Azure Data Explorer Python Demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis on Windows Logon Data\n",
    "\n",
    "** Objective: **\n",
    "\n",
    "The dataset is used for this analysis is especially failed windows logon data. \n",
    "<br>Demo is focussed to introduce basic data analysis in python against log data source and plotting visualization to find abnormalities in the trend\n",
    "\n",
    "**** Investigation :  ****\n",
    " - Analyze and indentify any inconsistency in failed logon trend\n",
    " - Find the user and check what has changed in failed logon trend\n",
    " - Investigate further activity from the user and hosts in scope before and after trend changes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Global Parameters to Configure (TimeStamp, Host, User for Investigation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "FilteredComputer=\"WIN-DC01\"\n",
    "FilteredAccount = \"SRVACCNT-01\"\n",
    "startdate = 'ago(10d)'\n",
    "#startdate= '\\'2018-01-01\\''\n",
    "enddate = 'now()'\n",
    "#enddate= '\\'2018-01-15\\''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connection to Log Data Source (Python Authentication)\n",
    "Reference Blog Post on query data using Azure Data Explorer Python library: \n",
    "<br>https://docs.microsoft.com/en-us/azure/data-explorer/python-query-data\n",
    "\n",
    "### Install the data library:\n",
    "> `pip install azure-kusto-data`\n",
    "\n",
    "### Libraries for Popular Log management/SIEM products\n",
    "> **Splunk** : `pip install splunk-sdk`\n",
    "<br>http://dev.splunk.com/python\n",
    "\n",
    "> **Elasticsearch** : `pip install elasticsearch`\n",
    "<br>https://elasticsearch-py.readthedocs.io/en/master/\n",
    "\n",
    "> **HuntLib** : `pip install huntlib`\n",
    "    <br>Common threat hunting data analysis operations : https://github.com/target/huntlib\n",
    "    <br>The `huntlib` module provides two major object classes as well as a few convenience functions.\n",
    "    <br>**ElasticDF**: Search Elastic and return results as a Pandas DataFrame\n",
    "    <br>**SplunkDF**: Search Splunk and return results as a Pandas DataFrame\n",
    "    <br>**entropy() / entropy_per_byte()**: Calculate Shannon entropy\n",
    "    <br>**promptCreds()**: Prompt for login credentials in the terminal or from within a Jupyter notebook.\n",
    "    <br>**edit_distance()**: Calculate how \"different\" two strings are from each other\n",
    "\n",
    "\n",
    "### Possible ways to retrieve credentials:\n",
    "> Store and read from environment variable after activating conda environment\n",
    "> <br>Store and read from static config file from filesystem which will be under auditing\n",
    "> <br>Store and read from Secret vault or other password stores using certificate based access\n",
    "> <br>Prompt user the password and store value until the session/kernel shutdonw\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing kusto libraries\n",
    "from azure.kusto.data.request import KustoClient, KustoConnectionStringBuilder\n",
    "from azure.kusto.data.exceptions import KustoServiceError\n",
    "from azure.kusto.data.helpers import dataframe_from_result_table\n",
    "\n",
    "\n",
    "######################################################\n",
    "##              AUTHENTICATION                      ##\n",
    "######################################################\n",
    "\n",
    "# Provide values respective to your cluster configuration\n",
    "cluster = \"<insert here your cluster name>\"\n",
    "client_id = \"<insert here your AAD application id>\"\n",
    "client_secret = \"<insert here your AAD application key>\"\n",
    "authority_id = \"<insert here your AAD tenet id>\"\n",
    "database = \"<insert here your database name>\"\n",
    "\n",
    "#making kusto connection string builder client\n",
    "kcsb = KustoConnectionStringBuilder.with_aad_application_key_authentication(\n",
    "    cluster, client_id, client_secret, authority_id\n",
    ")\n",
    "client = KustoClient(kcsb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing python libraries and set global styling parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set()\n",
    "pd.set_option('display.max_colwidth', -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sample query against log data source\n",
    "winlogonquery = '''\n",
    "windowslogons \n",
    "| where startdate < {} and enddate > {}\n",
    "| where EventId == \"Microsoft-Windows-Security-Auditing:4625\"\n",
    "| summarize LogonCount=count(),SourceCount=dcount(sourceAddress), HostCount=dcount(destinationAddress) by AccountName,Date\n",
    "'''\n",
    "#Formatting Query String to pass global arguments and also replace newline characters\n",
    "winlogonquery= winlogonquery.format(enddate,startdate).replace('\\n', '')\n",
    "#executing query against the database using client object\n",
    "response = client.execute(database, winlogonquery)\n",
    "#creating dataframe from result table\n",
    "winlogonquery_df = dataframe_from_result_table(response.primary_results[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Printing Dataset schema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41 entries, 0 to 40\n",
      "Data columns (total 5 columns):\n",
      "Date           41 non-null object\n",
      "AccountName    41 non-null object\n",
      "HostCount      41 non-null int64\n",
      "SourceCount    41 non-null int64\n",
      "LogonCount     41 non-null int64\n",
      "dtypes: int64(3), object(2)\n",
      "memory usage: 1.7+ KB\n"
     ]
    }
   ],
   "source": [
    "winlogonquery_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Printing Sample dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Date</th>\n",
       "      <th>AccountName</th>\n",
       "      <th>HostCount</th>\n",
       "      <th>SourceCount</th>\n",
       "      <th>LogonCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3/28/2018</td>\n",
       "      <td>ASHWIN</td>\n",
       "      <td>263</td>\n",
       "      <td>327</td>\n",
       "      <td>46448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3/29/2018</td>\n",
       "      <td>ASHWIN</td>\n",
       "      <td>230</td>\n",
       "      <td>320</td>\n",
       "      <td>46379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3/30/2018</td>\n",
       "      <td>ASHWIN</td>\n",
       "      <td>245</td>\n",
       "      <td>352</td>\n",
       "      <td>44951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3/31/2018</td>\n",
       "      <td>ASHWIN</td>\n",
       "      <td>261</td>\n",
       "      <td>343</td>\n",
       "      <td>44347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4/1/2018</td>\n",
       "      <td>ASHWIN</td>\n",
       "      <td>226</td>\n",
       "      <td>289</td>\n",
       "      <td>44971</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date AccountName  HostCount  SourceCount  LogonCount\n",
       "0  3/28/2018  ASHWIN      263        327          46448     \n",
       "1  3/29/2018  ASHWIN      230        320          46379     \n",
       "2  3/30/2018  ASHWIN      245        352          44951     \n",
       "3  3/31/2018  ASHWIN      261        343          44347     \n",
       "4  4/1/2018   ASHWIN      226        289          44971     "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "winlogonquery_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color=orange>Plotting HeatMap for failed logons using seaborn library</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f034e2d8390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "failedlogons_ph = winlogonquery_df.pivot(index='Date', columns='AccountName', values='LogonCount').fillna(0)\n",
    "f, ax = plt.subplots(figsize=(16, 12))\n",
    "hm1 = sns.heatmap(failedlogons_ph.T,cmap=\"YlGnBu\",ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color=orange>Plotting Scatterplot for failed logons using seaborn library</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f0344a754e0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YkiaIhYfv3x9629vn0NGxZZxbJEmSNPU4HVqSJEmSVBuGYEmSJElSbRiCJUmSJEm1YQiWJEmSJNWGIViSJEmSVBuGYEmSJElSbRiCJUmSJEm1YQiWJEmSJNWGIViSJEmSVBuGYEmSJElSbRiCJUmSJEm1YQiWJEmSJNVG63g3QJIkSVJ1urvuZ+uGZfRs76R1xjxmz1/KrLZF490sadwYgiVJkqQpqrvrfjY/eHP/cc+2jf3HBmHVldOhJUmSpClq64ZlI6pLdWAIliRJkqaonu2dI6pLdWAIliRJkqao1hnzRlSX6sAQLEmSJE1Rs+cvHVFdqgM3xpIkSZKmqL7Nr9wdWnqSIViSJEmawma1LTL0Sg2cDi1JkiRJqg1DsCRJkiSpNgzBkiRJkqTaMARLkiRJkmrDECxJkiRJqg1DsCRJkiSpNgzBkiRJkqTaMARLkiRJkmrDECxJkiRJqg1DsCRJkiSpNgzBkiRJkqTaGFYIjoi/Hk5NkiRJkqSJbLgjwWcPsyZJkiRJ0oTVOtSbEfES4DTg4Ij4x4a39qu0VZIkSZIkVWBXI8HbgUeAXmBrw89K4BXVNk2SJEmSpNE15EhwZv4A+EFEXJeZ94xRmyRJkiRJqsSQIbjBfRHxBmBR4zWZ6eZYkiRJkqRJY7gh+F+BGcCdwLbhXBARlwJ/AKzPzGPL2oeBNwEd5Wnvz8wbyvfeB7wB2AG8PTNvLOunAxcB04AvZOYFZf1w4Cpgf+AnwGszc/swv48kSZIkqYaGG4KPzMyjR3jvy4HPAFcOqP9TZn6ysRARz6DYbfoY4GDgPyPi6eXbnwVeAqwF7oqI6zPzXuAfyntdFRH/TBGgLxlhGyVJkiRpQomIjwHnAr+dmU+M8WfPBV6RmZeWx68HPg8szsxVEXEY8KXMPHks2zWahvuIpFURMWckN87MW4CNwzz9DOCqzNyWmauB+4Djy5/7MnNVOcp7FXBGRLQApwLXltdfAZw5kvZJkiRJ0kRTZp2XATcCLx6HJswFzh9QWwF8aBzaUonhjgRvBu6OiBuBx/qKu7km+G0R8TrgbuBdmdkJHAL8sOGctWUNYM2A+nOB+cCmzOxpcv6Q5s3bh9bWabvR7Oq1t4/o7wwaAfu2OvZtdezb6ti31bFvq2X/Vse+rY59O2IvBG4FLgP+CrgpIk4GLgB6gA2Z+cflbNrPUCwb7aEIzgspRm2nUTzl5/WZ2RERazPzUICI+BLFMtPvR8Q64CsUGeuXmfka4N3A70TE94Evlm36FvCiiFhMQx6MiLOAtwDTKQZAX5WZ3eW1K4CjyrZcTLEs9pDynHvL7/QxiicRdZRtfXT0unFwww3BWf7sqUuAj1J80Y8CF1L8laGlybm9NB+p7h3i/F3q7ByTfh2x9vY5dHRsGe9mTEn2bXXs2+rYt9Wxb6tj31bL/q2OfVudyd634xTgz6VY9vnfEfFb5Yzcy4Dfy8xfRkTfiN4XKfZSuisi9qLIQ58ALsjM70TEm4APAu8Y4rMOAC7KzF9FxC0RcWx5j+My84XQPx0a4G/Ln/c0XH9DZl5TnvcJiiWul5Xv3ZqZb42IL5RtPz0i/hR4c0T8BUU2fFFmPhwR76AI0xfuVo+N0LBCcGb+7Wh8WGb+pu91RPwL8M3ycC3FXy36HAo8WL5uVn8YmBsRreVocOP5kiRJkjTpRMRsiv2QDooIKGbA/m+gKzN/CZCZO8rTD83Mu8raE+X1i4H/Kt+/DfiTJh/TOKC4PjN/Vb7+Vfl5jzRrWxms3w8saSifHBF/RTESfNCAa39c/l4L/Lrh9e9ThO/fAq4tv+dMitHvMTGsEBwR/9isPtLp0BGxIDPXlYcvB/qePXw98JWI+BTFxlhHAT+i+Ac6qtwJ+tcUf1l4dWb2RsT3gFdSrBM+F/jGSNoiSZIkSRPMHwMXZ+bfAUTE04CrgbaIWJiZayJiWhmEfx0Rz87MH5friAFWAicC3wFOBn7ed+MyYG8HljZ83sDZtC3lOYPlxL8BPg30Ta/9B+CPyhHqT/PUgN07yOsWikHN1cCZmbmpbN+MQT5z1A13OvTWhtczKR59dPdQF0TEVynmsx8QEWspFlK/MCKWUnTCA8CfAWTmioi4BriXYj77W/v+whERb6NYFD4NuDQzV5Qf8R7gqnLntJ/y5Hx1SZIkSZqMzqVYBwxAZq6PiFnAG4GrI2I7sIEiLL8R+Ew5krqDYk3we4DPR8QHKILqueWtLgTuAH7GrmfQPgRsjIjrKNYL98vMWyLiYWCfsnQ5cENEJLAF2DScL1kOav5v4LpyKjcU07BvGM71e6qlt3dYS2mfopyX/qXMPGP0m1Stjo4tI//CY2Cyr5eYyOzb6ti31bFvq2PfVse+rZb9Wx37tjqTvW/b2+c024tIk9xwH5E00CPAkaPZEEmSJEmSqrY7a4L3Ap7N6OwWLUmSJEnSmNmdNcE9wOeA60a/OZIkSZIkVWdMH5EkSZIkSdJ4Gu506AOAzwAvptjZ+T+Bd2RmR4VtkyRJkiRpVA13Y6zPAb+geKbUs4D/KWuSJEmSJE0aw10TvCgz/7jh+EMRsayKBkmSJEmShiciXg58DTg6M1eWz939NHAqxSzex4CzMnN1RDwAPCczHy6vfSHFc4n/EOgAjsrMzohYQPE84VMy87by3A5gMfDnwCOZ+cmIuBx4CXBEZm4rZxDfnZmHjcmX303DHQneKyKe1ndQvt7dxytJkiRJkkbHOcBtwNnl8auAg4Elmfk7wMuBTUPdIDN7gTuB55WlE4Gflr+JiAAezswNTS7fAZy/h99hTA13JPiTwE8j4lsUf014GfC+ylolSZIkSVPIH77rG6dRhMUjgFXApf9+4Rnf2ZN7RsS+wEnAi4DrgQ8DC4B1mfkEQGauHebtbqcIvTeUvz8F9M0GPhH4r0Gu+zTwFxHxL7vxFcbFsEZzM/NK4DRgOXAP8PuZ+aUqGyZJkiRJU0EZgD8OHEmRwY4EPl7W98SZwLcz8xfAxoh4FnAN8IcRsSwiLoyIZw645nvle8uALzTU/4ty5Bc4Hvg3YGF5fCJFSG7mVxQj0a/dw+8yZoYMwRFxaEQ8ByAzV2TmZzLz/wKzI+KQMWmhJEmSJE1ug00XPm8P73sOcFX5+irgnHLkNyhm7j4BfDciXtxwzYsyc2lmLgXe2FD/EfDMiJgNTM/MR4BVEXEkQ48EQxHw380kWTK7q0b+I7BPk/q88j1JkiRJ0tCOGGF9lyJiPsXmV18oN7x6N/CqiGjJzG2Z+R+Z+W6KgHrmru6XmY8C91EE9p+U5R9SLIV9GpBDXHsfsAw4a3e/z1jaVQg+LjNvGVjMzO8Ax1XTJEmSJEmaUlaNsD4crwSuzMzfzszDMnMhsBp4fkQcDFDuFL0E+OUw73k78E7gjvL4DuAdwA/LzbOG8ncUO01PeLsKwS17cK0kSZIkCS4dpH7ZHtzzHODrA2rXAZcD/x4R91Ds6dQDfGaY97ydYnS6LwT/BDiUoadCA8XyWZ4cQZ7QWnp7Bw/0EfET4JWZuWpA/Qjg2sx8VsXtG3UdHVt29ReMcdHePoeOji3j3Ywpyb6tjn1bHfu2OvZtdezbatm/1bFvqzPZ+7a9fc5Qg4IjUm6CdR5P7g592Z7uDq3ds6tHJF0IXB8R76RYKA3FTmGfwjXBkiRJkjQsZeA19E4AQ4bgzPxyRMyiGL4/tCyvBT7mI5IkSZIkSZPNrkaCycwvUOw41g60ZOb66pslSZIkSdLoG9bmVhHRAvwR8K7y+LCIOHHoqyRJkiRJmliGu8Pzp4AXA2eUx1uAT1fSIkmSJEmSKjLcEPwi4DVAN0BmbgBmVtUoSZIkSZKqsMs1waXHMrM3IoD+hy6P2nbhkiRJkqSRiYgdwM8aSldl5gUR8X1gAfAYsB14U2YuK695AHhOZj4cEQdRzPD9XWAb8ADwzsz8xVh9h/Ew3BD8s4h4DdASEYcB7wNuraxVkiRJkqRd6c7MpYO895rMvDsizgM+Abyk8c1y36evA1dk5tllbSlwIGAIBv6SYl3wAuBO4HrKTbIkSZIkSUM76+q3nAacDxwBrAIuveZVl4zFc4PvAN7dpP4i4PHM/Oe+Qt9o8VQ3rBCcmVuAN5U/kiRJkqRhKgPwxxtKRwIfP+vqt7CHQXhWRDQG17/PzKsHnHM68G9Nrj0W+PEefPakNawQHBHvAf4lMzeWx/OB8zPzE1U2TpIkSZKmgPMHqZ8H7EkIHmo69JcjYjYwDXjWHnzGlDPc3aHP6QvA0L879KuraZIkSZIkTSlHjLA+Gl4DHA58Bfhsk/dXAM+u8PMnrOGG4GY7QQ93PbEkSZIk1dmqEdZHRWY+DnwQOCEijh7w9s3A3hHRv+Q1In43Il5QZZsmguEG2f+JiL8E/okiEP8FcF9lrZIkSZKkqeNSnromuM9le3jfgWuCv52Z7208ITO7I+JC4K+ANzTUeyPi5cCnI+K9FI9TegB45x62acIbbgh+O/Alin+4XuC/gD+tqlGSJEmSNFVc86pLvnPW1W+BYg1w3+7Ql+3p7tCZOW2Q+gsHHF/Y8PqwhtcPAmftSRsmo12G4IjYCzggM08tF1aTmVsrb5kkSZIkTRFl4B2LRyJpF3a5JjgznwC+WL7eagCWJEmSJE1Ww90Y6+cRcViVDZEkSZIkqWrDXRPcDiyPiNuAR/qKmVm7+eOSJEmSpMlruCH4qvJHkiRJkqRJa1ghODOvqLohkiRJkiRVbVghOCL+leLRSE/hdGhJkiRJGh8R8Uhm7lu+/gvg74EDM3NzWXsh8D3gjZn5xbL2TOAnwLsz85MRcTnwAmBzedtHM/PEiDiQYoPkhcB04IHMfNmYfbkKDXc69DcbXs8EXgncO/rNkSRJkiTthnOAu4CXA5c31H8GvIryiT/A2cB/D7j23Zl57YDaR4CbMvMigIhYMtoNHi+7NR06Ii4Drq+kRZIkSZI0xdx+xh+fBpwPHAGsAi496RvXjcpzgyNiEbAv8G7g/Tw1BP8KaCtHdtcDpwM3DOO2C2h4rnFmLh+Ntk4Ew31E0kC9wOGj2RBJkiRJmorKAPxx4EiKDHYk8PGyPhrOAb4K3ApERDxtwPvXAn8CnEgxFXrbgPc/ERHLyp8vl7XPAl+MiO9FxAci4uBRauu42501wXsBS4D/rKpRkiRJkjSFnD9I/TwaRlv3wNnAyzPziYj4GkXg/WzD+9cAVwOLKcLyiQOu32k6dGbeGBFHUIwcvxT4aUQcm5kdo9DecbU7a4J7gE9m5g8raI8kSZIkTTVHjLA+bOVa3aOAmyICYAbFdOv+EJyZD0XE48BLgHewcwhuKjM3Al8BvhIR3wSeD1y3p20ebyNaExwRs8vjrVU2SpIkSZKmkFUUU6Cb1ffUOcCHM/Pv+woRsToifnvAeX8DPC0zd5RheUgRcSrww8x8NCLmAIso1hdPesOdDn0ExV8AjiuPfwr8aWaOxj+aJEmSJE1ll1KsCR7ost29YUS0UqztPZtiunKjr5f1O/sKmflfQ9zuExHxwYbj44FnA5+JiB6KJbFfyMy7dre9E8lwp0N/Dvg8T/4jvb6svaSCNkmSJEnSlHHSN677zu1n/DEUa4D7doe+bA93hz4GuD8zjx/4Rmb+ZcPh95u8/+GG168f5P6fKH+mnOGG4PbMvLTh+LKIeEcVDZIkSZKkqaYMvKP1SKT/D3g78M7RuF/dDDcEPxERkZkJEBFPB3ZU1yxJkiRJUjOZ+c/AP493Oyar4Ybg9wO3RsSy8vg44LXVNEmSJEmSpGoMd3fob0fEMRQLrp8Afp6ZP660ZZIkSZIkjbK9hnozIr5UPncKiunPHwfeB9wQEW+sunGSJEmSJI2mIUMw8KzMXF6+fi1wb2YeQ7Fd9tsqbZkkSZIkSaNsV9OhH2t4fTLF86bIzLUR0VtZqyRJkiRJQ4qIDwCvppi1+wTwZ8A/AAsostx24E2ZuSwiLgfuyMzPNVx/JvDmzHxZefxy4GvA0Zm5suG8pwOfBp4OPA78DPjzzPxNRBwPfBI4EOgFbqPYufosiucjL+0bWI2Ie4A/AK4G9gb2B2YBvy4/6szMfGDAd3wf8IbyO749M28s65eW91qfmceOpN92NRIxd1/0AAAf8UlEQVRMRBwcEbOAFwI/aHhr5kg+SJIkSZI0OiLieRQh8FmZuQT4PWBN+fZrMvM44GKefNbvV4GzB9zm7LLe5xyKENt/XkTMBL4FXJKZR2bm0cAlQHtEHAj8K/CezAzgaODbwJzy8rXABwa2PTOfm5lLgb8Brs7MpeXPAwO+4zPKthwDnA5cHBHTyrcvL2sjtquR4L8HllH8BeG2zLy3bMwJwK925wMlSZIkqW4+8q5/Pw04HzgCWAVc+jcX/uGePDd4AfBwZm4DyMyHASKi8Zw7gHeXr/8TuDwiFmTmuojYhyI4v6m8bl/gJOBFwPXAh8vrXk0xgvzvfTfNzO+V13wEuCIz7yjrvcC1De34JvD8xsftjtAZwFXld1wdEfcBx5ftuSUiDtuNew49EpyZ/wosofgLwysa3voVZWdJkiRJkgZXBuCPA0dSZLAjgY+X9d31HWBhRPwiIi6OiBc0Oed04N8AMnMHxVTns8r3/gj4XmZuKY/PBL6dmb8ANkbEs8r6scBgTwYa6j0opmj/I8Ujd3fHITw5ug3FyPIhu3mvfrt8RFJmPgQ8NKD24J5+sCRJkiTVxPmD1M+jCLMjlpmPRMSzgVMoRm+vjoj3lm9/OSJmA9OAZzVc9lWK6dEXUUwzvrLhvXMo1v0CXFUe/2R32jbAV4APRMThu3FtS5PaHu9NNaznBO+OZguVI2J/ikXQhwEPAGdlZmdEtFD8Q7wMeBR4fWb+pLzmXOCD5W0/lplXlPVnU8wDnwXcALyjHH6XJEmSpInkiBHWh6Uc3f0+8P2I+BlwbvnWa4D/Bi4APsuTs3pvBxZExHHAiZRrfyNiPnAqcGy5AfI0oDci/hpYATQbZaZ879nAN4ZoY09EXAi8Z1ffp9yY60Pl4RspRn4XNpxyKLDHA7K73BhrD1zOzguV3wt8NzOPAr5bHgO8FDiq/HkzxULrvtD8IeC5FHO/PxQR88prLinP7btutxZFS5IkSVLFVo2wvktROKqhtBT4Zd9BZj5OMZh4QkQcXdZ6gWuAK4AbMrPvaUCvBK7MzN/OzMMycyGwmuIJQV8BToyI/9Xw2adHxO8AnwHOjYjnNrz3pxFx0IDmXk6x/rh9qO+UmV9v2CTrboq1yWdHxN7lSPJRwI+G1UFDqCwEZ+YtwMYB5TMoOpzy95kN9SszszczfwjMjYgFwO8DN2XmxszsBG4CTi/fa8vMO8p/yCsb7iVJkiRJE8mlg9Qv24N77gtcERH3RsRy4Bk8uZkVAJnZDVwI/FVD+avAcRRTnvucQ/k43AbXAa8u7/EHwJ9HxP9ExL3A6ylm/P6GYjT5kxGREfFziunZXQPasR34P8DTRvIFM3MFRWi/l2LX6beWo99ExFcpNv6KiFgbEW8Y7n1benurm0Fc7tb1zYbp0Jsyc27D+52ZOS8ivglckJm3lfXvUgyXvxCYmZkfK+v/P9BNMeR/QWb+Xlk/hWJb7j/YVZt6enb0trZO29VpkiRJktRsTepuKTfBOo8nd4e+bA93h9ZuqmxN8AgNtuB5pPVd6ux8dATNGjvt7XPo6Niy6xM1YvZtdezb6ti31bFvq2PfVsv+rY59W53J3rft7XN2fdIwlYHX0DsBVLkmuJnflFOZKX+vL+uDLXgeqn5ok7okSZIkSYMa6xB8PU/uWHYuT+4idj3wuohoiYgTgM2ZuQ64ETgtIuaVG2KdBtxYvrclIk4od5Z+HUPsSCZJkiRJElT7iKSvUqzpPSAi1lLs8nwBcE25aPlXwJ+Up99A8Xik+ygekXQeQGZujIiPAneV530kM/s223oLTz4i6T/KH0mSJEmSBlVZCM7McwZ568VNzu0F3jrIfS6lyW5q5ZbZx+5JGyVJkiRJ9TLW06ElSZIkSRo3E2V3aEmSJEnSCETEB4BXAzuAJ4A/A/4BWAA8BmwH3pSZyyLicuCOzPxcw/VnAm/OzJeVxy8HvgYcnZkrG857OvBp4OnA48DPgD/PzN9ExPHAJ4EDKZ7YcxvwduAsihm9SzNzeXmfeyieOXw1sDewP8Xy1l+XH3VmZj4w4Du+D3hD+R3fnpk3RsRC4ErgoPJ7fz4zLxpuvzkSLEmSJEmTTEQ8jyJQPiszlwC/B6wp335NZh4HXAx8oqx9FTh7wG3OLut9zqEIsf3nRcRM4FvAJZl5ZGYeDVwCtEfEgcC/Au/JzACOBr4N9D1bai3wgYFtz8znZuZS4G+AqzNzafnzwIDv+IyyLccApwMXR8Q0oAd4V9mWE4C3lucOiyPBkiRJklSxH3/n3acB5wNHAKuAS5992if25LnBC4CHM3MbQGY+DBARjefcAby7fP2fwOURsSAz10XEPhTB+U3ldfsCJwEvonh6z4fL615NMYL87303zczvldd8BLgiM+8o673AtQ3t+Cbw/IiIzMzd+I5nAFeV33F1RNwHHF9+3rryM7dExM+BQ4B7h3NTR4IlSbW3dcU9rPvcxfzyY3/Lus9dzNYV94x3kyRJU0gZgD8OHEmRwY4EPl7Wd9d3gIUR8YuIuDgiXtDknNOBfwPIzB0UU53PKt/7I+B7mbmlPD4T+HZm/gLYGBHPKuvHAj8epA1DvQfFVOV/BN4/zO800CE8OboNxcjyIY0nRMRhwDOBO4d7U0OwJKnWtq64h4e/di3b16+H3l62r1/Pw1+71iAsSRpN5w9SP293b5iZjwDPBt4MdABXR8Try7e/XD6m9j3A/224rHFKdLOp0FeVr68qj0fDV4ATIuLw3bi2pUmtt+9FOXp9HfDOzOwa7k2dDi1JqrWu225pXr/9VmYf45P4JEmj4ogR1oelHN39PvD9iPgZcG751muA/wYuAD4LvKKs3w4siIjjgBMpA3FEzAdOBY6NiF5gGtAbEX8NrACajTJTvvds4BtDtLEnIi6kCORDKjfm+lB5+EaKkd+FDaccCjxYnjudIgB/OTO/tqt7N3IkWJJUa9s7OprWHx+kLknSblg1wvouReGohtJS4Jd9B5n5OPBBilHYo8taL3ANcAVwQ2Y+Vp7+SuDKzPztzDwsMxcCq4GTKUZyT4yI/9Xw2adHxO8AnwHOjYjnNrz3pxFx0IDmXk6x/rh9qO+UmV9v2CTrboq1yWdHxN7lSPJRwI8iogX4IvDzzPzUMLrrKQzBkqRam9He/P+Ppw9SlyRpN1w6SP2yPbjnvsAVEXFvRCwHnsGTm1kBkJndwIXAXzWUvwocx5NTn6GY+vz1Afe/Dnh1eY8/AP48Iv4nIu4FXg+sz8zfUIwmfzIistyg6hTgKVOTM3M78H+Ap43kC2bmCorQfi/FrtNvLUe/TwJeC5waEcvKn5cN974tvb29uz5rCuno2DIhv3B7+xw6Orbs+kSNmH1bHfu2OvZtdQb2bd+a4IEOeMUrnQ49Qv7ntlr2b3Xs2+pM9r5tb5/TbE3qbik3wTqPJ3eHvmwPd4fWbnJNsCSpVjp/uox137qR7R0dzGhvp+3k53PAK15J1+238nhHB9Pb22k76RQDsCRpVJWB19A7ARiCJUm1sXXFPWy6/uv09OwA6N8J+oBXvJIFb37LOLdOkiSNBdcES5JqY6idoCVJUj0YgiVJteFO0JIkyenQkqTamNHezhMbN+xUH2on6K0r7qHrtluesobY9cKSJE1ejgRLkmqj7eTnN6+fdErTet/O0dvXr4fe3v41xFtX3FNlMyVJUoUcCZYk1cbsY45lv/1mseaG7wxrJ+iBa4if6O5mxyNbWPf5S5j9jGMcFZYkaRIyBEuSamXeM5fSc+iiYZ3buIb4ie5uejZ1PvleOSoMGIQlSZpEnA4tSdIgZjSsFd7xyJb+1y2t0/tfu7O0JEmTiyFYkqRBNK4h7u3p6X89bd99+1+7s7QkSZOL06ElSRpE3zTnrttv5fGHO6C3CMB7zZrVf85QO0tLkqSJxxAsSdIQZh9zLLOPObZ/p+iBBttZWpIkTUyGYEmShuEpo8LD2FlakiRNTIZgSdKktXXFPXTddgvbOzqY0d5e+SOL+kaFJUnS5GUIliRNSgOnJ/vIIkmSNBzuDi1JmpS6bruled1HFkmSpCEYgiVJk9L2QR5N5COLJEnSUJwOLUmalGa0t7N9/fqd6i0zprPucxeP2TphSZI0uTgSLEmalNpOfv5OtSe6u+np3FSE497e/nXCW1fcMw4tlCRJE5EhWJI0Kc0+5lgOeMUrmXHggbTstRczDjyQ1rlz2WvWrJ3OdZ2wJEnq43RoSdKkNfCRRb/82N82Pc91wpIkqY8jwZKkKWNGe3vT+vRB6pIkqX4MwZKkKaPZOmGAtpNOGeOWSJKkicrp0JKkKaNvanTX7bfyeEcH09vbaTvpFHeHliRJ/QzBkqQpZeA6YUmSpEZOh5YkSZIk1YYhWJIkSZJUG06HliRNCltX3EPXbbewvaODGe3ttJ38fKc9S5KkETMES5ImvK0r7uHhr13bf7x9/fr+Y4OwJEkaCadDS5ImvK7bbmlev/3WMW6JJEma7AzBkqQJb3tHR9P644PUJUmSBmMIliRNeDPa25vWpw9SlyRJGowhWJI04bWd/Pzm9ZNOGeOWSJKkyc6NsSRJE17f5lddt9/K4x0dTG9vp+2kU9wUS5IkjZghWJI0Kcw+5lhDryRJ2mNOh5YkSZIk1YYhWJIkSZJUG4ZgSZIkSVJtGIIlSZIkSbVhCJYkSZIk1YYhWJIkSZJUG4ZgSZIkSVJtGIIlSZIkSbVhCJYkSZIk1YYhWJIkSZJUG4ZgSZIkSVJttI7Hh0bEA8AWYAfQk5nPiYj9gauBw4AHgLMyszMiWoCLgJcBjwKvz8yflPc5F/hgeduPZeYVY/g1JEmSJEmTzHiOBL8oM5dm5nPK4/cC383Mo4DvlscALwWOKn/eDFwCUIbmDwHPBY4HPhQR88aw/ZIkSZKkSWYiTYc+A+gbyb0COLOhfmVm9mbmD4G5EbEA+H3gpszcmJmdwE3A6WPdaEmSJEnS5DEu06GBXuA7EdELfC4zPw8cmJnrADJzXUQ8rTz3EGBNw7Vry9pg9SHNm7cPra3TRuErjL729jnj3YQpy76tjn1bHfu2OvZtdezbatm/1bFvq2PfaqIZrxB8UmY+WAbdmyJi5RDntjSp9Q5RH1Jn56PDbOLYam+fQ0fHlvFuxpRk31bHvq2OfVsd+7Y69m217N/q2LfVmex9a4CfmsZlOnRmPlj+Xg98nWJN72/Kac6Uv9eXp68FFjZcfijw4BB1SZIkSZKaGvMQHBGzI2JO32vgNOAe4Hrg3PK0c4FvlK+vB14XES0RcQKwuZw2fSNwWkTMKzfEOq2sSZIkSZLU1HhMhz4Q+HpE9H3+VzLz2xFxF3BNRLwB+BXwJ+X5N1A8Huk+ikcknQeQmRsj4qPAXeV5H8nMjWP3NSRJkiRJk82Yh+DMXAUc16S+AXhxk3ov8NZB7nUpcOlot1GSJEmSNDVNpEckSZIkSZJUKUOwJEmSJKk2xusRSZI06WxdcQ9dt93C9o4OZrS303by85l9zLHj3SxJkiSNgCFYkoZh64p7ePhr1/Yfb1+/vv/YICxJkjR5OB1akoah67Zbmtdvv3WMWyJJkqQ9YQiWpGHY3tHRtP74IHVJkiRNTIZgSRqGGe3tTevTB6lLkiRpYjIES9IwtJ38/Ob1k04Z45ZIkiRpT7gxliQNQ9/mV12338rjHR1Mb2+n7aRT3BRLkiRpkjEES9IwzT7mWEOvJEnSJOd0aEmSJElSbRiCJUmSJEm1YQiWJEmSJNWGIViSJEmSVBuGYEmSJElSbRiCJUmSJEm1YQiWJEmSJNWGIViSJEmSVBuGYEmSJElSbRiCJUmSJEm1YQiWJEmSJNWGIViSJEmSVBuGYEmSJElSbRiCJUmSJEm1YQiWJEmSJNWGIViSJEmSVBuGYEmSJElSbRiCJUmSJEm1YQiWJEmSJNWGIViSJEmSVBuGYEmSJElSbRiCJUmSJEm1YQiWJEmSJNWGIViSJEmSVBuGYEmSJElSbRiCJUmSJEm1YQiWJEmSJNWGIViSJEmSVBuGYEmSJElSbRiCJUmSJEm1YQiWJEmSJNWGIViSJEmSVBuGYEmSJElSbRiCJUmSJEm1YQiWJEmSJNWGIViSJEmSVBuGYEmSJElSbRiCJUmSJEm1YQiWJEmSJNWGIViSJEmSVBut490AVW/N6o2sXL6Ork2P0TZ3JouXLGDh4fuPd7MkSZIkacwZgie4b97xADf9aA1bH3uclpYWFszfh7NOPZJjD58/rHC7ZvVG7vzBqv7jzZ3d/ccGYUmSJEl1YwiegG584GZu/fUP6dr2CI9va6Vnv4U80b0IentZ27GVz31jBWc997f4TT7cf81g4Xbl8nVNP2Pl8ocMwZIkSZJqxzXBE8yND9zM9au+Tee2TfT09tAy4zGmH/o/TDvo/v5ztj7Ww0/vWtv0+pXLH3rKcdemx5qe17Wpe/QaLUmSJEmThCPBE8y3Vt+0c7EFWg+5jx0PLeov9XT3QNvOpw4Mt21zZ7K5c+fA2zZ3Ft1d97N1wzJ6tnfSOmMes+cvZVbbop3OlSRJkqSpwhA8Qfz3Q/fy7Z/fwo7eHU3fb9mr9ynH21ua36dt7qynHC9esuApa4L7xNHb2fzg7f3HPds2svnBmwGaBmEDsyRJkqSpYNKH4Ig4HbgImAZ8ITMvGOcmjdh/rLibm9fdzPaeHTAdGCTgNmqdP6tpffGSg55y3Lfud+Xyh+ja1E3b3FksXnIQs/gePdt2vn7rhmU7hdvurvv7AzLsOjBLkiRJ0kQ1qUNwREwDPgu8BFgL3BUR12fmvePbsuG7Z/UGblp1B09M3wF9g729QEuRhXubXLP39L04/dQj2Y+WncJts82uFh6+/071h7KzaXt6tu9c37phWdNzmwVmSZIkSZrIJnUIBo4H7svMVQARcRVwBjBpQvBty9exY8YjDYO/ZfTtC8Llyycen04LMH+/vXnd6Ys59vD5wO4/5qh1xjx6tm1sWh+oWTAeqi5JkiRJE9VkD8GHAGsajtcCzx3qgnnz9qG1dVqljRqJzke2M33uvvRMe4SWFpi2YzY7pj3y5AktMGva3rzrhW/muNc+Y9Q+d++W5/Hr+769U/2QI55H2wFznlLbsq6dbY9u2Pke+xxAe/ucneoT0WRp52Rk31bHvq2OfVsd+7Za9m917Nvq2LeaaCZ7CG62erbZDOJ+nZ2PVtSU3TNv3xls2XIYPXPvobcXWnZMZ1rvvjBtO/Nn78fCfQ/meQt+l4OnLaSjY8sofvLBzG5/wU6bXW3rPXinz2mdfSxbN9+80x1mzz5mlNtUjfb2OZOinZORfVsd+7Y69m117Ntq2b/VsW+rM9n71gA/NU32ELwWWNhwfCjw4Di1ZbecvGQB1/2gm9Yte/HIzFXsaN1K6/Z5vOSI5/HSY55T6WfPals0rDW9fee4O7QkSZKkyW6yh+C7gKMi4nDg18DZwKvHt0kj07e29+5sY81vDqR97kxOXrKgvz5RDDcwS5IkSdJENqlDcGb2RMTbgBspHpF0aWauGOdmjdixh8/nRccfNqmnikiSJEnSZDCpQzBAZt4A3DDe7ZAkSZIkTXx7jXcDJEmSJEkaK4ZgSZIkSVJtGIIlSZIkSbVhCJYkSZIk1YYhWJIkSZJUG4ZgSZIkSVJtGIIlSZIkSbVhCJYkSZIk1YYhWJIkSZJUG4ZgSZIkSVJtGIIlSZIkSbVhCJYkSZIk1YYhWJIkSZJUGy29vb3j3QZJkiRJksaEI8GSJEmSpNowBEuSJEmSasMQLEmSJEmqDUOwJEmSJKk2DMGSJEmSpNowBEuSJEmSasMQLEmSJEmqjdbxbkDdRcTpwEXANOALmXnBODdpwouIhcCVwEHAE8DnM/9fe/ceI1dZh3H8u1RAELCggAQwtKQ83AKLrYVIIAVrEVJBDJcSwk2g1JSAUYQWjBDgD4SI8YJACKSQAAW5xCYWSiGof2gRKNcCj1ysWGlatdwMEdKy/nHerdPtzHa3THd2dp5Pstk575zznjO/efPO+Z3znnP8M0lXAOcC/yyzXmp7fllmNnA2sAa4wPaCUp749yFpKfA+VaxW254gaQfgHmAPYClwku23JXVRxe8Y4APgTNuLSz1nAD8s1V5t+/Yh/BjDjiRRxbDXWOBHwGjSbjeKpNuAqcBK2/uXsqa1VUnjgTnAVsB84ELbPUPy4VqsQWyvA74BfAS8Dpxl+x1JewAvAy6LL7I9oyxTN4aNvqch+XAt1iC2V9CkfkDSGGAusAOwGDjN9kdD8+laq0Fs7wFUZhkNvGO7O+12cPrZ90qfG20pZ4JbSNIo4AbgaGBf4BRJ+7Z2q9rCauD7tvcBDgFm1sTtp7a7y1/vDsS+wDRgP+DrwK8kjUr8+3VEieGEMj0LeMz2OOCxMg1V7MaVv+nAjbA2EbkcOBiYCFwuafsh3P5hx5Vu293AeKqdggfL22m3G2cOVWxqNbOt3ljm7V2u77pGsjms/3kXAvvbPgD4CzC75r3Xa9rwjJryRjFs9D11gjnUb0vN6gd+XOoaB7xNlUB3ijn0ia3tk2v63vuBB2reTrsduEb7Xulzoy0lCW6ticBrtt8oR2nnAse1eJuGPdvLe48m2n6f6kjurv0schww1/aHtv8KvEYV+8R/4I4Des/k3g58s6b8Dts9thcBoyXtAhwFLLS9qhwlX0h+zGp9lWrn62/9zJN2uwG2/wCs6lPclLZa3tvO9p/KmYg7auoa8erF1vYjtleXyUXAbv3VsYEYNvqeRrwG7baRQfUD5ezbkcB9ZfnEtiixOQm4u7860m7r62ffK31utKUkwa21K/D3mull9J/MRR9lONNBwBOl6HxJz0u6rebIYqM4J/719QCPSHpa0vRStrPt5VD9EAI7lfLEduNMY90dsbTb5mlWW921vO5bHpVvAw/VTI+R9Iyk30s6rJT1F8NG31Mna0Y/8Dmq4b6r+5QHHAassP1qTVna7Ubos++VPjfaUpLg1uqqU5ZrHwZI0jZUQ5u+a/s9qmE0ewLdwHLgJ2XWRnFO/Os71PaXqIYyzZR0eD/zJraDJGkL4Fjg16Uo7XZoDDaeiXMDki6jGhp5ZylaDnzR9kHA94C7JG1HYjgYzeoHEvPGTmHdg49ptxuhzr5XI2m7MawlCW6tZcDuNdO7AW+1aFvaiqTNqTrhO20/AGB7he01tj8GbqEaLgaN45z412H7rfJ/JdU1qxOBFWWoUu9QsZVl9sR28I4GFtteAWm3m0Cz2uoy1h3umziz9oY2U4FTe29YU4bq/ru8fprqpll70X8MG31PHamJ/cC/qIadfqpPeUcr8fgWNTcnTLsdvHr7XqTPjTaVJLi1ngTGSRpTzg5NA+a1eJuGvXJdz63Ay7avrynfpWa244EXy+t5wDRJW5a7Zo4D/kzivx5Jn5G0be9rYApVHOcBZ5TZzgB+U17PA06X1CXpEODdMhxqATBF0vZlWN+UUhZ9zkak3TZdU9pqee99SYeUPuf0mro6Urkb8SXAsbY/qCnfsdyoCUljqdrqGxuIYaPvqSM1qx8oByYeB04oy3d8bIvJwCu21w63TbsdnEb7XqTPjTaVRyS1kO3Vks6n6hBGAbfZXtLizWoHhwKnAS9IeraUXUp1d8xuquEzS4HzAGwvkXQv8BLVEL6ZttcAJP7r2Rl4UBJU/cNdth+W9CRwr6SzgTeBE8v886kef/Aa1d2OzwKwvUrSVVQ7agBX2h7ojWBGLElbA1+jtM3i2rTbjSPpbmAS8HlJy6juOHoNzWur3+H/j+t4iHWvgR3RGsR2NrAlsLD0Eb2PlDkcuFLSaqrH+MwYQAwbfU8jXoPYTmpiP3AJMFfS1cAzVIlLR6gXW9u3sv59GCDtdrAa7Xulz4221NXTk+H2ERERERER0RkyHDoiIiIiIiI6RpLgiIiIiIiI6BhJgiMiIiIiIqJjJAmOiIiIiIiIjpEkOCIiIiIiIjpGHpEUERHDjqSlwFTbL9aUPQVcZPt3g6xrNDDd9rV9yk8FLqJ6HEcP8Bxwse03P9HG19+GScAWth9pdt0RERExODkTHBERI91o4OLaAknnAJcBJ9ne2/Y+wE3AFzbRNkwCpmyiuiMiImIQciY4IiLaiqSdqRLWPYEu4Drbd0jaDPglcCTwIfAf24cCNwCjJT0LfGD7K8DlwDm2X+2tt/YMs6TTgR9QnSF+HTjP9kpJVwDb2L6ozLd2urwW8FlgbFnuxLKdM4DNJE0G5tq+ZlPEJiIiIjYsSXBERAxX90n6b830XuX/z4EXbR8vaRdgsaTFwObAZGBv2x9L2r7MPxN4ynY3gKSdgN2AJ+qtVNL+wDXAeNvLJV0F/AI4eQDbPAH4MvAusAA41fYtkm6iJnmOiIiI1slw6IiIGK5OsN3d+we8VMonAzcD2F4O/BY4AngDGAXcKum0furt2sB6jwDml7op65o8wG1eYPsd2z1USfaeA1wuIiIihkiS4IiIaEc9fadtvwvsB9wDHAAskbTeNb62VwD/ACY2qLurXv3l/2rW/e38dJ/5as9cryEjriIiIoadJMEREdFuHgWmA5Qk9xjgcUk7AlvZfhiYRTUkeSzwHrC1pNqE9Crgeklrz9RKOkrSwcBjwDE1CfS5ZZ1QXec7XtJmkrYFpg5wm9+julY4IiIiWixJcEREtJsLgAMlPQ8sBGbZXgLsDjwq6TngeeAhYJHtVcCdwAuS/ghg+2bgWqrrjl+R9BJwJvBWqWs2sLCs40DgwrLu+4FVwBLgLuDpAW7zg8AESc9KmvXJPn5ERER8El09PX1HfEVERERERESMTDkTHBERERERER0jSXBERERERER0jCTBERERERER0TGSBEdERERERETHSBIcERERERERHSNJcERERERERHSMJMERERERERHRMf4HpC//H+iXbJ4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0344a75780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='HostCount', y='SourceCount', data=winlogonquery_df,\n",
    "fit_reg=False,hue='AccountName', size=6, aspect=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "There is inconsistent faied logons observed with account `SRVACCNT-01` and `SRVACCNT-02` which can be further investigated for any possible successful bruteforce activity.\n",
    "- From the heatmaps, high number of failed logons were observed in first 4-6 days for 2 accounts and later it significantly reduced.  \n",
    "- Scatterplot also shows for `SRVACCNT-01` and `SRVACCNT-02` , failed logons were observed from high number of source addresses as compared to other accounts or for same accounts for the period of 7 days.\n",
    "\n",
    "### Next Steps:\n",
    " - Investigate the the accounts- `SRVACCNT01` and `SRVACCNT-02`.\n",
    " - Load the logon activity for both accounts prior and after the logon trend change. you can have parameterized query for the accounts against logondataset to pull and display the details.\n",
    " - Check the succesful logons observed from the accounts and investigate activity observed from the accounts for any suspicious activities e.g. process execution, group changes, scheduled tasks, service installations etc. This can also be achieved with follow-up query for all events observed from the accounts for the mentioned days in next cell.\n",
    " - Based on the displayed cells output investigation, you can conclude if it is malicious activity or not."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:py35]",
   "language": "python",
   "name": "conda-env-py35-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}