cyber-operations-incidents.ipynb

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   "source": [
    "# A2 - Introdução à Ciência de Dados\n",
    "\n",
    "\n",
    "## Integrantes\n",
    "- Dominique de Azevedo\n",
    "- Gabriel de Melo\n",
    "- Lindsey de Azevedo\n",
    "\n",
    "## Base de Dados\n",
    "\n",
    "Escolhemos a base [Cyber Incidents Up to 2020](#https://www.kaggle.com/fireballbyedimyrnmom/cyber-incidents-up-to-2020).\n",
    "\n",
    "## Perguntas Norteadoras\n",
    "\n",
    "* Quais são os tipos de incidentes mais comuns para cada categoria de alvo?\n",
    "\n",
    "* Quais países mais fazem ataques e quais os tipos de ataque mais frequentes? \n",
    "\n",
    "Bibliotecas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly as pl\n",
    "import plotly.express as px\n",
    "import plotly.offline as py\n",
    "import plotly.graph_objs as go\n",
    "py.init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Limpeza dos dados:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Victims</th>\n",
       "      <th>Sponsor</th>\n",
       "      <th>Type</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2/13/2020</td>\n",
       "      <td>Austrian Foreign Ministry</td>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>Espionage</td>\n",
       "      <td>Government</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/23/2020</td>\n",
       "      <td>Employees of the U.S. government</td>\n",
       "      <td>Korea (Democratic People's Republic of)</td>\n",
       "      <td>Espionage</td>\n",
       "      <td>Government</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4/6/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Data destruction</td>\n",
       "      <td>Private sector</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2/16/2020</td>\n",
       "      <td>Israeli Defense Forces (IDF) soldiers</td>\n",
       "      <td>Palestine, State of</td>\n",
       "      <td>Espionage</td>\n",
       "      <td>Military</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8/10/2020</td>\n",
       "      <td>U.S. government agencies, U.S. companies</td>\n",
       "      <td>Iran (Islamic Republic of)</td>\n",
       "      <td>Espionage</td>\n",
       "      <td>Government, Private sector</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "        Date                                   Victims  \\\n",
       "0  2/13/2020                 Austrian Foreign Ministry   \n",
       "1  1/23/2020          Employees of the U.S. government   \n",
       "2   4/6/2020                                       NaN   \n",
       "3  2/16/2020     Israeli Defense Forces (IDF) soldiers   \n",
       "4  8/10/2020  U.S. government agencies, U.S. companies   \n",
       "\n",
       "                                   Sponsor              Type  \\\n",
       "0                       Russian Federation         Espionage   \n",
       "1  Korea (Democratic People's Republic of)         Espionage   \n",
       "2                                Australia  Data destruction   \n",
       "3                      Palestine, State of         Espionage   \n",
       "4               Iran (Islamic Republic of)         Espionage   \n",
       "\n",
       "                     Category  \n",
       "0                  Government  \n",
       "1                  Government  \n",
       "2              Private sector  \n",
       "3                    Military  \n",
       "4  Government, Private sector  "
      ]
     },
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   "source": [
    "dados = pd.read_csv(\"cyber-operations-incidents.csv\")\n",
    "\n",
    "dado = dados.loc[:,[\"Date\", \"Victims\", \"Sponsor\", \"Type\", \"Category\"]]\n",
    "\n",
    "Data = dado[\"Date\"]\n",
    "Vitima = dado[\"Victims\"]\n",
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    "Tipo = dado[\"Type\"]\n",
    "Categoria = [\"Category\"]\n",
    "\n",
    "dado.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotagem de países que mais causam incidentes cibernéticos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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   "source": [
    "fig = px.histogram(y = dado[\"Sponsor\"], color_discrete_sequence = [\"#451923\"],\n",
    "                   title = \"Quantidade de incidentes cibernéticos causados por país\")\n",
    "fig.show()"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Com esse gráfico podemos visualizar os países que mais efetuam ataques, sendo eles: China, Rússia, Irã e Coréia do Norte, e com isso filtramos esses países para obter mais informações:"
   ]
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   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#tirando os NA\n",
    "\n",
    "dt = dado.dropna(subset = ['Sponsor'])\n",
    "\n",
    "#separando para cada pais\n",
    "\n",
    "data_china = dt[dt['Sponsor'].str.contains('China')]\n",
    "data_iran = dt[dt['Sponsor'].str.contains('Iran')]\n",
    "data_korea = dt[dt['Sponsor'].str.contains('Korea')]\n",
    "data_russian = dt[dt['Sponsor'].str.contains('Russian')]"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Após essa pequena filtragem separamos em 4 novos dataframes para plotá-los separadamente, pois a base não permite fazer essa separação a partir de um único data frame já que existem muitos eventos em que mais de um pais participam."
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tipos de ataques mais frequentes em relação às categorias\n",
    "\n",
    "Separamos as categorias de incidentes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#tirando os NA\n",
    "\n",
    "dt = dado.dropna(subset = ['Category'])\n",
    "\n",
    "#separando para cada Categoria\n",
    "\n",
    "data_cv = dt[dt['Category'].str.contains('Civil')]\n",
    "data_gv = dt[dt['Category'].str.contains('Gover')]\n",
    "data_pv = dt[dt['Category'].str.contains('Private')]\n",
    "data_ml = dt[dt['Category'].str.contains('Military')]\n"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Então analisamos os tipos de ataques mais comuns em cada categoria."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
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   "source": [
    "fig = px.histogram(y = data_pv[\"Type\"], title = \"Tipos de ataques mais comuns na categoria Setor Privado\",\n",
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   ],
   "source": [
    "fig = px.histogram(y = data_ml[\"Type\"], title = \"Tipos de ataques mais comuns na categoria Militar\",\n",
    "                   color_discrete_sequence = [\"#a734a2\"])\n",
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   "source": [
    "Com esses plots fica claro que a maior parte dos eventos são apenas de espionagem, os outros tipos de ataques ocupam uma pequena parcela deles.\n",
    "\n",
    "## Tipos de ataques mais frequentes em relação aos países\n",
    " \n",
    "Observamos também a distribuição de tipos de incidentes em cada um dos países em questão:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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          "3/5/2020",
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   ],
   "source": [
    "trace1 = go.Box(y = data_china.loc[data_china['Type'] == \"Espionage\", \"Date\"],\n",
    "                name = 'Espionagem',\n",
    "                marker = {'color': '#1e90ff'})\n",
    "\n",
    "trace2 = go.Box(y = data_china.loc[data_china['Type'] == \"Sabotage\", \"Date\"],\n",
    "                name = 'Sabotagem',\n",
    "                marker = {'color': '#4169e1'})\n",
    "\n",
    "trace3 = go.Box(y = data_china.loc[data_china['Type'] == \"Denial of service\", \"Date\"],\n",
    "                name = 'Denial of service',\n",
    "                marker = {'color': '#6959cd'})\n",
    "\n",
    "trace4 = go.Box(y = data_china.loc[data_china['Type'] == \"Data destruction\", \"Date\"],\n",
    "                name = 'Destruição de Dados',\n",
    "                marker = {'color': '#483d8b'})\n",
    "\n",
    "data = [trace1, trace2, trace3, trace4]\n",
    "layout = go.Layout(title = 'Dispersão de Tipos de Ataques realizados pela China ao longo dos Anos',\n",
    "                   titlefont = {'family': 'Arial',\n",
    "                                'size': 22,\n",
    "                                'color': '#7f7f7f'},\n",
    "                   xaxis = {'title': 'Tipos'},\n",
    "                   paper_bgcolor = 'rgb(243, 243, 243)',\n",
    "                   plot_bgcolor = 'rgb(243, 243, 243)')\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig)"
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  {
   "cell_type": "code",
   "execution_count": 11,
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     },
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   ],
   "source": [
    "trace1 = go.Box(y = data_russian.loc[data_russian['Type'] == \"Espionage\", \"Date\"],\n",
    "                name = 'Espionagem',\n",
    "                marker = {'color': '#00ff00'})\n",
    "\n",
    "trace2 = go.Box(y = data_russian.loc[data_russian['Type'] == \"Sabotage\", \"Date\"],\n",
    "                name = 'Sabotagem',\n",
    "                marker = {'color': '#7fff00'})\n",
    "\n",
    "trace3 = go.Box(y = data_russian.loc[data_russian['Type'] == \"Denial of service\", \"Date\"],\n",
    "                name = 'Denial of service',\n",
    "                marker = {'color': '#adff2f'})\n",
    "\n",
    "trace4 = go.Box(y = data_russian.loc[data_russian['Type'] == \"Defacement\", \"Date\"],\n",
    "                name = 'Defacement',\n",
    "                marker = {'color': '#ffff00'})\n",
    "\n",
    "trace5 = go.Box(y = data_russian.loc[data_russian['Type'] == \"Data destruction\", \"Date\"],\n",
    "                name = 'Destruição de Dados',\n",
    "                marker = {'color': '#ffd700'})\n",
    "\n",
    "trace6 = go.Box(y = data_russian.loc[data_russian['Type'] == \"Doxing\", \"Date\"],\n",
    "                name = 'Doxing',\n",
    "                marker = {'color': '#ffa500'})\n",
    "\n",
    "\n",
    "data = [trace1, trace2, trace3, trace4, trace5, trace6]\n",
    "layout = go.Layout(title = 'Dispersão de Tipos de Ataques realizados pelo Iran ao longo dos Anos',\n",
    "                   titlefont = {'family': 'Arial',\n",
    "                                'size': 22,\n",
    "                                'color': '#7f7f7f'},\n",
    "                   xaxis = {'title': 'Tipos'},\n",
    "                   paper_bgcolor = 'rgb(243, 243, 243)',\n",
    "                   plot_bgcolor = 'rgb(243, 243, 243)')\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig)"
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   "cell_type": "code",
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          "4/2/2020",
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          "9/11/2019",
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          "10/4/2019",
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   ],
   "source": [
    "trace1 = go.Box(y = data_iran.loc[data_iran['Type'] == \"Espionage\", \"Date\"],\n",
    "                name = 'Espionagem',\n",
    "                marker = {'color': '#40e0d0'})\n",
    "\n",
    "trace2 = go.Box(y = data_iran.loc[data_iran['Type'] == \"Sabotage\", \"Date\"],\n",
    "                name = 'Sabotagem',\n",
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    "\n",
    "trace3 = go.Box(y = data_iran.loc[data_iran['Type'] == \"Denial of service\", \"Date\"],\n",
    "                name = 'Denial of service',\n",
    "                marker = {'color': '#7b68ee'})\n",
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    "trace4 = go.Box(y = data_iran.loc[data_iran['Type'] == \"Defacement\", \"Date\"],\n",
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    "\n",
    "trace5 = go.Box(y = data_iran.loc[data_iran['Type'] == \"Data destruction\", \"Date\"],\n",
    "                name = 'Destruição de Dados',\n",
    "                marker = {'color': '#9400d3'})\n",
    "\n",
    "\n",
    "data = [trace1, trace2, trace3, trace4, trace5]\n",
    "layout = go.Layout(title = 'Dispersão de Tipos de Ataques realizados pelo Irã ao longo dos Anos',\n",
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       "                            \n",
       "var gd = document.getElementById('527dea80-fada-447c-ba62-9ef52b532ae3');\n",
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       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trace1 = go.Box(y = data_korea.loc[data_korea['Type'] == \"Espionage\", \"Date\"],\n",
    "                name = 'Espionagem',\n",
    "                marker = {'color': '#f39c12'})\n",
    "\n",
    "trace2 = go.Box(y = data_korea.loc[data_korea['Type'] == \"Doxing\", \"Date\"],\n",
    "                name = 'Doxing',\n",
    "                marker = {'color': '#e67e22'})\n",
    "\n",
    "trace3 = go.Box(y = data_korea.loc[data_korea['Type'] == \"Denial of service\", \"Date\"],\n",
    "                name = 'Denial of service',\n",
    "                marker = {'color': '#d35400'})\n",
    "\n",
    "trace4 = go.Box(y = data_korea.loc[data_korea['Type'] == \"Financial Theft\", \"Date\"],\n",
    "                name = 'Roubo Financeiro',\n",
    "                marker = {'color': '#e74c3c'})\n",
    "\n",
    "trace5 = go.Box(y = data_korea.loc[data_korea['Type'] == \"Data destruction\", \"Date\"],\n",
    "                name = 'Destruição de Dados',\n",
    "                marker = {'color': '#ff69b4'})\n",
    "\n",
    "data = [trace1, trace2, trace3, trace4, trace5]\n",
    "layout = go.Layout(title = 'Dispersão de Tipos de Ataques realizados pela Coréia do Norte ao longo dos Anos',\n",
    "                   titlefont = {'family': 'Arial',\n",
    "                                'size': 22,\n",
    "                                'color': '#7f7f7f'},\n",
    "                   xaxis = {'title': 'Tipos'},\n",
    "                   paper_bgcolor = 'rgb(243, 243, 243)',\n",
    "                   plot_bgcolor = 'rgb(243, 243, 243)')\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusão\n",
    "\n",
    "Percebemos que, apesar de trocarmos a variável, de categoria para país, o tipo \"Espionagem\" continua sendo o tipo de incidente que mais ocorre, o que indica grande falha no sistema de segurança tecnológica dos países atacados. Também podemos concluir os quatro países que mais causam \"cyber\" ataques, China, Coréia do Norte, Irã e Rússia, o que pode estar relacionado com o grande poder bélico que esses países possuem, mesmo sem relação aparente com o tema dos ataques."
   ]
  }
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