initial_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SIGINT Analysis in Python\n",
    "\n",
    "*Tags: Inteliigence, NLP, Signals, COMINT, ELINT, Radar*\n",
    "\n",
    "## Contents\n",
    "1. Data Overview and Exploration\n",
    "    - What type of signals did we receive?\n",
    "    - What source and targets are most active?\n",
    "2. Identify Entities\n",
    "    - What entities are communicating\n",
    "    - What is the relationship between entities\n",
    "3. Message Prioritization\n",
    "    - What is the sentiment of each message\n",
    "    - Do any keywords exist in messages\n",
    "    - Do any strange patterns exist\n",
    "4. Concluding Analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Signal ID</th>\n",
       "      <th>Signal Type</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Modulation</th>\n",
       "      <th>Encryption</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S1</td>\n",
       "      <td>COMINT</td>\n",
       "      <td>2.4 GHz</td>\n",
       "      <td>FM</td>\n",
       "      <td>RSA</td>\n",
       "      <td>A1</td>\n",
       "      <td>A2</td>\n",
       "      <td>Alpha Two sitrep?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S2</td>\n",
       "      <td>ELINT</td>\n",
       "      <td>10 GHz</td>\n",
       "      <td>AM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R1</td>\n",
       "      <td>-</td>\n",
       "      <td>Radar pulse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S3</td>\n",
       "      <td>COMINT</td>\n",
       "      <td>5 GHz</td>\n",
       "      <td>QPSK</td>\n",
       "      <td>AES</td>\n",
       "      <td>B1</td>\n",
       "      <td>B2</td>\n",
       "      <td>Bravo One to Bravo Two, we have a target in sight.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S4</td>\n",
       "      <td>ELINT</td>\n",
       "      <td>15 GHz</td>\n",
       "      <td>FSK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R2</td>\n",
       "      <td>-</td>\n",
       "      <td>Radar sweep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S5</td>\n",
       "      <td>COMINT</td>\n",
       "      <td>3 GHz</td>\n",
       "      <td>PSK</td>\n",
       "      <td>RSA</td>\n",
       "      <td>C1</td>\n",
       "      <td>A1</td>\n",
       "      <td>Charlie One to Alpha One, we are under attack!</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Signal ID Signal Type Frequency Modulation Encryption Source Destination  \\\n",
       "0        S1      COMINT   2.4 GHz         FM        RSA     A1          A2   \n",
       "1        S2       ELINT    10 GHz         AM        NaN     R1           -   \n",
       "2        S3      COMINT     5 GHz       QPSK        AES     B1          B2   \n",
       "3        S4       ELINT    15 GHz        FSK        NaN     R2           -   \n",
       "4        S5      COMINT     3 GHz        PSK        RSA     C1          A1   \n",
       "\n",
       "                                              Content  \n",
       "0                                   Alpha Two sitrep?  \n",
       "1                                         Radar pulse  \n",
       "2  Bravo One to Bravo Two, we have a target in sight.  \n",
       "3                                         Radar sweep  \n",
       "4      Charlie One to Alpha One, we are under attack!  "
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\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>Signal ID</th>\n",
       "      <th>Signal Type</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Modulation</th>\n",
       "      <th>Encryption</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>14</td>\n",
       "      <td>36</td>\n",
       "      <td>22</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>S1</td>\n",
       "      <td>COMINT</td>\n",
       "      <td>2.4 GHz</td>\n",
       "      <td>QAM</td>\n",
       "      <td>DES</td>\n",
       "      <td>A1</td>\n",
       "      <td>A1</td>\n",
       "      <td>Radar chirp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Signal ID Signal Type Frequency Modulation Encryption Source  \\\n",
       "count         36          36        36         36         14     36   \n",
       "unique        36           2        21         10          5     22   \n",
       "top           S1      COMINT   2.4 GHz        QAM        DES     A1   \n",
       "freq           1          19         7          8          5      3   \n",
       "\n",
       "       Destination      Content  \n",
       "count           22           36  \n",
       "unique           9           30  \n",
       "top             A1  Radar chirp  \n",
       "freq             6            3  "
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 36 entries, 0 to 35\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   Signal ID    36 non-null     object\n",
      " 1   Signal Type  36 non-null     object\n",
      " 2   Frequency    36 non-null     object\n",
      " 3   Modulation   36 non-null     object\n",
      " 4   Encryption   14 non-null     object\n",
      " 5   Source       36 non-null     object\n",
      " 6   Destination  22 non-null     object\n",
      " 7   Content      36 non-null     object\n",
      "dtypes: object(8)\n",
      "memory usage: 2.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above summaries we can see there are a few attributes that have null entries. We know this because we have 35 rows total and some values are less than 35. \n",
    "\n",
    "We can make our data more readable by replacing some nulls with domain friendly language. For example, we can replace null values in the Encryption column with the value \"Unecnrypted\" or \"Unknown\".\n",
    "\n",
    "*Note: that for some data nulls are appropriate. Destination being null is valid is the source is not transmitting a message to anyone but just creating a signal based on an action.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data prep\n",
    "\n",
    "#replace NaN values in Encryption column with the value 'Unencrypted'\n",
    "df.Encryption = df['Encryption'].fillna(\"Unencrypted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the signal types and encryption types present in the data.\n",
    "fig, axes = plt.subplots(nrows=3,ncols=1, figsize=(8,12))\n",
    "\n",
    "# get count by Signal Type\n",
    "sns.countplot(data = df, x='Signal Type', ax=axes[0])\n",
    "\n",
    "# get count by Modulation\n",
    "sns.countplot(data = df, x='Modulation', ax=axes[1])\n",
    "\n",
    "# get count by Encryption type\n",
    "sns.countplot(data = df, x='Encryption', ax=axes[2])\n",
    "\n",
    "plt.subplots_adjust(wspace=0.6, hspace=0.6)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis:\n",
    "- About half our signals are COMINT messages and half are ELINT\n",
    "- There is a wide range of modulation types but QAM makes up the majority followed by FM\n",
    "- Most signals are unencrypted while DES is the most common encryption. There is also one MD5 encrypted message.\n",
    "\n",
    "# Entity Identification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What sources are most active.\n",
    "fig = plt.figure(figsize=(10,4))\n",
    "\n",
    "axes = sns.countplot(data=df, x='Source', hue='Signal Type')\n",
    "axes.set_title('Count by Source')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plot above we can see that all ELINT signals are from sources that start with the letter R. All our COMINT messages are from sources labeled A, B, C, D, G, and J. \n",
    "\n",
    "This may be useful later. \n",
    "\n",
    "Additionally, there are more messages from source A1, G1, and G2. R7 also emits the most signals from ELINT messages.\n",
    "\n",
    "G1 and G2 have the most messages indicating there may be some importance to G sources. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "source_count_df = df.groupby(['Signal Type', 'Source'])['Source'].count(). \\\n",
    "    reset_index(name='Signal Count'). \\\n",
    "    sort_values(by=['Signal Type', 'Signal Count'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the sources by activity and signal type.\n",
    "fig, axes = plt.subplots(nrows=1,ncols=2, figsize=(14,4))\n",
    "\n",
    "# get count for COMINT\n",
    "sns.barplot(data = source_count_df[source_count_df['Signal Type']=='COMINT'], x='Source', y='Signal Count',color = 'skyblue', ax=axes[0])\n",
    "\n",
    "# get count for ELINT\n",
    "sns.barplot(data = source_count_df[source_count_df['Signal Type']=='ELINT'], x='Source', y='Signal Count', color='orange', ax=axes[1])\n",
    "\n",
    "plt.subplots_adjust(wspace=0.2, hspace=0.2)\n",
    "\n",
    "axes[0].set_title(\"COMINT Message Count by Source\")\n",
    "axes[1].set_title(\"ELINT Message Count by Source\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the Target/Destination column next."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['A2', '-', 'B2', 'A1', 'C1', 'D1', nan, 'G2', 'J2', 'G1'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Destination'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "None of the R* sources send signals to another entity. If we look at our ELINT signals message content, we can see that these rows are all radar noises. This is useful but there is little more to analyze. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1                          Radar pulse\n",
       "3                          Radar sweep\n",
       "5                          Radar burst\n",
       "9              Radar frequency hopping\n",
       "11                         Radar chirp\n",
       "13                 Radar doppler shift\n",
       "14                         Radar burst\n",
       "15                         Radar chirp\n",
       "16               Radar bistatic signal\n",
       "18    Radar inverse synthetic aperture\n",
       "19             Radar passive detection\n",
       "20              Radar active detection\n",
       "22                  Radar noise signal\n",
       "27                  Radar noise signal\n",
       "28                         Radar chirp\n",
       "33             Radar passive detection\n",
       "34                 Radar doppler shift\n",
       "Name: Content, dtype: object"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Signal Type'] == 'ELINT'].Content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rest of this analysis will focus on the COMINT signals only. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>Signal ID</th>\n",
       "      <th>Signal Type</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Modulation</th>\n",
       "      <th>Encryption</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S1</td>\n",
       "      <td>COMINT</td>\n",
       "      <td>2.4 GHz</td>\n",
       "      <td>FM</td>\n",
       "      <td>RSA</td>\n",
       "      <td>A1</td>\n",
       "      <td>A2</td>\n",
       "      <td>Alpha Two sitrep?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S3</td>\n",
       "      <td>COMINT</td>\n",
       "      <td>5 GHz</td>\n",
       "      <td>QPSK</td>\n",
       "      <td>AES</td>\n",
       "      <td>B1</td>\n",
       "      <td>B2</td>\n",
       "      <td>Bravo One to Bravo Two, we have a target in sight.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S5</td>\n",
       "      <td>COMINT</td>\n",
       "      <td>3 GHz</td>\n",
       "      <td>PSK</td>\n",
       "      <td>RSA</td>\n",
       "      <td>C1</td>\n",
       "      <td>A1</td>\n",
       "      <td>Charlie One to Alpha One, we are under attack!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>S7</td>\n",
       "      <td>COMINT</td>\n",
       "      <td>4 GHz</td>\n",
       "      <td>OOK</td>\n",
       "      <td>DES</td>\n",
       "      <td>D1</td>\n",
       "      <td>C1</td>\n",
       "      <td>Delta One to Charlie One, moving to your position. ETA 5 minutes.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>S8</td>\n",
       "      <td>COMINT</td>\n",
       "      <td>4 GHz</td>\n",
       "      <td>QAM</td>\n",
       "      <td>Unencrypted</td>\n",
       "      <td>D1</td>\n",
       "      <td>A1</td>\n",
       "      <td>Alpha One, Delta One is moving to support Charlie one.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Signal ID Signal Type Frequency Modulation   Encryption Source Destination  \\\n",
       "0        S1      COMINT   2.4 GHz         FM          RSA     A1          A2   \n",
       "2        S3      COMINT     5 GHz       QPSK          AES     B1          B2   \n",
       "4        S5      COMINT     3 GHz        PSK          RSA     C1          A1   \n",
       "6        S7      COMINT     4 GHz        OOK          DES     D1          C1   \n",
       "7        S8      COMINT     4 GHz        QAM  Unencrypted     D1          A1   \n",
       "\n",
       "                                                             Content  \n",
       "0                                                  Alpha Two sitrep?  \n",
       "2                 Bravo One to Bravo Two, we have a target in sight.  \n",
       "4                     Charlie One to Alpha One, we are under attack!  \n",
       "6  Delta One to Charlie One, moving to your position. ETA 5 minutes.  \n",
       "7             Alpha One, Delta One is moving to support Charlie one.  "
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create dataframe without radar noise\n",
    "comint_df = df[df['Signal Type'] == 'COMINT']\n",
    "comint_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the relationship between Sources and Destinations for the messages. This is a good use case for a network visual. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot visual showing relationship between sources and destinations\n",
    "plt.figure(figsize=(9,9))\n",
    "\n",
    "#create graph object\n",
    "graph = nx.from_pandas_edgelist(comint_df,\n",
    "                                 'Source',\n",
    "                                   'Destination',\n",
    "                                   create_using=nx.MultiDiGraph)\n",
    "\n",
    "\n",
    "#node sizes (a destination node will be larger if it received more signals)\n",
    "degree = dict(graph.degree)\n",
    "node_size = [v * 250 for v in degree.values()]\n",
    "\n",
    "# Create a layout for the nodes \n",
    "pos = nx.spring_layout(graph, k=1.0)  # Change 'k' to space out the nodes more\n",
    "pos = nx.shell_layout(graph)\n",
    "pos = nx.circular_layout(graph)\n",
    "\n",
    "\n",
    "\n",
    "#plot graph\n",
    "nx.draw(G=graph, pos=pos,with_labels=True,\n",
    "         node_color='skyblue', node_size=node_size,\n",
    "           edge_color='k', arrowsize=15, arrowstyle='-|>')\n",
    "\n",
    "plt.title(\"Source to Target Network\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the traffic converges on A1. When we look at the actual message content we can see that this entity is called \"Alpha One\". Due to the high number of in-bound messages we can speculate that Alpha One is a command unit. \n",
    "\n",
    "Another point to note is J (Juliet) units and B (Bravo) units do not communicate with the larger org and may be independent of the larger unit comprised of Alpha, Golf, Delta, and Charlie units. \n",
    "\n",
    "Golf Two received alot of communications and communicated back with Golf One. It may be worth deeper analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\grant\\AppData\\Local\\Temp\\ipykernel_2492\\172734353.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  comint_df.loc[:, 'Interaction'] = comint_df['Source'] + comint_df['Destination']\n"
     ]
    }
   ],
   "source": [
    "comint_df.loc[:, 'Interaction'] = comint_df['Source'] + comint_df['Destination']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "comint_df.Interaction.value_counts().plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>G1</td>\n",
       "      <td>G2</td>\n",
       "      <td>Golf One to Golf Two, we have intercepted a hostile signal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>G2</td>\n",
       "      <td>G1</td>\n",
       "      <td>Roger, send for interpretation.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>G1</td>\n",
       "      <td>G2</td>\n",
       "      <td>Transmitting now over digital. Acknowledge when received.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>G1</td>\n",
       "      <td>G2</td>\n",
       "      <td>e0df43a4bf3faaab65ed44d0b07a3ce48bc247aebf1796ab61f71085f42362ad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>G2</td>\n",
       "      <td>G1</td>\n",
       "      <td>Acknowledge Golf One. Message received. Golf two out.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>G2</td>\n",
       "      <td>A1</td>\n",
       "      <td>Alpha One, be advised hostile communications indicate a major offensive is planned in your AO. Advise you increase local security checks and advis...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>A1</td>\n",
       "      <td>G2</td>\n",
       "      <td>Understood Alpha One. Recalling Alpha Two to base.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Source Destination  \\\n",
       "12     G1          G2   \n",
       "23     G2          G1   \n",
       "24     G1          G2   \n",
       "25     G1          G2   \n",
       "26     G2          G1   \n",
       "29     G2          A1   \n",
       "30     A1          G2   \n",
       "\n",
       "                                                                                                                                                  Content  \n",
       "12                                                                                            Golf One to Golf Two, we have intercepted a hostile signal.  \n",
       "23                                                                                                                        Roger, send for interpretation.  \n",
       "24                                                                                              Transmitting now over digital. Acknowledge when received.  \n",
       "25                                                                                       e0df43a4bf3faaab65ed44d0b07a3ce48bc247aebf1796ab61f71085f42362ad  \n",
       "26                                                                                                  Acknowledge Golf One. Message received. Golf two out.  \n",
       "29  Alpha One, be advised hostile communications indicate a major offensive is planned in your AO. Advise you increase local security checks and advis...  \n",
       "30                                                                                                     Understood Alpha One. Recalling Alpha Two to base.  "
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_colwidth', 150)\n",
    "\n",
    "comint_df[(comint_df.Source == 'G2') | (comint_df.Destination == 'G2')][['Source', 'Destination', 'Content']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis:\n",
    "- Golf one and two appear to be a SIGINT unit that is listening for hostile signals \n",
    "    - Golf One may be the technical unit actually intercepting and listening for signals\n",
    "    - Golf Two appears to be the analysis unit that extracts valuable intel from the interepted signal\n",
    "- After intercepting a hostile signal, Golf One sends the signal to Golf Two for analysis\n",
    "- Golf Two decodes the message and relays the intel back to command at Alpha One.\n",
    "- The encrypted hostile message is included in S25.\n",
    "\n",
    "Now we know which units are listening. This could be useful for counterintelligence operations.\n",
    "\n",
    "# Message Prioritization\n",
    "\n",
    "Currently the messages are given in the order we intercepted them. While this is useful and provides some chronological flow, without timestamps it's hard to know the exact order of events. Let's assume these messages are in chornological order. \n",
    "\n",
    "What we really care about is what messages are important. When a set of signals comes in, it would be useful to have them classified by importance and then put those messages at the top of our queue for immediate analysis. \n",
    "\n",
    "We can try to accomplish this with natural language processing using sentiment and keyword recognition. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package names to\n",
      "[nltk_data]     C:\\Users\\grant\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package names is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\grant\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package state_union to\n",
      "[nltk_data]     C:\\Users\\grant\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package state_union is already up-to-date!\n",
      "[nltk_data] Downloading package twitter_samples to\n",
      "[nltk_data]     C:\\Users\\grant\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package twitter_samples is already up-to-date!\n",
      "[nltk_data] Downloading package movie_reviews to\n",
      "[nltk_data]     C:\\Users\\grant\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package movie_reviews is already up-to-date!\n",
      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
      "[nltk_data]     C:\\Users\\grant\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
      "[nltk_data]       date!\n",
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     C:\\Users\\grant\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\grant\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.sentiment import SentimentIntensityAnalyzer \n",
    "import nltk\n",
    "\n",
    "nltk.download([\n",
    "     \"names\",\n",
    "     \"stopwords\",\n",
    "     \"state_union\",\n",
    "     \"twitter_samples\",\n",
    "     \"movie_reviews\",\n",
    "     \"averaged_perceptron_tagger\",\n",
    "     \"vader_lexicon\",\n",
    "     \"punkt\",\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentiment_analyzer = SentimentIntensityAnalyzer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alpha Two sitrep? ---- 0.0\n",
      "Bravo One to Bravo Two, we have a target in sight. ---- 0.0\n",
      "Charlie One to Alpha One, we are under attack! ---- -0.5255\n",
      "Delta One to Charlie One, moving to your position. ETA 5 minutes. ---- 0.0\n",
      "Alpha One, Delta One is moving to support Charlie one. ---- 0.4019\n",
      "Delta Two to Delta One, we have secured the area North of Charile One. ---- 0.4019\n",
      "Alpha one, alpha two is proceeding to checkpoint 1. ---- 0.0\n",
      "Golf One to Golf Two, we have intercepted a hostile signal. ---- -0.3818\n",
      "Juliet One to Juliet Two, radio check? ---- 0.0\n",
      "Charlie One to Alpha One area now secure. Significant hostile casualties and damage to local facilities. Requesting medical and engineering support to our position. ---- 0.0258\n",
      "Roger, send for interpretation. ---- 0.0\n",
      "Transmitting now over digital. Acknowledge when received. ---- 0.0\n",
      "e0df43a4bf3faaab65ed44d0b07a3ce48bc247aebf1796ab61f71085f42362ad ---- 0.0\n",
      "Acknowledge Golf One. Message received. Golf two out. ---- 0.0\n",
      "Alpha One, be advised hostile communications indicate a major offensive is planned in your AO. Advise you increase local security checks and advise non-essential personal to shelter in place. ---- -0.2263\n",
      "Understood Alpha One. Recalling Alpha Two to base. ---- 0.0\n",
      "Alpha Two, return to base immediately to assist with security efforts. ---- 0.34\n",
      "Acknowledge Alpha One. Alpha Two returning to base. ---- 0.0\n",
      "Operatives in the area. Today's passphrase is 'ski patrol'. ---- 0.0\n"
     ]
    }
   ],
   "source": [
    "sentiment_scores = []\n",
    "for message in comint_df.Content:\n",
    "    score = sentiment_analyzer.polarity_scores(message)[\"compound\"]\n",
    "    print(f'{message} ---- {score}')\n",
    "    sentiment_scores.append(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the pre-built sentiment analyzer we don't get much useful information. The longer messages are given a rating other than neutral but this isn't super helpful. \n",
    "\n",
    "One thing we could do is scrape other more specific datasources such as 911 transcripts and build our own corpus that would do a better job of identifying message sentiment. \n",
    "\n",
    "However for simplicity sake, we can try keyword identification which will give us more control over what is flagged. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create keyword list\n",
    "keywords = [\n",
    "    \"hostile\",\n",
    "    \"hostiles\",\n",
    "    \"attack\",\n",
    "    \"help\",\n",
    "    \"emergency\",\n",
    "    \"enemy\",\n",
    "    \"KIA\",\n",
    "    \"urgent\",\n",
    "    \"priority\",\n",
    "    \"wounded\",\n",
    "    \"injury\",\n",
    "    \"injuries\",\n",
    "    \"casualties\",\n",
    "    \"intelligence\",\n",
    "    \"signal\",\n",
    "    \"offensive\",\n",
    "    \"medical\",\n",
    "    \"security\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keyword(s) found in signal S5: 'Charlie One to Alpha One, we are under attack!'\n",
      "Keyword(s) found in signal S13: 'Golf One to Golf Two, we have intercepted a hostile signal.'\n",
      "Keyword(s) found in signal S22: 'Charlie One to Alpha One area now secure. Significant hostile casualties and damage to local facilities. Requesting medical and engineering support to our position.'\n",
      "Keyword(s) found in signal S30: 'Alpha One, be advised hostile communications indicate a major offensive is planned in your AO. Advise you increase local security checks and advise non-essential personal to shelter in place.'\n",
      "Keyword(s) found in signal S32: 'Alpha Two, return to base immediately to assist with security efforts.'\n"
     ]
    }
   ],
   "source": [
    "keyword_analysis_counts = dict.fromkeys(comint_df['Signal ID'], 0)\n",
    "\n",
    "for index ,signal in comint_df.iterrows():\n",
    "    tokens = nltk.word_tokenize(signal['Content'])\n",
    "    if any(keyword in tokens for keyword in keywords):\n",
    "        print(f\"Keyword(s) found in signal {signal['Signal ID']}: '{signal['Content']}'\")\n",
    "        for i in tokens:\n",
    "            if i in keywords:\n",
    "                keyword_analysis_counts[signal['Signal ID']] += 1\n",
    "\n",
    "#add column to dataframe\n",
    "comint_df = comint_df.assign(Priority = keyword_analysis_counts.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Interaction</th>\n",
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       "      <td>C1A1</td>\n",
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       "      <td>G2A1</td>\n",
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       "      <th>12</th>\n",
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       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>31</th>\n",
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      "text/plain": [
       "   Interaction  \\\n",
       "21        C1A1   \n",
       "29        G2A1   \n",
       "12        G1G2   \n",
       "4         C1A1   \n",
       "31        A1A2   \n",
       "\n",
       "                                                                                                                                                  Content  \\\n",
       "21  Charlie One to Alpha One area now secure. Significant hostile casualties and damage to local facilities. Requesting medical and engineering suppor...   \n",
       "29  Alpha One, be advised hostile communications indicate a major offensive is planned in your AO. Advise you increase local security checks and advis...   \n",
       "12                                                                                            Golf One to Golf Two, we have intercepted a hostile signal.   \n",
       "4                                                                                                          Charlie One to Alpha One, we are under attack!   \n",
       "31                                                                                 Alpha Two, return to base immediately to assist with security efforts.   \n",
       "\n",
       "    Priority  \n",
       "21         3  \n",
       "29         3  \n",
       "12         2  \n",
       "4          1  \n",
       "31         1  "
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comint_df[comint_df.Priority > 0].sort_values('Priority', ascending=False)[['Interaction', 'Content', 'Priority']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis:\n",
    "Now we have a way to sort messages to see which ones to prioritize based on our keywords. \n",
    "\n",
    "We can also make use of the sentiment scores calculated above to help break ties like with the first two messages in our priority ordering. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
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       "      <td>S26</td>\n",
       "      <td>e0df43a4bf3faaab65ed44d0b07a3ce48bc247aebf1796ab61f71085f42362ad</td>\n",
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       "      <th>26</th>\n",
       "      <td>S27</td>\n",
       "      <td>Acknowledge Golf One. Message received. Golf two out.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>S31</td>\n",
       "      <td>Understood Alpha One. Recalling Alpha Two to base.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>S33</td>\n",
       "      <td>Acknowledge Alpha One. Alpha Two returning to base.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>S36</td>\n",
       "      <td>Operatives in the area. Today's passphrase is 'ski patrol'.</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   Signal ID  \\\n",
       "0         S1   \n",
       "2         S3   \n",
       "6         S7   \n",
       "7         S8   \n",
       "8         S9   \n",
       "10       S11   \n",
       "17       S18   \n",
       "23       S24   \n",
       "24       S25   \n",
       "25       S26   \n",
       "26       S27   \n",
       "30       S31   \n",
       "32       S33   \n",
       "35       S36   \n",
       "\n",
       "                                                                   Content  \n",
       "0                                                        Alpha Two sitrep?  \n",
       "2                       Bravo One to Bravo Two, we have a target in sight.  \n",
       "6        Delta One to Charlie One, moving to your position. ETA 5 minutes.  \n",
       "7                   Alpha One, Delta One is moving to support Charlie one.  \n",
       "8   Delta Two to Delta One, we have secured the area North of Charile One.  \n",
       "10                     Alpha one, alpha two is proceeding to checkpoint 1.  \n",
       "17                                  Juliet One to Juliet Two, radio check?  \n",
       "23                                         Roger, send for interpretation.  \n",
       "24               Transmitting now over digital. Acknowledge when received.  \n",
       "25        e0df43a4bf3faaab65ed44d0b07a3ce48bc247aebf1796ab61f71085f42362ad  \n",
       "26                   Acknowledge Golf One. Message received. Golf two out.  \n",
       "30                      Understood Alpha One. Recalling Alpha Two to base.  \n",
       "32                     Acknowledge Alpha One. Alpha Two returning to base.  \n",
       "35             Operatives in the area. Today's passphrase is 'ski patrol'.  "
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comint_df[comint_df.Priority == 0].sort_values('Priority', ascending=False)[['Signal ID', 'Content']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "comint_df = comint_df.assign(Sentiment = sentiment_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a column for compound priority score (CPS)\n",
    "\n",
    "comint_df = comint_df.assign(CPS = comint_df['Priority'] + -comint_df['Sentiment'])\n",
    "\n",
    "#note we added the negative to our CPS from the sentiment score\n",
    "#this is because we decided bad news should take priority over good/positive news."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
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       "31       S32   \n",
       "\n",
       "                                                                                                                                                  Content  \\\n",
       "29  Alpha One, be advised hostile communications indicate a major offensive is planned in your AO. Advise you increase local security checks and advis...   \n",
       "21  Charlie One to Alpha One area now secure. Significant hostile casualties and damage to local facilities. Requesting medical and engineering suppor...   \n",
       "12                                                                                            Golf One to Golf Two, we have intercepted a hostile signal.   \n",
       "4                                                                                                          Charlie One to Alpha One, we are under attack!   \n",
       "31                                                                                 Alpha Two, return to base immediately to assist with security efforts.   \n",
       "\n",
       "       CPS  \n",
       "29  3.2263  \n",
       "21  2.9742  \n",
       "12  2.3818  \n",
       "4   1.5255  \n",
       "31  0.6600  "
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comint_df[comint_df.Priority > 0].sort_values('CPS', ascending=False)[['Signal ID', 'Content', 'CPS']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that the message that indicates an attack is imminent has now been moved to the top of our queue. The message indicating the area is secure but help is needed has been moved down one but it still very high due to the request for assistance. The top two messages are very important as they require immediate action (i.e. Repositioning security and sending medical aid).\n",
    "\n",
    "Next we have a signal that a message about a hostile signal being intercepted has come through. This information would need to be passed on quicky so intelligence units can work to decode the enemy's signal. \n",
    "\n",
    "Our prioritization queue however isn't perfect as you can the Charlie One has sent a signal that it is under attack. Ideally if we were getting many messages in real time and not looking back at a series of events, we would want this message very high on the queue. One solution would be to add a weight to certain keywords like \"attack\" and \"help\" so they are always placed at the top.\n",
    "\n",
    "That works well for simple scenarios but context is important. For example, a message could say \"No evidence of impending attack. Maintain minimal security posture.\" This message menetions an attack and security so would be very high on our priority queue even though it doesn't require immediate action. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus\n",
    "##### Decrypting a message\n",
    "\n",
    "In our signals data there was an exchange with Golf unit about an intercepted message. This message was then sent to be decrypted. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>Encryption</th>\n",
       "      <th>Content</th>\n",
       "      <th>Interaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>MD5</td>\n",
       "      <td>e0df43a4bf3faaab65ed44d0b07a3ce48bc247aebf1796ab61f71085f42362ad</td>\n",
       "      <td>G1G2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Encryption  \\\n",
       "25        MD5   \n",
       "\n",
       "                                                             Content  \\\n",
       "25  e0df43a4bf3faaab65ed44d0b07a3ce48bc247aebf1796ab61f71085f42362ad   \n",
       "\n",
       "   Interaction  \n",
       "25        G1G2  "
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from cryptofuncs import decrypt_string\n",
    "\n",
    "comint_df[comint_df['Signal ID'] == 'S26'][['Encryption', 'Content','Interaction']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis:\n",
    "\n",
    "The message is encrpyted using MD5 which is normally impossible to reverse. However after further analysis we see that an unknown unit passed along a message about a passphrase. This could be the key needed to decrypt the message. Let's give it a try"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>Encryption</th>\n",
       "      <th>Content</th>\n",
       "      <th>Interaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Unencrypted</td>\n",
       "      <td>Operatives in the area. Today's passphrase is 'ski patrol'.</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Encryption                                                      Content  \\\n",
       "35  Unencrypted  Operatives in the area. Today's passphrase is 'ski patrol'.   \n",
       "\n",
       "   Interaction  \n",
       "35         NaN  "
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#message with passphrase reference\n",
    "comint_df[comint_df['Signal ID'] == 'S36'][['Encryption', 'Content','Interaction']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "secret_msg = comint_df[comint_df['Signal ID'] == 'S26'].iloc[0,7]\n",
    "secret_passphrase = \"ski patrol\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Kilo is entering the AO at 0600.'"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decrypt_string(secret_msg, secret_passphrase)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That did it. With the unencrypted message we know now what the hostile unit knows. It looks like they intercepted one of the unit's messages regarding Kilo's movements. \n",
    "\n",
    "This could indicate a leak in communications. "
   ]
  }
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