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    "# Title: IP Explorer\n",
    "<details>\n",
    "    <summary> <u>Details...</u></summary>\n",
    "    \n",
    "**Notebook Version:** 1.0<br>\n",
    "**Python Version:** Python 3.7 (including Python 3.6 - AzureML)<br>\n",
    "**Required Packages**: kqlmagic, msticpy, pandas, numpy, matplotlib, networkx, ipywidgets, ipython, scikit_learn, dnspython, ipwhois, folium, holoviews<br>\n",
    "**Platforms Supported**:\n",
    "- Azure Notebooks Free Compute\n",
    "- Azure Notebooks DSVM\n",
    "- OS Independent\n",
    "\n",
    "**Data Sources Required**:\n",
    "- Log Analytics \n",
    "    - Heartbeat\n",
    "    - SecurityAlert\n",
    "    - SecurityEvent\n",
    "    - AzureNetworkAnalytics_CL\n",
    "    \n",
    "- (Optional)  \n",
    "    - VirusTotal (with API key)\n",
    "    - Alienvault OTX (with API key) \n",
    "    - IBM Xforce (with API key) \n",
    "    - CommonSecurityLog\n",
    "&lt;/details&gt;\n",
    "\n",
    "\n",
    "Brings together a series of queries and visualizations to help you assess the security state of an IP address. It works with both internal addresses and public addresses. \n",
    "<br> For internal addresses it focuses on traffic patterns and behavior of the host using that IP address. For public IPs it lets you perform threat intelligence lookups, passive dns, whois and other checks. \n",
    "<br>It also allows you to examine any network traffic between the external IP address and your resources."
   ]
  },
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   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Hunting-Hypothesis\" data-toc-modified-id=\"Hunting-Hypothesis-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Hunting Hypothesis</a></span><ul class=\"toc-item\"><li><span><a href=\"#IP-Explorer-Mindmap\" data-toc-modified-id=\"IP-Explorer-Mindmap-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>IP Explorer Mindmap</a></span></li></ul></li><li><span><a href=\"#Notebook-Initialization\" data-toc-modified-id=\"Notebook-Initialization-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Notebook Initialization</a></span><ul class=\"toc-item\"><li><span><a href=\"#Get-WorkspaceId-and-Authenticate-to-Log-Analytics\" data-toc-modified-id=\"Get-WorkspaceId-and-Authenticate-to-Log-Analytics-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Get WorkspaceId and Authenticate to Log Analytics</a></span></li></ul></li><li><span><a href=\"#Enter-the-IP-Address-and-query-time-window\" data-toc-modified-id=\"Enter-the-IP-Address-and-query-time-window-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Enter the IP Address and query time window</a></span></li><li><span><a href=\"#Detemine-IP-Address-Type\" data-toc-modified-id=\"Detemine-IP-Address-Type-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Detemine IP Address Type</a></span></li><li><span><a href=\"#External-IP\" data-toc-modified-id=\"External-IP-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>External IP</a></span><ul class=\"toc-item\"><li><span><a href=\"#GeoIP-Lookups-for-External-IP-Addresses\" data-toc-modified-id=\"GeoIP-Lookups-for-External-IP-Addresses-5.1\"><span class=\"toc-item-num\">5.1&nbsp;&nbsp;</span>GeoIP Lookups for External IP Addresses</a></span></li><li><span><a href=\"#Whois-Registrars-for-External-IP-Addresses\" data-toc-modified-id=\"Whois-Registrars-for-External-IP-Addresses-5.2\"><span class=\"toc-item-num\">5.2&nbsp;&nbsp;</span>Whois Registrars for External IP Addresses</a></span></li><li><span><a href=\"#Opensource-and-Azure-Sentinel-ThreatIntel-Lookups\" data-toc-modified-id=\"Opensource-and-Azure-Sentinel-ThreatIntel-Lookups-5.3\"><span class=\"toc-item-num\">5.3&nbsp;&nbsp;</span>Opensource and Azure Sentinel ThreatIntel Lookups</a></span><ul class=\"toc-item\"><li><span><a href=\"#Configure-your-TI-Provider-settings\" data-toc-modified-id=\"Configure-your-TI-Provider-settings-5.3.1\"><span class=\"toc-item-num\">5.3.1&nbsp;&nbsp;</span>Configure your TI Provider settings</a></span></li></ul></li><li><span><a href=\"#Passive-DNS-lookups-for-External-IP-Addresses\" data-toc-modified-id=\"Passive-DNS-lookups-for-External-IP-Addresses-5.4\"><span class=\"toc-item-num\">5.4&nbsp;&nbsp;</span>Passive DNS lookups for External IP Addresses</a></span></li></ul></li><li><span><a href=\"#Internal-IP-Address\" data-toc-modified-id=\"Internal-IP-Address-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Internal IP Address</a></span><ul class=\"toc-item\"><li><span><a href=\"#Data-Sources-available-to-query-related-to-IP\" data-toc-modified-id=\"Data-Sources-available-to-query-related-to-IP-6.1\"><span class=\"toc-item-num\">6.1&nbsp;&nbsp;</span>Data Sources available to query related to IP</a></span></li><li><span><a href=\"#Check-if-IP-is-assigned-to-multiple-hostnames\" data-toc-modified-id=\"Check-if-IP-is-assigned-to-multiple-hostnames-6.2\"><span class=\"toc-item-num\">6.2&nbsp;&nbsp;</span>Check if IP is assigned to multiple hostnames</a></span></li><li><span><a href=\"#System-Info\" data-toc-modified-id=\"System-Info-6.3\"><span class=\"toc-item-num\">6.3&nbsp;&nbsp;</span>System Info</a></span></li><li><span><a href=\"#ServiceMap---Get-List-of-Services-for-Host\" data-toc-modified-id=\"ServiceMap---Get-List-of-Services-for-Host-6.4\"><span class=\"toc-item-num\">6.4&nbsp;&nbsp;</span>ServiceMap - Get List of Services for Host</a></span></li></ul></li><li><span><a href=\"#Related-Alerts\" data-toc-modified-id=\"Related-Alerts-7\"><span class=\"toc-item-num\">7&nbsp;&nbsp;</span>Related Alerts</a></span><ul class=\"toc-item\"><li><span><a href=\"#Visualization---Timeline-of-Related-Alerts\" data-toc-modified-id=\"Visualization---Timeline-of-Related-Alerts-7.1\"><span class=\"toc-item-num\">7.1&nbsp;&nbsp;</span>Visualization - Timeline of Related Alerts</a></span></li><li><span><a href=\"#Browse-List-of-Related-Alerts\" data-toc-modified-id=\"Browse-List-of-Related-Alerts-7.2\"><span class=\"toc-item-num\">7.2&nbsp;&nbsp;</span>Browse List of Related Alerts</a></span></li></ul></li><li><span><a href=\"#Related-Hosts\" data-toc-modified-id=\"Related-Hosts-8\"><span class=\"toc-item-num\">8&nbsp;&nbsp;</span>Related Hosts</a></span><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><span><a href=\"#NOTE---the-following-sections-are-only-relevant-for-Internal-IP-Addresses.\" data-toc-modified-id=\"NOTE---the-following-sections-are-only-relevant-for-Internal-IP-Addresses.-8.0.1\"><span class=\"toc-item-num\">8.0.1&nbsp;&nbsp;</span><strong>NOTE - the following sections are only relevant for Internal IP Addresses.</strong></a></span></li></ul></li><li><span><a href=\"#Visualization---Networkx-Graph\" data-toc-modified-id=\"Visualization---Networkx-Graph-8.1\"><span class=\"toc-item-num\">8.1&nbsp;&nbsp;</span>Visualization - Networkx Graph</a></span></li></ul></li><li><span><a href=\"#Related-Accounts\" data-toc-modified-id=\"Related-Accounts-9\"><span class=\"toc-item-num\">9&nbsp;&nbsp;</span>Related Accounts</a></span><ul class=\"toc-item\"><li><span><a href=\"#Visualization---Networkx-Graph\" data-toc-modified-id=\"Visualization---Networkx-Graph-9.1\"><span class=\"toc-item-num\">9.1&nbsp;&nbsp;</span>Visualization - Networkx Graph</a></span></li></ul></li><li><span><a href=\"#Logon-Summary-for-Related-Entities\" data-toc-modified-id=\"Logon-Summary-for-Related-Entities-10\"><span class=\"toc-item-num\">10&nbsp;&nbsp;</span>Logon Summary for Related Entities</a></span><ul class=\"toc-item\"><li><span><a href=\"#HeatMap-for-Weekly-failed-logons\" data-toc-modified-id=\"HeatMap-for-Weekly-failed-logons-10.1\"><span class=\"toc-item-num\">10.1&nbsp;&nbsp;</span>HeatMap for Weekly failed logons</a></span></li><li><span><a href=\"#Host-Logons-Timeline\" data-toc-modified-id=\"Host-Logons-Timeline-10.2\"><span class=\"toc-item-num\">10.2&nbsp;&nbsp;</span>Host Logons Timeline</a></span></li><li><span><a href=\"#Failed-Logons-Timeline\" data-toc-modified-id=\"Failed-Logons-Timeline-10.3\"><span class=\"toc-item-num\">10.3&nbsp;&nbsp;</span>Failed Logons Timeline</a></span></li></ul></li><li><span><a href=\"#Network-Connection-Analysis\" data-toc-modified-id=\"Network-Connection-Analysis-11\"><span class=\"toc-item-num\">11&nbsp;&nbsp;</span>Network Connection Analysis</a></span><ul class=\"toc-item\"><li><span><a href=\"#Network-Check-Communications-with-Other-Hosts\" data-toc-modified-id=\"Network-Check-Communications-with-Other-Hosts-11.1\"><span class=\"toc-item-num\">11.1&nbsp;&nbsp;</span>Network Check Communications with Other Hosts</a></span></li><li><span><a href=\"#Query-Flows-by-IP-Address\" data-toc-modified-id=\"Query-Flows-by-IP-Address-11.2\"><span class=\"toc-item-num\">11.2&nbsp;&nbsp;</span>Query Flows by IP Address</a></span></li><li><span><a href=\"#Bulk-whois-lookup\" data-toc-modified-id=\"Bulk-whois-lookup-11.3\"><span class=\"toc-item-num\">11.3&nbsp;&nbsp;</span>Bulk whois lookup</a></span></li><li><span><a href=\"#Choose-ASNs/IPs-to-Check-for-Threat-Intel-Reports\" data-toc-modified-id=\"Choose-ASNs/IPs-to-Check-for-Threat-Intel-Reports-11.4\"><span class=\"toc-item-num\">11.4&nbsp;&nbsp;</span>Choose ASNs/IPs to Check for Threat Intel Reports</a></span></li><li><span><a href=\"#GeoIP-Map-of-External-IPs\" data-toc-modified-id=\"GeoIP-Map-of-External-IPs-11.5\"><span class=\"toc-item-num\">11.5&nbsp;&nbsp;</span>GeoIP Map of External IPs</a></span></li><li><span><a href=\"#Outbound-Data-transfer-Time-Series-Anomalies\" data-toc-modified-id=\"Outbound-Data-transfer-Time-Series-Anomalies-11.6\"><span class=\"toc-item-num\">11.6&nbsp;&nbsp;</span>Outbound Data transfer Time Series Anomalies</a></span></li></ul></li><li><span><a href=\"#Conclusion\" data-toc-modified-id=\"Conclusion-12\"><span class=\"toc-item-num\">12&nbsp;&nbsp;</span>Conclusion</a></span><ul class=\"toc-item\"><li><span><a href=\"#List-of-Suspicious-Activities/-Observables/Hunting-bookmarks\" data-toc-modified-id=\"List-of-Suspicious-Activities/-Observables/Hunting-bookmarks-12.1\"><span class=\"toc-item-num\">12.1&nbsp;&nbsp;</span>List of Suspicious Activities/ Observables/Hunting bookmarks</a></span></li></ul></li><li><span><a href=\"#Appendices\" data-toc-modified-id=\"Appendices-13\"><span class=\"toc-item-num\">13&nbsp;&nbsp;</span>Appendices</a></span></li><li><span><a href=\"#Available-DataFrames\" data-toc-modified-id=\"Available-DataFrames-14\"><span class=\"toc-item-num\">14&nbsp;&nbsp;</span>Available DataFrames</a></span><ul class=\"toc-item\"><li><span><a href=\"#Saving-Data-to-Excel\" data-toc-modified-id=\"Saving-Data-to-Excel-14.1\"><span class=\"toc-item-num\">14.1&nbsp;&nbsp;</span>Saving Data to Excel</a></span></li></ul></li><li><span><a href=\"#Configuration\" data-toc-modified-id=\"Configuration-15\"><span class=\"toc-item-num\">15&nbsp;&nbsp;</span>Configuration</a></span><ul class=\"toc-item\"><li><span><a href=\"#msticpyconfig.yaml-configuration-File\" data-toc-modified-id=\"msticpyconfig.yaml-configuration-File-15.1\"><span class=\"toc-item-num\">15.1&nbsp;&nbsp;</span><code>msticpyconfig.yaml</code> configuration File</a></span></li></ul></li></ul></div>"
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   "source": [
    "<a></a>[Contents](#toc)\n",
    "## Hunting Hypothesis\n",
    "Our broad initial hunting hypothesis is that a we have received IP address entity which is suspected to be compromized internal host or external public address to whom internal hosts are communicating in malicious manner, we will need to hunt from a range of different positions to validate or disprove this hypothesis.\n",
    "\n",
    "Before you start hunting please run the cells in <a>Setup</a> at the bottom of this Notebook."
   ]
  },
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a></a>[Contents](#toc)\n",
    "### IP Explorer Mindmap\n",
    "Below mindmap diagram shows hunting workflow depending upon the type of IP address provided\n",
    "\n",
    "![ipexplorer-mindmapv2.PNG](attachment:ipexplorer-mindmapv2.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Notebook initialization\n",
    "The next cell:\n",
    "- Checks for the correct Python version\n",
    "- Checks versions and optionally installs required packages\n",
    "- Imports the required packages into the notebook\n",
    "- Sets a number of configuration options.\n",
    "\n",
    "This should complete without errors. If you encounter errors or warnings look at the following two notebooks:\n",
    "- [TroubleShootingNotebooks](https://github.com/Azure/Azure-Sentinel-Notebooks/blob/master/TroubleShootingNotebooks.ipynb)\n",
    "- [ConfiguringNotebookEnvironment](https://github.com/Azure/Azure-Sentinel-Notebooks/blob/master/ConfiguringNotebookEnvironment.ipynb)\n",
    "\n",
    "If you are running in the Azure Sentinel Notebooks environment (Azure Notebooks or Azure ML) you can run live versions of these notebooks:\n",
    "- [Run TroubleShootingNotebooks](./TroubleShootingNotebooks.ipynb)\n",
    "- [Run ConfiguringNotebookEnvironment](./ConfiguringNotebookEnvironment.ipynb)\n",
    "\n",
    "You may also need to do some additional configuration to successfully use functions such as Threat Intelligence service lookup and Geo IP lookup. \n",
    "There are more details about this in the `ConfiguringNotebookEnvironment` notebook and in these documents:\n",
    "- [msticpy configuration](https://msticpy.readthedocs.io/en/latest/getting_started/msticpyconfig.html)\n",
    "- [Threat intelligence provider configuration](https://msticpy.readthedocs.io/en/latest/data_acquisition/TIProviders.html#configuration-file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-15T23:01:51.949751Z",
     "start_time": "2020-05-15T23:01:51.909753Z"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import os\n",
    "import sys\n",
    "import warnings\n",
    "from IPython.display import display, HTML, Markdown\n",
    "\n",
    "REQ_PYTHON_VER=(3, 6)\n",
    "REQ_MSTICPY_VER=(0, 5, 0)\n",
    "\n",
    "display(HTML(\"<h3>Starting Notebook setup...</h3>\"))\n",
    "if Path(\"./utils/nb_check.py\").is_file():\n",
    "    from utils.nb_check import check_python_ver, check_mp_ver\n",
    "\n",
    "    check_python_ver(min_py_ver=REQ_PYTHON_VER)\n",
    "    try:\n",
    "        check_mp_ver(min_msticpy_ver=REQ_MSTICPY_VER)\n",
    "    except ImportError:\n",
    "        !pip install --user --upgrade msticpy\n",
    "        if \"msticpy\" in sys.modules:\n",
    "            importlib.reload(msticpy)\n",
    "        else:\n",
    "            import msticpy\n",
    "        check_mp_ver(REQ_PYTHON_VER)\n",
    "            \n",
    "from msticpy.nbtools import nbinit\n",
    "extra_imports = [\n",
    "    \"msticpy.nbtools.entityschema, IpAddress\",\n",
    "    \"msticpy.nbtools.entityschema, GeoLocation\",\n",
    "    \"msticpy.sectools.ip_utils, create_ip_record\",\n",
    "    \"msticpy.sectools.ip_utils, get_ip_type\",\n",
    "    \"msticpy.sectools.ip_utils, get_whois_info\",\n",
    "]\n",
    "nbinit.init_notebook(\n",
    "    namespace=globals(),\n",
    "    extra_imports=extra_imports,\n",
    ");\n",
    "WIDGET_DEFAULTS = {\n",
    "    \"layout\": widgets.Layout(width=\"95%\"),\n",
    "    \"style\": {\"description_width\": \"initial\"},\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a></a>[Contents](#toc)\n",
    "### Get WorkspaceId and Authenticate to Log Analytics \n",
    "&lt;details&gt;\n",
    "    <summary> <u>Details...</u></summary>\n",
    "If you are using user/device authentication, run the following cell. \n",
    "- Click the 'Copy code to clipboard and authenticate' button.\n",
    "- This will pop up an Azure Active Directory authentication dialog (in a new tab or browser window). The device code will have been copied to the clipboard. \n",
    "- Select the text box and paste (Ctrl-V/Cmd-V) the copied value. \n",
    "- You should then be redirected to a user authentication page where you should authenticate with a user account that has permission to query your Log Analytics workspace.\n",
    "\n",
    "Use the following syntax if you are authenticating using an Azure Active Directory AppId and Secret:\n",
    "```\n",
    "%kql loganalytics://tenant(aad_tenant).workspace(WORKSPACE_ID).clientid(client_id).clientsecret(client_secret)\n",
    "```\n",
    "instead of\n",
    "```\n",
    "%kql loganalytics://code().workspace(WORKSPACE_ID)\n",
    "```\n",
    "\n",
    "Note: you may occasionally see a JavaScript error displayed at the end of the authentication - you can safely ignore this.<br>\n",
    "On successful authentication you should see a ```popup schema``` button.\n",
    "To find your Workspace Id go to [Log Analytics](https://ms.portal.azure.com/#blade/HubsExtension/Resources/resourceType/Microsoft.OperationalInsights%2Fworkspaces). Look at the workspace properties to find the ID.\n",
    "&lt;/details&gt;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-15T23:02:52.662562Z",
     "start_time": "2020-05-15T23:02:52.653563Z"
    }
   },
   "outputs": [],
   "source": [
    "#See if we have an Azure Sentinel Workspace defined in our config file, if not let the user specify Workspace and Tenant IDs\n",
    "from msticpy.nbtools.wsconfig import WorkspaceConfig\n",
    "ws_config = WorkspaceConfig()\n",
    "try:\n",
    "    ws_id = ws_config['workspace_id']\n",
    "    ten_id = ws_config['tenant_id']\n",
    "    config = True\n",
    "    md(\"Workspace details collected from config file\")\n",
    "except KeyError:\n",
    "    md(('Please go to your Log Analytics workspace, copy the workspace ID'\n",
    "                 ' and/or tenant Id and paste here to enable connection to the workspace and querying of it..<br> '))\n",
    "    ws_id_wgt = nbwidgets.GetEnvironmentKey(env_var='WORKSPACE_ID',\n",
    "                                        prompt='Please enter your Log Analytics Workspace Id:', auto_display=True)\n",
    "    ten_id_wgt = nbwidgets.GetEnvironmentKey(env_var='TENANT_ID',\n",
    "                                         prompt='Please enter your Log Analytics Tenant Id:', auto_display=True)\n",
    "    config = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-15T23:03:22.552179Z",
     "start_time": "2020-05-15T23:02:56.043852Z"
    }
   },
   "outputs": [],
   "source": [
    "# Authentication\n",
    "qry_prov = QueryProvider(data_environment=\"LogAnalytics\")\n",
    "qry_prov.connect(connection_str=ws_config.code_connect_str)\n",
    "table_index = qry_prov.schema_tables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a></a>[Contents](#toc)\n",
    "## Enter the IP Address and query time window\n",
    "\n",
    "Type the IP address you want to search for and the time bounds over which search.\n",
    "\n",
    "You can specify the IP address value in the widget e.g. 192.168.1.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-15T23:03:22.632179Z",
     "start_time": "2020-05-15T23:03:22.619179Z"
    }
   },
   "outputs": [],
   "source": [
    "ipaddr_text = widgets.Text(\n",
    "    description=\"Enter the IP Address to search for:\", **WIDGET_DEFAULTS\n",
    ")\n",
    "display(ipaddr_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-15T23:03:56.698491Z",
     "start_time": "2020-05-15T23:03:56.631491Z"
    }
   },
   "outputs": [],
   "source": [
    "query_times = nbwidgets.QueryTime(units=\"day\", max_before=20, before=5, max_after=7)\n",
    "query_times.display()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-15T23:04:05.784278Z",
     "start_time": "2020-05-15T23:04:05.776278Z"
    }
   },
   "outputs": [],
   "source": [
    "# Set up function to allow easy reference to common parameters for queries throughout the notebook\n",
    "def ipaddr_query_params():\n",
    "    return {\n",
    "        \"start\": query_times.start,\n",
    "        \"end\": query_times.end,\n",
    "        \"ip_address\": ipaddr_text.value.strip()\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a></a>[Contents](#toc)\n",
    "## Detemine IP Address Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-15T23:04:47.927548Z",
     "start_time": "2020-05-15T23:04:43.963316Z"
    }
   },
   "outputs": [],
   "source": [
    "ipaddr_type = get_ip_type(ipaddr_query_params()['ip_address'])\n",
    "\n",
    "md(f'Depending on the IP Address origin, different sections of this notebook are applicable', styles=[\"bold\", \"large\"])\n",
    "md(f'Please follow either the Interal IP Address or External IP Address sections based on below Recommendation', styles=[\"bold\"])\n",
    "\n",
    "#Get details from Heartbeat table for the given IP Address and Time Parameters\n",
    "heartbeat_df = qry_prov.Heartbeat.get_info_by_ipaddress(**ipaddr_query_params())\n",
    "\n",
    "# Set hostnames retrived from Heartbeat table if available\n",
    "if not heartbeat_df.empty:\n",
    "    hostname = heartbeat_df[\"Computer\"][0]\n",
    "else:\n",
    "    hostname = \"\"\n",
    "    \n",
    "if not heartbeat_df.empty:\n",
    "    ipaddr_origin = \"Internal\"\n",
    "    md(f'IP Address type based on subnet: {ipaddr_type} & IP Address Owner based on available logs : {ipaddr_origin}', styles=[\"blue\",\"bold\"])\n",
    "    display(Markdown('#### Recommendation - Go to section [InternalIP](#goto_internalIP)'))\n",
    "elif ipaddr_type==\"Private\" and heartbeat_df.empty:\n",
    "    ipaddr_origin = \"Unknown\"\n",
    "    md(f'IP Address type based on subnet: {ipaddr_type} & IP Address Owner based on available logs : {ipaddr_origin}', styles=[\"blue\",\"bold\"])\n",
    "    display(Markdown('#### Recommendation - Go to section [InternalIP](#goto_internalIP)'))\n",
    "else:\n",
    "    ipaddr_origin = \"External\"\n",
    "    md(f'IP Address type based on subnet: {ipaddr_type} & IP Address Owner based on available logs : {ipaddr_origin}', styles=[\"blue\",\"bold\"])\n",
    "    display(Markdown('#### Recommendation - Go to section [ExternalIP](#goto_externalIP)'))\n",
    "    \n",
    "#Populate related IP addresses for the calculated hostname\n",
    "az_net_df = pd.DataFrame()\n",
    "if \"AzureNetworkAnalytics_CL\" in table_index:\n",
    "    aznet_query = f\"\"\"\n",
    "    AzureNetworkAnalytics_CL | where ResourceType == 'NetworkInterface' \n",
    "    | where SubType_s == \"Topology\" \n",
    "    | search \\'{ipaddr_text.value}\\' \n",
    "    | where TimeGenerated >= datetime({query_times.start}) \n",
    "    | where TimeGenerated <= datetime({query_times.end}) \n",
    "    | where VirtualMachine_s has '{hostname}' \n",
    "    | top 1 by TimeGenerated desc \n",
    "    | project PrivateIPAddresses = PrivateIPAddresses_s, PublicIPAddresses = PublicIPAddresses_s\"\"\"\n",
    "    az_net_df = qry_prov.exec_query(query=aznet_query)\n",
    "    \n",
    "# Create IP Entity record using available dataframes or input ip address if nothing present\n",
    "if az_net_df.empty and heartbeat_df.empty:\n",
    "    ip_entity = IpAddress()\n",
    "    ip_entity['Address'] = ipaddr_query_params()['ip_address']\n",
    "    ip_entity['Type'] = 'ipaddress'\n",
    "    ip_entity['OSType'] = 'Unknown'\n",
    "    md('No Heartbeat Data and Network topology data found')\n",
    "elif not heartbeat_df.empty:\n",
    "    if az_net_df.empty:\n",
    "        ip_entity = create_ip_record(\n",
    "            heartbeat_df=heartbeat_df)\n",
    "    else:\n",
    "        ip_entity = create_ip_record(\n",
    "            heartbeat_df=heartbeat_df, az_net_df=az_net_df)\n",
    "#Display IP Entity\n",
    "md(\"Displaying IP Entity\", styles=[\"green\",\"bold\"])\n",
    "print(ip_entity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='goto_externalIP'></a>\n",
    "## External IP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a></a>[Contents](#toc)\n",
    "### GeoIP Lookups for External IP Addresses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-27T08:33:37.478812Z",
     "start_time": "2020-04-27T08:33:37.470173Z"
    }
   },
   "outputs": [],
   "source": [
    "# msticpy- geoip module to retrieving Geo Location for Public IP addresses\n",
    "# To force Threatinel lookup for Internal public IP, replace and with or in if condition\n",
    "if ipaddr_type == \"Public\" and ipaddr_origin == \"External\" :\n",
    "    iplocation = GeoLiteLookup()\n",
    "\n",
    "    loc_results, ext_ip_entity = iplocation.lookup_ip(ip_address=ipaddr_query_params()['ip_address'])\n",
    "    md(\n",
    "            'Geo Location for the IP Address ::', styles=[\"bold\",\"green\"]\n",
    "        )\n",
    "    print(ext_ip_entity[0])\n",
    "else:\n",
    "    md(f'Analysis section Not Applicable since IP address owner is {ipaddr_origin}', styles=[\"bold\",\"red\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a></a>[Contents](#toc)\n",
    "### Whois Registrars for External IP Addresses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-27T08:33:39.572115Z",
     "start_time": "2020-04-27T08:33:39.566009Z"
    }
   },
   "outputs": [],
   "source": [
    "# ipwhois module to retrieve whois registrar for Public IP addresses\n",
    "# To force Threatinel lookup for Internal public IP, replace and with or in if condition\n",
    "if ipaddr_type == \"Public\" and ipaddr_origin == \"External\" :\n",
    "    from ipwhois import IPWhois\n",
    "\n",
    "    whois = IPWhois(ipaddr_query_params()['ip_address'])\n",
    "    whois_result = whois.lookup_whois()\n",
    "    if whois_result:\n",
    "        md(f'Whois Registrar Info ::', styles=[\"bold\",\"green\"])\n",
    "        display(whois_result)\n",
    "    else:\n",
    "        md(\n",
    "                f'No whois records available', styles=[\"bold\",\"orange\"]\n",
    "            )\n",
    "else:\n",
    "    md(f'Analysis section Not Applicable since IP address owner is {ipaddr_origin}', styles=[\"bold\",\"red\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a></a>[Contents](#toc)\n",
    "### Opensource and Azure Sentinel ThreatIntel Lookups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Configure your TI Provider settings\n",
    "If you have not used threat intelligence lookups before you will need to supply API keys for the \n",
    "TI Providers that you want to use. Please see the section on configuring [msticpyconfig.yaml](#msticpyconfig.yaml-configuration-File)\n",
    "\n",
    "Then reload provider settings:\n",
    "```\n",
    "mylookup = TILookup()\n",
    "mylookup.reload_provider_settings()\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-27T08:33:43.562087Z",
     "start_time": "2020-04-27T08:33:43.554830Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# To force Threatinel lookup for Internal public IP, replace and with or in if condition\n",
    "if ipaddr_type == \"Public\" and ipaddr_origin == \"External\" :\n",
    "    mylookup = TILookup()\n",
    "    mylookup.loaded_providers\n",
    "    resp = mylookup.lookup_ioc(observable=ipaddr_query_params()['ip_address'], ioc_type=\"ipv4\")\n",
    "    md(f'ThreatIntel Lookup for IP ::', styles=[\"bold\",\"green\"])\n",
    "    display(mylookup.result_to_df(resp).T)\n",
    "else:\n",
    "    md(f'Analysis section Not Applicable since IP address owner is {ipaddr_origin}', styles=[\"bold\",\"red\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a></a>[Contents](#toc)\n",
    "### Passive DNS lookups for External IP Addresses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-27T08:33:45.838706Z",
     "start_time": "2020-04-27T08:33:45.829919Z"
    }
   },
   "outputs": [],
   "source": [
    "# To force Passive DNS lookup for Internal public IP, change and with or in if\n",
    "if ipaddr_type == \"Public\" and ipaddr_origin == \"External\" :\n",
    "    # retrieve passive dns from TI Providers\n",
    "    pdns = mylookup.lookup_ioc(\n",
    "        observable=ipaddr_query_params()['ip_address'],\n",
    "        ioc_type=\"ipv4\",\n",
    "        ioc_query_type=\"passivedns\",\n",
    "        providers=[\"XForce\"],\n",
    "    )\n",
    "    pdns_df = mylookup.result_to_df(pdns)\n",
    "    if not pdns_df.empty and pdns_df[\"RawResult\"][0] and \"RDNS\" in pdns_df[\"RawResult\"][0]:\n",
    "        pdnsdomains = pdns_df[\"RawResult\"][0][\"RDNS\"]\n",
    "        md(\n",
    "                'Passive DNS domains for IP: {pdnsdomains}',styles=[\"bold\",\"green\"]\n",
    "            )\n",
    "        display(mylookup.result_to_df(pdns).T)\n",
    "    else:\n",
    "        md(\n",
    "                'No passive domains found from the providers', styles=[\"bold\",\"orange\"]\n",
    "            )\n",
    "else:\n",
    "     md(f'Analysis section Not Applicable since IP address owner is {ipaddr_origin}', styles=[\"bold\",\"red\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='goto_internalIP'></a>\n",
    "## Internal IP Address"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a></a>[Contents](#toc)\n",
    "### Data Sources available to query related to IP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-15T23:04:59.773853Z",
     "start_time": "2020-05-15T23:04:53.039482Z"
    }
   },
   "outputs": [],
   "source": [
    "if ipaddr_origin in [\"Internal\",\"Unknown\"]:\n",
    "    # KQL query for full text search of IP address and display all datatypes populated for the time period\n",
    "    datasource_status = \"\"\"\n",
    "    search \\'{ip_address}\\' or \\'{hostname}\\'\n",
    "    | where TimeGenerated >= datetime({start}) and TimeGenerated <= datetime({end})\n",
    "    | summarize RowCount=count() by Table=$table\n",
    "    \"\"\".format(\n",
    "        **ipaddr_query_params(), hostname=hostname\n",
    "    )\n",
    "    %kql -query datasource_status\n",
    "    datasource_status_df = _kql_raw_result_.to_dataframe()\n",
    "\n",
    "    # Display result as transposed matrix of datatypes availabel to query for the query period\n",
    "    if not datasource_status_df.empty:\n",
    "        available_datasets = datasource_status_df['Table'].values\n",
    "        md(\"Datasources available to query for IP ::\", styles=[\"green\",\"bold\"])\n",
    "        display(datasource_status_df)\n",
    "    else:\n",
    "        md_warn(\"No datasources contain given IP address for the query period\")\n",
    "else:\n",
    "    md(f'Analysis section Not Applicable since IP address type is: {ipaddr_type}', styles=[\"bold\",\"red\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a></a>[Contents](#toc)\n",
    "### Check if IP is assigned to multiple hostnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-15T23:05:03.895367Z",
     "start_time": "2020-05-15T23:05:02.486243Z"
    }
   },
   "outputs": [],
   "source": [
    "if ipaddr_origin == \"Internal\" or not datasource_status_df.empty:\n",
    "    # Get single event - try process creation\n",
    "    if ip_entity['OSType'] =='Windows':\n",
    "        if \"SecurityEvent\" not in available_datasets:\n",
    "            raise ValueError(\"No Windows event log data available in the workspace\")\n",
    "        host_name = None\n",
    "        matching_hosts_df = qry_prov.WindowsSecurity.list_host_processes(\n",
    "            query_times, host_name=hostname, add_query_items=\"| distinct Computer\"\n",
    "        )\n",
    "    elif ip_entity['OSType'] =='Linux':\n",
    "        if \"Syslog\" not in available_datasets:\n",
    "            raise ValueError(\"No Linux syslog data available in the workspace\")\n",
    "        else:\n",
    "            linux_syslog_query = f\"\"\" Syslog | where TimeGenerated >= datetime({query_times.start}) | where TimeGenerated <= datetime({query_times.end}) | where HostIP == '{ipaddr_text.value}' | distinct Computer \"\"\"\n",
    "            matching_hosts_df = qry_prov.exec_query(query=linux_syslog_query)\n",
    "\n",
    "    if len(matching_hosts_df) > 1:\n",
    "        print(f\"Multiple matches for '{hostname}'. Please select a host from the list.\")\n",
    "        choose_host = nbwidgets.SelectString(\n",
    "            item_list=list(matching_hosts_df[\"Computer\"].values),\n",
    "            description=\"Select the host.\",\n",
    "            auto_display=True,\n",
    "        )\n",
    "    elif not matching_hosts_df.empty:\n",
    "        host_name = matching_hosts_df[\"Computer\"].iloc[0]\n",
    "        print(f\"Unique host found for IP: {hostname}\")\n",
    "elif datasource_status_df.empty:\n",
    "    md_warn(\"No datasources contain given IP address for the query period\")\n",
    "else:   \n",
    "    md(f'Analysis section Not Applicable since IP address type is : {ipaddr_type}', styles=[\"bold\",\"red\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a></a>[Contents](#toc)\n",
    "### System Info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-15T23:05:07.346683Z",
     "start_time": "2020-05-15T23:05:07.330684Z"
    }
   },
   "outputs": [],
   "source": [
    "# Retrieving System info from internal table if IP address is not Public\n",
    "if ipaddr_origin == \"Internal\" and not heartbeat_df.empty:\n",
    "    md(\n",
    "            'System Info retrieved from Heartbeat table ::', styles=[\"green\",\"bold\"]\n",
    "        )\n",
    "    display(heartbeat_df.T)\n",
    "else:\n",
    "        md_warn(\n",
    "            'No records available in HeartBeat table'\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Contents](#toc)\n",
    "### ServiceMap - Get List of Services for Host"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-15T23:05:10.389939Z",
     "start_time": "2020-05-15T23:05:10.369939Z"
    }
   },
   "outputs": [],
   "source": [
    "if ipaddr_origin == \"Internal\":\n",
    "    if \"ServiceMapProcess_CL\" not in available_datasets:\n",
    "            md_warn(\"ServiceMap data is not enabled\")\n",
    "            md(\n",
    "                f\"Enable ServiceMap Solution from Azure marketplce: <br>\"\n",
    "                +\"https://docs.microsoft.com/en-us/azure/azure-monitor/insights/service-map#enable-service-map\",\n",
    "                styles=[\"bold\"]\n",
    "            )\n",
    "\n",
    "    else:\n",
    "        servicemap_proc_query = \"\"\"\n",
    "        ServiceMapProcess_CL\n",
    "        | where Computer == \\'{hostname}\\'\n",
    "        | where TimeGenerated >= datetime({start}) and TimeGenerated <= datetime({end})\n",
    "        | project Computer, Services_s, DisplayName_s, ExecutableName_s , ExecutablePath_s \n",
    "            \"\"\".format(\n",
    "            hostname=hostname, **ipaddr_query_params()\n",
    "        )\n",
    "\n",
    "        %kql -query servicemap_proc_query\n",
    "        servicemap_proc_df = _kql_raw_result_.to_dataframe()\n",
    "        display(servicemap_proc_df)\n",
    "else:\n",
    "    md(f'Analysis section Not Applicable since IP address type is {ipaddr_type}', styles=[\"bold\",\"red\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Related Alerts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-15T23:05:14.185177Z",
     "start_time": "2020-05-15T23:05:14.123178Z"
    }
   },
   "outputs": [],
   "source": [
    "ra_query_times = nbwidgets.QueryTime(\n",
    "    units=\"day\",\n",
    "    origin_time=query_times.origin_time,\n",
    "    max_before=28,\n",
    "    max_after=5,\n",
    "    before=5,\n",
    "    auto_display=True,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization - Timeline of Related Alerts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-15T23:05:19.536611Z",
     "start_time": "2020-05-15T23:05:17.943028Z"
    }
   },
   "outputs": [],
   "source": [
    "#Provide hostname if present to the query\n",
    "if hostname:\n",
    "    md(f\"Searching for alerts related to {hostname}...\")\n",
    "    related_alerts = qry_prov.SecurityAlert.list_related_alerts(\n",
    "        ra_query_times, host_name=hostname\n",
    "    )\n",
    "else:\n",
    "    md(f\"Searching for alerts related to ip address(es) {ipaddr_query_params()['ip_address']}\")\n",
    "    related_alerts = qry_prov.SecurityAlert.list_alerts_for_ip(\n",
    "        ra_query_times, source_ip_list=ipaddr_query_params()['ip_address']\n",
    "    )\n",
    "\n",
    "\n",
    "def print_related_alerts(alertDict, entityType, entityName):\n",
    "    if len(alertDict) > 0:\n",
    "        md(\n",
    "                f\"Found {len(alertDict)} different alert types related to this {entityType} (`{entityName}`)\",styles=[\"bold\",\"orange\"]\n",
    "            )\n",
    "        for (k, v) in alertDict.items():\n",
    "            print(f\"- {k}, # Alerts: {v}\")\n",
    "    else:\n",
    "        print(f\"No alerts for {entityType} entity `{entityName}`\")\n",
    "\n",
    "\n",
    "if isinstance(related_alerts, pd.DataFrame) and not related_alerts.empty:\n",
    "    host_alert_items = (\n",
    "        related_alerts[[\"AlertName\", \"TimeGenerated\"]]\n",
    "        .groupby(\"AlertName\")\n",
    "        .TimeGenerated.agg(\"count\")\n",
    "        .to_dict()\n",
    "    )\n",
    "    print_related_alerts(host_alert_items, \"host\", hostname)\n",
    "    nbdisplay.display_timeline(\n",
    "        data=related_alerts, title=\"Alerts\", source_columns=[\"AlertName\"], height=200\n",
    "    )\n",
    "else:\n",
    "    md(\"No related alerts found.\",styles=[\"bold\",\"green\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### Browse List of Related Alerts\n",
    " Select an Alert to view details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-15T23:05:24.530275Z",
     "start_time": "2020-05-15T23:05:24.464277Z"
    }
   },
   "outputs": [],
   "source": [
    "def disp_full_alert(alert):\n",
    "    global related_alert\n",
    "    related_alert = SecurityAlert(alert)\n",
    "    nbdisplay.display_alert(related_alert, show_entities=True)\n",
    "\n",
    "recenter_wgt = widgets.Checkbox(\n",
    "    value=True,\n",
    "    description='Center subsequent query times round selected Alert?',\n",
    "    disabled=False,\n",
    "    **WIDGET_DEFAULTS\n",
    ")\n",
    "if related_alerts is not None and not related_alerts.empty:\n",
    "    related_alerts[\"CompromisedEntity\"] = related_alerts[\"Computer\"]\n",
    "    md(\"Click on alert to view details.\", styles=[\"bold\"])\n",
    "    display(recenter_wgt)\n",
    "    rel_alert_select = nbwidgets.AlertSelector(\n",
    "        alerts=related_alerts,\n",
    "        action=disp_full_alert,\n",
    "    )\n",
    "    rel_alert_select.display()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a></a>[Contents](#toc)\n",
    "## Related Hosts\n",
    "**Hypothesis:** That an attacker has gained  access to the host, compromized credentials for the accounts and laterally moving to the network gaining access to more hosts.\n",
    "\n",
    "This section provides related hosts of IP address which is being investigated. .If you wish to expand the scope of hunting then investigate each hosts in detail, it is recommended that to use the **Host Explorer Notebook (include link).**\n",
    "\n",
    "#### __NOTE - the following sections are only relevant for Internal IP Addresses.__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a></a>[Contents](#toc)\n",
    "### Visualization - Networkx Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-15T23:05:30.863302Z",
     "start_time": "2020-05-15T23:05:29.870080Z"
    }
   },
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "if ipaddr_origin == \"Internal\":\n",
    "    # Retrived relatd accounts from SecurityEvent table for Windows OS\n",
    "    if ip_entity['OSType'] =='Windows':\n",
    "        if \"SecurityEvent\" not in available_datasets:\n",
    "            raise ValueError(\"No Windows event log data available in the workspace\")\n",
    "        else:\n",
    "            related_hosts = \"\"\"\n",
    "               SecurityEvent\n",
    "               | where TimeGenerated >= datetime({start}) and TimeGenerated <= datetime({end})\n",
    "               | where IpAddress == \\'{ip_address}\\' or Computer == \\'{hostname}\\'      \n",
    "               | summarize count() by Computer, IpAddress\n",
    "            \"\"\".format(\n",
    "                **ipaddr_query_params(), hostname=hostname\n",
    "            )\n",
    "            %kql -query related_hosts\n",
    "            related_hosts_df = _kql_raw_result_.to_dataframe()\n",
    "\n",
    "    elif ip_entity['OSType'] =='Linux':\n",
    "        if \"Syslog\" not in available_datasets:\n",
    "            raise ValueError(\"No Linux syslog data available in the workspace\")\n",
    "        else:\n",
    "            related_hosts_df = qry_prov.LinuxSyslog.list_logons_for_source_ip(invest_times, ip_address=ipaddr_query_params()['ip_address'],add_query_items='extend IpAddress = HostIP | summarize count() by Computer, IpAddress')\n",
    "\n",
    "    # Displaying networkx - static graph. for interactive graph uncomment and run next block of code.\n",
    "    plt.figure(10, figsize=(22, 14))\n",
    "    g = nx.from_pandas_edgelist(related_hosts_df, \"IpAddress\", \"Computer\")\n",
    "    md('Entity Relationship Graph - Related Hosts :: ',styles=[\"bold\",\"green\"])\n",
    "    nx.draw_circular(g, with_labels=True, size=40, font_size=12, font_color=\"blue\")\n",
    "\n",
    "\n",
    "    # Uncomment below cells if you want to dispaly interactive graphs using Pyvis library, Azure notebook free tier may not render the graph correctly.\n",
    "    # logonpyvis_graph = Network(notebook=True, height=\"750px\", width=\"100%\", bgcolor=\"#222222\", font_color=\"white\")\n",
    "\n",
    "    # # set the physics layout of the network\n",
    "    # logonpyvis_graph.barnes_hut()\n",
    "\n",
    "    # sources = related_hosts_df['Computer']\n",
    "    # targets = related_hosts_df['IpAddress']\n",
    "    # weights = related_hosts_df['count_']\n",
    "\n",
    "    # edge_data = zip(sources, targets, weights)\n",
    "\n",
    "    # for e in edge_data:\n",
    "    #     src = e[0]\n",
    "    #     dst = e[1]\n",
    "    #     w = e[2]\n",
    "\n",
    "    # logonpyvis_graph.add_node(src, src, title=src)\n",
    "    # logonpyvis_graph.add_node(dst, dst, title=dst)\n",
    "    # logonpyvis_graph.add_edge(src, dst, value=w)\n",
    "\n",
    "    # neighbor_map = logonpyvis_graph.get_adj_list()\n",
    "\n",
    "    # # add neighbor data to node hover data\n",
    "    # for node in logonpyvis_graph.nodes:\n",
    "    #     node[\"title\"] += \" Neighbors:<br>\" + \"<br>\".join(neighbor_map[node[\"id\"]])\n",
    "    #     node[\"value\"] = len(neighbor_map[node[\"id\"]]) \n",
    "\n",
    "    # logonpyvis_graph.show(\"hostlogonpyvis_graph.html\")\n",
    "else:\n",
    "    md(f'Analysis section Not Applicable since IP address owner is {ipaddr_origin}', styles=[\"bold\",\"red\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a></a>[Contents](#toc)\n",
    "## Related Accounts\n",
    "**Hypothesis:** That an attacker has gained  access to the host, compromized credentials for the accounts on it and laterally moving to the network gaining access to more accounts.\n",
    "\n",
    "This section provides related accounts of IP address which is being investigated. .If you wish to expand the scope of hunting then investigate each accounts in detail, it is recommended that to use the **Account Explorer Notebook (include link).**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-10T20:12:42.022358Z",
     "start_time": "2019-09-10T20:12:42.010961Z"
    }
   },
   "source": [
    "<a></a>[Contents](#toc)\n",
    "### Visualization - Networkx Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-15T23:05:36.951055Z",
     "start_time": "2020-05-15T23:05:35.741976Z"
    }
   },
   "outputs": [],
   "source": [
    "if ipaddr_origin == \"Internal\":\n",
    "    # Retrived relatd accounts from SecurityEvent table for Windows OS\n",
    "    if ip_entity['OSType'] =='Windows':\n",
    "        if \"SecurityEvent\" not in available_datasets:\n",
    "            raise ValueError(\"No Windows event log data available in the workspace\")\n",
    "        else:\n",
    "            related_accounts = \"\"\"\n",
    "               SecurityEvent\n",
    "               | where TimeGenerated >= datetime({start}) and TimeGenerated <= datetime({end})\n",
    "               | where IpAddress == \\'{ip_address}\\' or Computer == \\'{hostname}\\'       \n",
    "               | summarize count() by Account, Computer\n",
    "            \"\"\".format(\n",
    "                **ipaddr_query_params(), hostname=hostname\n",
    "            )\n",
    "            %kql -query related_accounts\n",
    "            related_accounts_df = _kql_raw_result_.to_dataframe()\n",
    "\n",
    "    elif ip_entity['OSType'] =='Linux':\n",
    "        if \"Syslog\" not in available_datasets:\n",
    "            raise ValueError(\"No Linux syslog data available in the workspace\")\n",
    "        else:\n",
    "            related_accounts_df = qry_prov.LinuxSyslog.list_logons_for_source_ip(invest_times, ip_address=ipaddr_query_params()['ip_address'],add_query_items='extend Account = AccountName | summarize count() by Account, Computer')\n",
    "\n",
    "\n",
    "    # Uncomment- below cells if above visualization does not render - Networkx connected Graph\n",
    "    plt.figure(10, figsize=(22, 14))\n",
    "    g = nx.from_pandas_edgelist(related_accounts_df, \"Computer\", \"Account\")\n",
    "    md('Entity Relationship Graph - Related Accounts :: ',styles=[\"bold\",\"green\"])\n",
    "    nx.draw_circular(g, with_labels=True, size=40, font_size=12, font_color=\"blue\")\n",
    "\n",
    "    # Uncomment below cells if you want to display interactive graphs using Pyvis library, Azure notebook free tier may not render the graph correctly.\n",
    "    # acclogon_pyvisgraph = Network(notebook=True, height=\"750px\", width=\"100%\", bgcolor=\"#222222\", font_color=\"white\")\n",
    "\n",
    "    # # set the physics layout of the network\n",
    "    # acclogon_pyvisgraph.barnes_hut()\n",
    "\n",
    "\n",
    "    # sources = related_accounts_df['Computer']\n",
    "    # targets = related_accounts_df['Account']\n",
    "    # weights = related_accounts_df['count_']\n",
    "\n",
    "    # edge_data = zip(sources, targets, weights)\n",
    "\n",
    "    # for e in edge_data:\n",
    "    #     src = e[0]\n",
    "    #     dst = e[1]\n",
    "    #     w = e[2]\n",
    "\n",
    "    # acclogon_pyvisgraph.add_node(src, src, title=src)\n",
    "    # acclogon_pyvisgraph.add_node(dst, dst, title=dst)\n",
    "    # acclogon_pyvisgraph.add_edge(src, dst, value=w)\n",
    "\n",
    "    # neighbor_map = acclogon_pyvisgraph.get_adj_list()\n",
    "\n",
    "    # # add neighbor data to node hover data\n",
    "    # for node in acclogon_pyvisgraph.nodes:\n",
    "    #     node[\"title\"] += \" Neighbors:<br>\" + \"<br>\".join(neighbor_map[node[\"id\"]])\n",
    "    #     node[\"value\"] = len(neighbor_map[node[\"id\"]]) # this value attrribute for the node affects node size\n",
    "\n",
    "    # acclogon_pyvisgraph.show(\"accountlogonpyvis_graph.html\")\n",
    "else:\n",
    "    md(f'Analysis section Not Applicable since IP address owner is {ipaddr_origin}', styles=[\"bold\",\"red\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-30T15:50:05.854226Z",
     "start_time": "2019-08-30T15:50:04.517392Z"
    }
   },
   "source": [
    "<a></a>[Contents](#toc)\n",
    "## Logon Summary for Related Entities\n",
    "**Hypothesis:** By analyzing logon activities of the related entities, we can identify change in logon patterns and narrow down the entities to few suspicious logon patterns.\n",
    "\n",
    "This section provides various visualization of logon attributes such as \n",
    "- Weekly Failed Logon trend\n",
    "- Logon Types \n",
    "- Logon Processes\n",
    "\n",
    "If you wish to expand the scope of hunting then investigate specific host in detail, it is recommended that to use the **Host Explorer Notebook (include link).**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-10T20:18:33.673179Z",
     "start_time": "2019-09-10T20:18:33.670042Z"
    }
   },
   "source": [
    "<a></a>[Contents](#toc)\n",
    "### HeatMap for Weekly failed logons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-15T23:05:46.615934Z",
     "start_time": "2020-05-15T23:05:44.570772Z"
    }
   },
   "outputs": [],
   "source": [
    "if ipaddr_origin == \"Internal\":\n",
    "    # Retrived related accounts from SecurityEvent table for Windows OS\n",
    "    if ip_entity['OSType'] =='Windows':\n",
    "        if \"SecurityEvent\" not in available_datasets:\n",
    "            raise ValueError(\"No Windows event log data available in the workspace\")\n",
    "        else:\n",
    "            failed_logons = \"\"\"\n",
    "               SecurityEvent\n",
    "                | where EventID in (4624,4625) | where IpAddress == \\'{ip_address}\\' or Computer == \\'{hostname}\\'  \n",
    "                | where TimeGenerated >= datetime({start}) and TimeGenerated <= datetime({end})\n",
    "                | extend DayofWeek = case(dayofweek(TimeGenerated) == time(1.00:00:00), \"Monday\", \n",
    "                                    dayofweek(TimeGenerated) == time(2.00:00:00), \"Tuesday\",\n",
    "                                    dayofweek(TimeGenerated) == time(3.00:00:00), \"Wednesday\",\n",
    "                                    dayofweek(TimeGenerated) == time(4.00:00:00), \"Thursday\",\n",
    "                                    dayofweek(TimeGenerated) == time(5.00:00:00), \"Friday\",\n",
    "                                    dayofweek(TimeGenerated) == time(6.00:00:00), \"Saturday\",\n",
    "                                    \"Sunday\")\n",
    "                | summarize LogonCount=count() by DayofWeek, HourOfDay=format_datetime(bin(TimeGenerated,1h),'HH:mm')\n",
    "            \"\"\".format(\n",
    "                **ipaddr_query_params(), hostname=hostname\n",
    "            )\n",
    "            %kql -query failed_logons\n",
    "            failed_logons_df = _kql_raw_result_.to_dataframe()\n",
    "\n",
    "    elif ip_entity['OSType'] =='Linux':\n",
    "        if \"Syslog\" not in available_datasets:\n",
    "            raise ValueError(\"No Linux syslog data available in the workspace\")\n",
    "        else:   \n",
    "            failed_logons_df = qry_prov.LinuxSyslog.user_logon(invest_times, account_name ='', add_query_items=\"\"\"| where HostIP == '{ipaddr_text.value}' |extend Account = AccountName | extend DayofWeek = case(dayofweek(TimeGenerated) == time(1.00:00:00), \"Monday\", dayofweek(TimeGenerated) == time(2.00:00:00), \"Tuesday\",\n",
    "                                    dayofweek(TimeGenerated) == time(3.00:00:00), \"Wednesday\",\n",
    "                                    dayofweek(TimeGenerated) == time(4.00:00:00), \"Thursday\",\n",
    "                                    dayofweek(TimeGenerated) == time(5.00:00:00), \"Friday\",\n",
    "                                    dayofweek(TimeGenerated) == time(6.00:00:00), \"Saturday\", \"Sunday\") | summarize LogonCount=count() by DayofWeek, HourOfDay=format_datetime(bin(TimeGenerated,1h),'HH:mm')\"\"\")\n",
    "\n",
    "    # Plotting hearmap using seaborn library if there are failed logons\n",
    "    if len(failed_logons_df) > 0:\n",
    "        df_pivot = (\n",
    "            failed_logons_df.reset_index()\n",
    "            .pivot_table(index=\"DayofWeek\", columns=\"HourOfDay\", values=\"LogonCount\")\n",
    "            .fillna(0)\n",
    "        )\n",
    "        display(\n",
    "            Markdown(\n",
    "                f'###  <span style=\"color:blue\"> Heatmap - Weekly Failed Logon Trend :: </span>'\n",
    "            )\n",
    "        )\n",
    "        f, ax = plt.subplots(figsize=(16, 8))\n",
    "        hm1 = sns.heatmap(df_pivot, cmap=\"YlGnBu\", ax=ax)\n",
    "        plt.xticks(rotation=45)\n",
    "        plt.yticks(rotation=30)\n",
    "    else:\n",
    "        linux_logons=qry_prov.LinuxSyslog.list_logons_for_source_ip(**ipaddr_query_params())\n",
    "        failed_logons = (logon_events[logon_events['LogonResult'] == 'Failure'])\n",
    "else:\n",
    "    md(f'Analysis section Not Applicable since IP address owner is {ipaddr_origin}', styles=[\"bold\",\"red\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a></a>[Contents](#toc)\n",
    "### Host Logons Timeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-15T23:05:49.256466Z",
     "start_time": "2020-05-15T23:05:49.190460Z"
    }
   },
   "outputs": [],
   "source": [
    "# set the origin time to the time of our alert\n",
    "try:\n",
    "    origin_time = (related_alert.TimeGenerated \n",
    "                   if recenter_wgt.value \n",
    "                   else query_times.origin_time)\n",
    "except NameError:\n",
    "    origin_time = query_times.origin_time\n",
    "    \n",
    "logon_query_times = nbwidgets.QueryTime(\n",
    "    units=\"day\",\n",
    "    origin_time=origin_time,\n",
    "    before=5,\n",
    "    after=1,\n",
    "    max_before=20,\n",
    "    max_after=20,\n",
    ")\n",
    "logon_query_times.display()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-15T23:05:55.096129Z",
     "start_time": "2020-05-15T23:05:52.661823Z"
    }
   },
   "outputs": [],
   "source": [
    "if ipaddr_origin == \"Internal\":\n",
    "    host_logons = qry_prov.WindowsSecurity.list_host_logons(\n",
    "        logon_query_times, host_name=hostname\n",
    "    )\n",
    "\n",
    "    if host_logons is not None and not host_logons.empty:\n",
    "        display(Markdown(\"### Logon timeline.\"))\n",
    "        tooltip_cols = [\n",
    "            \"TargetUserName\",\n",
    "            \"TargetDomainName\",\n",
    "            \"SubjectUserName\",\n",
    "            \"SubjectDomainName\",\n",
    "            \"LogonType\",\n",
    "            \"IpAddress\",\n",
    "        ]\n",
    "        nbdisplay.display_timeline(\n",
    "            data=host_logons,\n",
    "            group_by=\"TargetUserName\",\n",
    "            source_columns=tooltip_cols,\n",
    "            legend=\"right\", yaxis=True\n",
    "        )\n",
    "\n",
    "        display(Markdown(\"### Counts of logon events by logon type.\"))\n",
    "        display(Markdown(\"Min counts for each logon type highlighted.\"))\n",
    "        logon_by_type = (\n",
    "            host_logons[[\"Account\", \"LogonType\", \"EventID\"]]\n",
    "            .astype({'LogonType': 'int32'})\n",
    "            .merge(right=pd.Series(data=nbdisplay._WIN_LOGON_TYPE_MAP, name=\"LogonTypeDesc\"),\n",
    "                   left_on=\"LogonType\", right_index=True)\n",
    "            .drop(columns=\"LogonType\")\n",
    "            .groupby([\"Account\", \"LogonTypeDesc\"])\n",
    "            .count()\n",
    "            .unstack()\n",
    "            .rename(columns={\"EventID\": \"LogonCount\"})\n",
    "            .fillna(0)\n",
    "            .style\n",
    "            .background_gradient(cmap=\"viridis\", low=0.5, high=0)\n",
    "            .format(\"{0:0>3.0f}\")\n",
    "        )\n",
    "        display(logon_by_type)\n",
    "    else:\n",
    "        display(Markdown(\"No logon events found for host.\"))\n",
    "else:\n",
    "    md(f'Analysis section Not Applicable since IP address owner is {ipaddr_origin}', styles=[\"bold\",\"red\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Failed Logons Timeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-15T23:06:01.493064Z",
     "start_time": "2020-05-15T23:05:59.580819Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "if ipaddr_origin == \"Internal\":\n",
    "    failedLogons = qry_prov.WindowsSecurity.list_host_logon_failures(\n",
    "        logon_query_times, host_name=ip_entity.hostname\n",
    "    )\n",
    "    if failedLogons.empty:\n",
    "        print(\"No logon failures recorded for this host between \",\n",
    "              f\" {logon_query_times.start} and {logon_query_times.end}\"\n",
    "            )\n",
    "    else:\n",
    "        nbdisplay.display_timeline(\n",
    "            data=host_logons.query('TargetLogonId != \"0x3e7\"'),\n",
    "            overlay_data=failedLogons,\n",
    "            alert=related_alert,\n",
    "            title=\"Logons (blue=user-success, green=failed)\",\n",
    "            source_columns=tooltip_cols,\n",
    "            height=200,\n",
    "        )\n",
    "        display(failedLogons\n",
    "     .astype({'LogonType': 'int32'})\n",
    "     .merge(right=pd.Series(data=nbdisplay._WIN_LOGON_TYPE_MAP, name=\"LogonTypeDesc\"),\n",
    "                   left_on=\"LogonType\", right_index=True)\n",
    "     [['Account', 'EventID', 'TimeGenerated',\n",
    "      'Computer', 'SubjectUserName', 'SubjectDomainName',\n",
    "       'TargetUserName', 'TargetDomainName',\n",
    "       'LogonTypeDesc','IpAddress', 'WorkstationName'\n",
    "      ]])\n",
    "else:\n",
    "    md(f'Analysis section Not Applicable since IP address owner is {ipaddr_origin}', styles=[\"bold\",\"red\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-30T15:52:54.700099Z",
     "start_time": "2019-08-30T15:52:54.661189Z"
    }
   },
   "source": [
    "<a></a>[Contents](#toc)\n",
    "## Network Connection Analysis\n",
    "\n",
    "**Hypothesis:** That an attacker is remotely communicating with the host in order to compromise the host or for outbound communication to C2 for data exfiltration purposes after compromising the host.\n",
    "\n",
    "This section provides an overview of network activity to and from the host during hunting time frame, the purpose of this is for the identification of anomalous network traffic. If you wish to investigate a specific IP in detail it is recommended that to use another instance of this notebook with each IP addresses."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a></a>[Contents](#toc)\n",
    "### Network Check Communications with Other Hosts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-15T23:06:06.486183Z",
     "start_time": "2020-05-15T23:06:06.429184Z"
    }
   },
   "outputs": [],
   "source": [
    "ip_q_times = nbwidgets.QueryTime(\n",
    "    label=\"Set time bounds for network queries\",\n",
    "    units=\"day\",\n",
    "    max_before=28,\n",
    "    before=2,\n",
    "    after=5,\n",
    "    max_after=28,\n",
    "    origin_time=logon_query_times.origin_time\n",
    ")\n",
    "ip_q_times.display()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a></a>[Contents](#toc)\n",
    "### Query Flows by IP Address"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-15T23:06:22.160247Z",
     "start_time": "2020-05-15T23:06:10.292782Z"
    }
   },
   "outputs": [],
   "source": [
    "if \"AzureNetworkAnalytics_CL\" not in available_datasets:\n",
    "    md_warn(\"No network flow data available.\")\n",
    "    md(\"Please skip the remainder of this section and go to [Time-Series-Anomalies](#Outbound-Data-transfer-Time-Series-Anomalies)\")\n",
    "    az_net_comms_df = None\n",
    "else:\n",
    "    all_host_ips = (\n",
    "        ip_entity['private_ips'] + ip_entity['public_ips']\n",
    "    )\n",
    "    host_ips = [i.Address for i in all_host_ips]\n",
    "\n",
    "    az_net_comms_df = qry_prov.Network.list_azure_network_flows_by_ip(\n",
    "        ip_q_times, ip_address_list=host_ips\n",
    "    )\n",
    "\n",
    "    if isinstance(az_net_comms_df, pd.DataFrame) and not az_net_comms_df.empty:\n",
    "        az_net_comms_df['TotalAllowedFlows'] = az_net_comms_df['AllowedOutFlows'] + az_net_comms_df['AllowedInFlows']\n",
    "        nbdisplay.display_timeline(\n",
    "            data=az_net_comms_df,\n",
    "            group_by=\"L7Protocol\",\n",
    "            title=\"Network Flows by Protocol\",\n",
    "            time_column=\"FlowStartTime\",\n",
    "            source_columns=[\"FlowType\", \"AllExtIPs\", \"L7Protocol\", \"FlowDirection\"],\n",
    "            height=300,\n",
    "            legend=\"right\",\n",
    "            yaxis=True\n",
    "        )\n",
    "        nbdisplay.display_timeline(\n",
    "            data=az_net_comms_df,\n",
    "            group_by=\"FlowDirection\",\n",
    "            title=\"Network Flows by Direction\",\n",
    "            time_column=\"FlowStartTime\",\n",
    "            source_columns=[\"FlowType\", \"AllExtIPs\", \"L7Protocol\", \"FlowDirection\"],\n",
    "            height=300,\n",
    "            legend=\"right\",\n",
    "            yaxis=True\n",
    "        )\n",
    "    else:\n",
    "        md_warn(\"No network data for specified time range.\")\n",
    "        md(\"Please skip the remainder of this section and go to [Time-Series-Anomalies](#Outbound-Data-transfer-Time-Series-Anomalies)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-15T23:06:50.373391Z",
     "start_time": "2020-05-15T23:06:50.084392Z"
    }
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    flow_plot = nbdisplay.display_timeline_values(\n",
    "        data=az_net_comms_df,\n",
    "        group_by=\"L7Protocol\",\n",
    "        source_columns=[\"FlowType\", \n",
    "                      \"AllExtIPs\", \n",
    "                      \"L7Protocol\", \n",
    "                      \"FlowDirection\", \n",
    "                      \"TotalAllowedFlows\"],\n",
    "        time_column=\"FlowStartTime\",\n",
    "        y=\"TotalAllowedFlows\",\n",
    "        legend=\"right\",\n",
    "        height=500,\n",
    "        kind=[\"vbar\", \"circle\"],\n",
    "    );\n",
    "except NameError as err:\n",
    "    md(f\"Error Occured, Make sure to execute previous cells in notebook: {err}\",styles=[\"bold\",\"red\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-15T23:06:55.928554Z",
     "start_time": "2020-05-15T23:06:55.790553Z"
    }
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    if az_net_comms_df is not None and not az_net_comms_df.empty:\n",
    "        cm = sns.light_palette(\"green\", as_cmap=True)\n",
    "\n",
    "        cols = [\n",
    "            \"VMName\",\n",
    "            \"VMIPAddress\",\n",
    "            \"PublicIPs\",\n",
    "            \"SrcIP\",\n",
    "            \"DestIP\",\n",
    "            \"L4Protocol\",\n",
    "            \"L7Protocol\",\n",
    "            \"DestPort\",\n",
    "            \"FlowDirection\",\n",
    "            \"AllExtIPs\",\n",
    "            \"TotalAllowedFlows\",\n",
    "        ]\n",
    "        flow_index = az_net_comms_df[cols].copy()\n",
    "\n",
    "        def get_source_ip(row):\n",
    "            if row.FlowDirection == \"O\":\n",
    "                return row.VMIPAddress if row.VMIPAddress else row.SrcIP\n",
    "            else:\n",
    "                return row.AllExtIPs if row.AllExtIPs else row.DestIP\n",
    "\n",
    "        def get_dest_ip(row):\n",
    "            if row.FlowDirection == \"O\":\n",
    "                return row.AllExtIPs if row.AllExtIPs else row.DestIP\n",
    "            else:\n",
    "                return row.VMIPAddress if row.VMIPAddress else row.SrcIP\n",
    "    \n",
    "        flow_index[\"source\"] = flow_index.apply(get_source_ip, axis=1)\n",
    "        flow_index[\"dest\"] = flow_index.apply(get_dest_ip, axis=1)\n",
    "        display(flow_index)\n",
    "\n",
    "        # Uncomment to view flow_index results\n",
    "    #     with warnings.catch_warnings():\n",
    "    #         warnings.simplefilter(\"ignore\")\n",
    "    #         display(\n",
    "    #             flow_index[\n",
    "    #                 [\"source\", \"dest\", \"L7Protocol\", \"FlowDirection\", \"TotalAllowedFlows\"]\n",
    "    #             ]\n",
    "    #             .groupby([\"source\", \"dest\", \"L7Protocol\", \"FlowDirection\"])\n",
    "    #             .sum()\n",
    "    #             .reset_index()\n",
    "    #             .style.bar(subset=[\"TotalAllowedFlows\"], color=\"#d65f5f\")\n",
    "    #         )\n",
    "except NameError as err:\n",
    "    md(f\"Error Occured, Make sure to execute previous cells in notebook: {err}\",styles=[\"bold\",\"red\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a></a>[Contents](#toc)\n",
    "### Bulk whois lookup "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-15T23:08:00.744206Z",
     "start_time": "2020-05-15T23:07:01.493951Z"
    }
   },
   "outputs": [],
   "source": [
    "# Bulk WHOIS lookup function\n",
    "from functools import lru_cache\n",
    "from ipwhois import IPWhois\n",
    "from ipaddress import ip_address\n",
    "\n",
    "try:\n",
    "    # Add ASN informatio from Whois\n",
    "    flows_df = (\n",
    "        flow_index[[\"source\", \"dest\", \"L7Protocol\", \"FlowDirection\", \"TotalAllowedFlows\"]]\n",
    "        .groupby([\"source\", \"dest\", \"L7Protocol\", \"FlowDirection\"])\n",
    "        .sum()\n",
    "        .reset_index()\n",
    "    )\n",
    "\n",
    "    num_ips = len(flows_df[\"source\"].unique()) + len(flows_df[\"dest\"].unique())\n",
    "    print(f\"Performing WhoIs lookups for {num_ips} IPs \", end=\"\")\n",
    "    #flows_df = flows_df.assign(DestASN=\"\", DestASNFull=\"\", SourceASN=\"\", SourceASNFull=\"\")\n",
    "    flows_df[\"DestASN\"] = flows_df.apply(lambda x: get_whois_info(x.dest, True), axis=1)\n",
    "    flows_df[\"SourceASN\"] = flows_df.apply(lambda x: get_whois_info(x.source, True), axis=1)\n",
    "    print(\"done\")\n",
    "\n",
    "    # Split the tuple returned by get_whois_info into separate columns\n",
    "    flows_df[\"DestASNFull\"] = flows_df.apply(lambda x: x.DestASN[1], axis=1)\n",
    "    flows_df[\"DestASN\"] = flows_df.apply(lambda x: x.DestASN[0], axis=1)\n",
    "    flows_df[\"SourceASNFull\"] = flows_df.apply(lambda x: x.SourceASN[1], axis=1)\n",
    "    flows_df[\"SourceASN\"] = flows_df.apply(lambda x: x.SourceASN[0], axis=1)\n",
    "\n",
    "    our_host_asns = [get_whois_info(ip.Address)[0] for ip in ip_entity.public_ips]\n",
    "    md(f\"Host {ip_entity.hostname} ASNs:\", \"bold\")\n",
    "    md(str(our_host_asns))\n",
    "\n",
    "    flow_sum_df = flows_df.groupby([\"DestASN\", \"SourceASN\"]).agg(\n",
    "        TotalAllowedFlows=pd.NamedAgg(column=\"TotalAllowedFlows\", aggfunc=\"sum\"),\n",
    "        L7Protocols=pd.NamedAgg(column=\"L7Protocol\", aggfunc=lambda x: x.unique().tolist()),\n",
    "        source_ips=pd.NamedAgg(column=\"source\", aggfunc=lambda x: x.unique().tolist()),\n",
    "        dest_ips=pd.NamedAgg(column=\"dest\", aggfunc=lambda x: x.unique().tolist()),\n",
    "    ).reset_index()\n",
    "    flow_sum_df\n",
    "except NameError as err:\n",
    "    md(f\"Error Occured, Make sure to execute previous cells in notebook: {err}\",styles=[\"bold\",\"red\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choose ASNs/IPs to Check for Threat Intel Reports\n",
    "Choose from the list of Selected ASNs for the IPs you wish to check on.\n",
    "The Source list is been pre-populated with all ASNs found in the network flow summary.\n",
    "\n",
    "As an example, we've populated the `Selected` list with the ASNs that have the lowest number of flows to and from the host. We also remove the ASN that matches the ASN of the host we are investigating.\n",
    "\n",
    "Please edit this list, using flow summary data above as a guide and leaving only ASNs that you are suspicious about. Typicially these would be ones with relatively low `TotalAllowedFlows` and possibly with unusual `L7Protocols`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-15T23:08:01.347207Z",
     "start_time": "2020-05-15T23:08:01.287206Z"
    }
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    if isinstance(flow_sum_df, pd.DataFrame) and not flow_sum_df.empty:\n",
    "        all_asns = list(flow_sum_df[\"DestASN\"].unique()) + list(flow_sum_df[\"SourceASN\"].unique())\n",
    "        all_asns = set(all_asns) - set([\"private address\"])\n",
    "\n",
    "        # Select the ASNs in the 25th percentile (lowest number of flows)\n",
    "        quant_25pc = flow_sum_df[\"TotalAllowedFlows\"].quantile(q=[0.25]).iat[0]\n",
    "        quant_25pc_df = flow_sum_df[flow_sum_df[\"TotalAllowedFlows\"] <= quant_25pc]\n",
    "        other_asns = list(quant_25pc_df[\"DestASN\"].unique()) + list(quant_25pc_df[\"SourceASN\"].unique())\n",
    "        other_asns = set(other_asns) - set(our_host_asns)\n",
    "        md(\"Choose IPs from Selected ASNs to look up for Threat Intel.\", \"bold\")\n",
    "        sel_asn = nbwidgets.SelectSubset(source_items=all_asns, default_selected=other_asns)\n",
    "except NameError as err:\n",
    "    md(f\"Error Occured, Make sure to execute previous cells in notebook: {err}\",styles=[\"bold\",\"red\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-15T23:08:14.516746Z",
     "start_time": "2020-05-15T23:08:01.935205Z"
    }
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    if isinstance(flow_sum_df, pd.DataFrame) and not flow_sum_df.empty:\n",
    "        ti_lookup = TILookup()\n",
    "        from itertools import chain\n",
    "        dest_ips = set(chain.from_iterable(flow_sum_df[flow_sum_df[\"DestASN\"].isin(sel_asn.selected_items)][\"dest_ips\"]))\n",
    "        src_ips = set(chain.from_iterable(flow_sum_df[flow_sum_df[\"SourceASN\"].isin(sel_asn.selected_items)][\"source_ips\"]))\n",
    "        selected_ips = dest_ips | src_ips\n",
    "        print(f\"{len(selected_ips)} unique IPs in selected ASNs\")\n",
    "\n",
    "        # Add the IoCType to save cost of inferring each item\n",
    "        selected_ip_dict = {ip: \"ipv4\" for ip in selected_ips}\n",
    "        ti_results = ti_lookup.lookup_iocs(data=selected_ip_dict)\n",
    "\n",
    "        print(f\"{len(ti_results)} results received.\")\n",
    "\n",
    "        # ti_results_pos = ti_results[ti_results[\"Severity\"] > 0]\n",
    "        #####\n",
    "        # WARNING - faking results for illustration purposes\n",
    "        #####\n",
    "        ti_results_pos = ti_results.sample(n=2)\n",
    "\n",
    "        print(f\"{len(ti_results_pos)} positive results found.\")\n",
    "\n",
    "\n",
    "        if not ti_results_pos.empty:\n",
    "            src_pos = flows_df.merge(ti_results_pos, left_on=\"source\", right_on=\"Ioc\")\n",
    "            dest_pos = flows_df.merge(ti_results_pos, left_on=\"dest\", right_on=\"Ioc\")\n",
    "            ti_ip_results = pd.concat([src_pos, dest_pos])\n",
    "            md_warn(\"Positive Threat Intel Results found for the following flows\")\n",
    "            md(\"Please examine these IP flows using the IP Explorer notebook.\", \"bold, large\")\n",
    "            display(ti_ip_results)\n",
    "except NameError as err:\n",
    "    md(f\"Error Occured, Make sure to execute previous cells in notebook: {err}\",styles=[\"bold\",\"red\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### GeoIP Map of External IPs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-15T23:08:16.023912Z",
     "start_time": "2020-05-15T23:08:15.611915Z"
    }
   },
   "outputs": [],
   "source": [
    "iplocation = GeoLiteLookup()\n",
    "def format_ip_entity(row, ip_col):\n",
    "    ip_entity = entities.IpAddress(Address=row[ip_col])\n",
    "    iplocation.lookup_ip(ip_entity=ip_entity)\n",
    "    ip_entity.AdditionalData[\"protocol\"] = row.L7Protocol\n",
    "    if \"severity\" in row:\n",
    "        ip_entity.AdditionalData[\"threat severity\"] = row[\"severity\"]\n",
    "    if \"Details\" in row:\n",
    "        ip_entity.AdditionalData[\"threat details\"] = row[\"Details\"]\n",
    "    return ip_entity\n",
    "\n",
    "# from msticpy.nbtools.foliummap import FoliumMap\n",
    "folium_map = FoliumMap()\n",
    "if az_net_comms_df is None or az_net_comms_df.empty:\n",
    "    print(\"No network flow data available.\")\n",
    "else:\n",
    "    # Get the flow records for all flows not in the TI results\n",
    "    selected_out = flows_df[flows_df[\"DestASN\"].isin(sel_asn.selected_items)]\n",
    "    selected_out = selected_out[~selected_out[\"dest\"].isin(ti_ip_results[\"Ioc\"])]\n",
    "    if selected_out.empty:\n",
    "        ips_out = []\n",
    "    else:\n",
    "        ips_out = list(selected_out.apply(lambda x: format_ip_entity(x, \"dest\"), axis=1))\n",
    "    \n",
    "    selected_in = flows_df[flows_df[\"SourceASN\"].isin(sel_asn.selected_items)]\n",
    "    selected_in = selected_in[~selected_in[\"source\"].isin(ti_ip_results[\"Ioc\"])]\n",
    "    if selected_in.empty:\n",
    "        ips_in = []\n",
    "    else:\n",
    "        ips_in = list(selected_in.apply(lambda x: format_ip_entity(x, \"source\"), axis=1))\n",
    "\n",
    "    ips_threats = list(ti_ip_results.apply(lambda x: format_ip_entity(x, \"Ioc\"), axis=1))\n",
    "\n",
    "    display(HTML(\"<h3>External IP Addresses communicating with host</h3>\"))\n",
    "    display(HTML(\"Numbered circles indicate multiple items - click to expand\"))\n",
    "    display(HTML(\"Location markers: <br>Blue = outbound, Purple = inbound, Green = Host, Red = Threats\"))\n",
    "\n",
    "    icon_props = {\"color\": \"green\"}\n",
    "    for ips in ip_entity.public_ips:\n",
    "        ips.AdditionalData[\"host\"] = ip_entity.hostname\n",
    "    folium_map.add_ip_cluster(ip_entities=ip_entity.public_ips, **icon_props)\n",
    "    icon_props = {\"color\": \"blue\"}\n",
    "    folium_map.add_ip_cluster(ip_entities=ips_out, **icon_props)\n",
    "    icon_props = {\"color\": \"purple\"}\n",
    "    folium_map.add_ip_cluster(ip_entities=ips_in, **icon_props)\n",
    "    icon_props = {\"color\": \"red\"}\n",
    "    folium_map.add_ip_cluster(ip_entities=ips_threats, **icon_props)\n",
    "    \n",
    "    display(folium_map)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-05T18:03:37.980223Z",
     "start_time": "2019-09-05T18:03:37.804856Z"
    }
   },
   "source": [
    "<a></a>[Contents](#toc)\n",
    "### Outbound Data transfer Time Series Anomalies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section will look into the network datasources to check outbound data transfer trends. \n",
    "You can also use time series analysis using below built-in KQL query example to analyze anamalous data transfer trends.below example shows sample dataset trends comparing with actual vs baseline traffic trends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-15T23:08:42.937737Z",
     "start_time": "2020-05-15T23:08:41.794266Z"
    }
   },
   "outputs": [],
   "source": [
    "if \"VMConnection\" in table_index or \"CommonSecurityLog\" in table_index:\n",
    "    # KQL query for full text search of IP address and display all datatypes\n",
    "    dataxfer_stats = \"\"\"\n",
    "      union isfuzzy=true\n",
    "      (\n",
    "      CommonSecurityLog \n",
    "      | where TimeGenerated >= datetime({start}) and TimeGenerated <= datetime({end})\n",
    "      | where isnotempty(DestinationIP) and isnotempty(SourceIP)\n",
    "      | where SourceIP == \\'{ip_address}\\'\n",
    "      | extend SentBytesinKB = (SentBytes / 1024), ReceivedBytesinKB = (ReceivedBytes / 1024)\n",
    "      | summarize DailyCount = count(), ListOfDestPorts = make_set(DestinationPort), TotalSentBytesinKB = sum(SentBytesinKB), TotalReceivedBytesinKB = sum(ReceivedBytesinKB) by SourceIP, DestinationIP, DeviceVendor, bin(TimeGenerated,1d)\n",
    "      | project DeviceVendor, TimeGenerated, SourceIP, DestinationIP, ListOfDestPorts, TotalSentBytesinKB, TotalReceivedBytesinKB \n",
    "      ),\n",
    "      (\n",
    "      VMConnection \n",
    "      | where TimeGenerated >= datetime({start}) and TimeGenerated <= datetime({end}) \n",
    "      | where isnotempty(DestinationIp) and isnotempty(SourceIp)\n",
    "      | where SourceIp == \\'{ip_address}\\'\n",
    "      | extend DeviceVendor = \"VMConnection\", SourceIP = SourceIp, DestinationIP = DestinationIp\n",
    "      | extend SentBytesinKB = (BytesSent / 1024), ReceivedBytesinKB = (BytesReceived / 1024)\n",
    "      | summarize DailyCount = count(), ListOfDestPorts = make_set(DestinationPort), TotalSentBytesinKB = sum(SentBytesinKB),TotalReceivedBytesinKB = sum(ReceivedBytesinKB) by SourceIP, DestinationIP, DeviceVendor, bin(TimeGenerated,1d)\n",
    "      | project DeviceVendor, TimeGenerated, SourceIP, DestinationIP, ListOfDestPorts, TotalSentBytesinKB, TotalReceivedBytesinKB \n",
    "      )\n",
    "    \"\"\".format(**ipaddr_query_params())\n",
    "    %kql -query dataxfer_stats\n",
    "    dataxfer_stats_df = _kql_raw_result_.to_dataframe()\n",
    "\n",
    "#Display result as transposed matrix of datatypes availabel to query for the query period\n",
    "if len(dataxfer_stats_df) > 0:\n",
    "    md(\n",
    "            'Data transfer daily stats for IP ::', styles=[\"bold\",\"green\"]\n",
    "        )\n",
    "    #display(dataxfer_stats_df)\n",
    "else:\n",
    "    md_warn(\n",
    "            f'No Data transfer logs found for the query period'\n",
    "        )\n",
    "        #####\n",
    "        # WARNING - faking results for illustration purposes\n",
    "        #####\n",
    "md(\n",
    "            'Visualizing time series data transfer on dummy dataset for demonstration ::', styles=[\"bold\",\"green\"]\n",
    "    )\n",
    "\n",
    "#Generating graph based on dummy dataset in custom table representing Flow records outbound data transfer\n",
    "timechartquery = \"\"\"\n",
    "let TimeSeriesData = PaloAltoBytesSent_CL\n",
    "| extend TimeGenerated = todatetime(EventTime_s), TotalBytesSent = todouble(TotalBytesSent_s) \n",
    "| summarize TimeGenerated=make_list(TimeGenerated, 10000),TotalBytesSent=make_list(TotalBytesSent, 10000) by deviceVendor_s\n",
    "| project TimeGenerated, TotalBytesSent;\n",
    "TimeSeriesData\n",
    "| extend (baseline,seasonal,trend,residual) = series_decompose(TotalBytesSent)\n",
    "| mv-expand TotalBytesSent to typeof(double), TimeGenerated to typeof(datetime), baseline to typeof(long), seasonal to typeof(long), trend to typeof(long), residual to typeof(long)\n",
    "| project TimeGenerated, TotalBytesSent, baseline\n",
    "| render timechart with (title=\"Palo Alto Outbound Data Transfer Time Series decomposition\")\n",
    "\"\"\"\n",
    "%kql -query timechartquery"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List of Suspicious Activities/ Observables/Hunting bookmarks\n",
    "- Suspicious alerts for the IP\n",
    "- Anamalous Failed Logon trend on few days at 04:00 AM\n",
    "- Anamalous spike in traffic logs on http\n",
    "- Positive TI Hit from Open source feeds.\n",
    "- Unusual data transfer deviating from normal baseline."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a></a>[Contents](#toc)\n",
    "## Appendices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Available DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-02T10:00:41.436112Z",
     "start_time": "2020-04-02T10:00:41.426605Z"
    }
   },
   "outputs": [],
   "source": [
    "print('List of current DataFrames in Notebook')\n",
    "print('-' * 50)\n",
    "current_vars = list(locals().keys())\n",
    "for var_name in current_vars:\n",
    "    if isinstance(locals()[var_name], pd.DataFrame) and not var_name.startswith('_'):\n",
    "        print(var_name)"
   ]
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    "### Saving Data to Excel\n",
    "To save the contents of a pandas DataFrame to an Excel spreadsheet\n",
    "use the following syntax\n",
    "```\n",
    "writer = pd.ExcelWriter('myWorksheet.xlsx')\n",
    "my_data_frame.to_excel(writer,'Sheet1')\n",
    "writer.save()\n",
    "```"
   ]
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    "## Configuration\n",
    "\n",
    "### `msticpyconfig.yaml` configuration File\n",
    "You can configure primary and secondary TI providers and any required parameters in the `msticpyconfig.yaml` file. This is read from the current directory or you can set an environment variable (`MSTICPYCONFIG`) pointing to its location.\n",
    "\n",
    "To configure this file see the [ConfigureNotebookEnvironment notebook](https://github.com/Azure/Azure-Sentinel-Notebooks/blob/master/ConfiguringNotebookEnvironment.ipynb)"
   ]
  }
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