp</td>\n",
       "      <td>106</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>14550.0</td>\n",
       "      <td>14551.0</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>udp</td>\n",
       "      <td>106</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>14550.0</td>\n",
       "      <td>14551.0</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>udp</td>\n",
       "      <td>108</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>14550.0</td>\n",
       "      <td>14551.0</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>udp</td>\n",
       "      <td>106</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>14550.0</td>\n",
       "      <td>14551.0</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  Protocol  Bytes  TimeDelta  DestPort  SrcPort   Class\n",
       "0      udp    106   0.000012   14550.0  14551.0  normal\n",
       "1      udp    106   0.000011   14550.0  14551.0  normal\n",
       "2      udp    106   0.000037   14550.0  14551.0  normal\n",
       "3      udp    108   0.000036   14550.0  14551.0  normal\n",
       "4      udp    106   0.000064   14550.0  14551.0  normal"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"dataset.csv.gz\", header = 0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "normal      459839\n",
       "deauth        8576\n",
       "udpflood      5493\n",
       "dostcp         403\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 474311 entries, 0 to 474310\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count   Dtype  \n",
      "---  ------     --------------   -----  \n",
      " 0   Protocol   474311 non-null  object \n",
      " 1   Bytes      474311 non-null  int64  \n",
      " 2   TimeDelta  474311 non-null  float64\n",
      " 3   DestPort   179837 non-null  float64\n",
      " 4   SrcPort    179837 non-null  float64\n",
      " 5   Class      474311 non-null  object \n",
      "dtypes: float64(3), int64(1), object(2)\n",
      "memory usage: 21.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tcp     213259\n",
       "udp     179837\n",
       "wlan     81215\n",
       "Name: Protocol, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Protocol'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Protocol</th>\n",
       "      <th>Bytes</th>\n",
       "      <th>TimeDelta</th>\n",
       "      <th>DestPort</th>\n",
       "      <th>SrcPort</th>\n",
       "      <th>Class</th>\n",
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       "      <td>0</td>\n",
       "      <td>106</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>106</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>14550.0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>106</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>14550.0</td>\n",
       "      <td>14551.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>108</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>14550.0</td>\n",
       "      <td>14551.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>106</td>\n",
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       "      <td>14550.0</td>\n",
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      "text/plain": [
       "   Protocol  Bytes  TimeDelta  DestPort  SrcPort  Class\n",
       "0         0    106   0.000012   14550.0  14551.0      0\n",
       "1         0    106   0.000011   14550.0  14551.0      0\n",
       "2         0    106   0.000037   14550.0  14551.0      0\n",
       "3         0    108   0.000036   14550.0  14551.0      0\n",
       "4         0    106   0.000064   14550.0  14551.0      0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'udp': 0, 'tcp': 1, 'wlan': 2}\n",
    "df['Protocol'] = df['Protocol'].map(d)\n",
    "\n",
    "d = {'normal': 0, 'deauth': 1, 'dostcp': 1, 'udpflood': 1}\n",
    "df['Class'] = df['Class'].map(d)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applicable features for Learning Task\n",
    "> Just `Bytes` and `TimeDelta`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Bytes', 'TimeDelta'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = list(df.columns[1:3])\n",
    "df['TimeDelta'] = df['TimeDelta'].astype(np.float64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_names = ['Normal', 'DoS']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stratified Train-test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df[\"Class\"]\n",
    "X = df[features]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,stratify=y,test_size=0.4, random_state=seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = tree.DecisionTreeClassifier(max_depth = 3, random_state=seed)\n",
    "clf = clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Interpretability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "<!-- 2&#45;&gt;4 -->\r\n",
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       "</svg>\r\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x214346537c0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import graphviz\n",
    "# DOT data\n",
    "dot_data = tree.export_graphviz(clf, out_file=None, \n",
    "                                feature_names=features,  \n",
    "                                class_names=target_names,\n",
    "                               )\n",
    "# Draw graph\n",
    "graph = graphviz.Source(dot_data, format=\"pdf\") \n",
    "graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_graph = pydotplus.graph_from_dot_data(dot_data) \n",
    "out_graph.write_pdf('dt.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tree Classifier Accuracy: \n",
      "0.9979760179206747\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      Normal       1.00      1.00      1.00    183936\n",
      "         DoS       0.94      0.99      0.97      5789\n",
      "\n",
      "    accuracy                           1.00    189725\n",
      "   macro avg       0.97      1.00      0.98    189725\n",
      "weighted avg       1.00      1.00      1.00    189725\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print (\"Tree Classifier Accuracy: \")\n",
    "print (clf.score(X_test,y_test))\n",
    "y_pred_dt = clf.predict(X_test)\n",
    "print(classification_report(y_test, y_pred_dt, target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred_dt)\n",
    "tn, fp, fn, tp = confusion_matrix(y_test, y_pred_dt).ravel()\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=target_names)\n",
    "\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.002 0.0002\n"
     ]
    }
   ],
   "source": [
    "n = y_pred_dt.shape[0]\n",
    "print(round(fp/n,3), round(fn/n,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=11)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR = linear_model.LogisticRegression(random_state=seed)\n",
    "LR.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Interpretability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ -0.07862087, -12.13391404]]), array([5.44717146]))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR.coef_, LR.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Classifier Accuracy: \n",
      "0.982010805112663\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      Normal       0.99      0.99      0.99    183936\n",
      "         DoS       0.76      0.59      0.67      5789\n",
      "\n",
      "    accuracy                           0.98    189725\n",
      "   macro avg       0.88      0.79      0.83    189725\n",
      "weighted avg       0.98      0.98      0.98    189725\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print (\"Logistic Regression Classifier Accuracy: \")\n",
    "print (LR.score(X_test,y_test))\n",
    "\n",
    "y_pred_lr = LR.predict(X_test)\n",
    "print(classification_report(y_test, y_pred_lr,target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred_lr, labels=LR.classes_)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=target_names)\n",
    "tn_lr, fp_lr, fn_lr, tp_lr = confusion_matrix(y_test, y_pred_lr).ravel()\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = y_pred_lr.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.006 0.012\n"
     ]
    }
   ],
   "source": [
    "print(round(fp_lr/n,3), round(fn_lr/n,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "---\n",
    "# Prediction Inference Latency\n",
    "> This Inference Latency calculation is for all Test subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.59 ms ± 178 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit LR.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.75 ms ± 427 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of 189725 samples infered.\n"
     ]
    }
   ],
   "source": [
    "print(\"Total of {} samples infered.\".format(X_test.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "venv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}