{ "cells": [ { "cell_type": "markdown", "source": [ "# MSTICPy - Matrix Plot\r\n", "\r\n", "This notebook demonstrates the use of the MSTICPy matrix visualization built using the [Bokeh library](https://bokeh.pydata.org).\r\n", "\r\n", "You must have msticpy installed:\r\n", "```\r\n", "%pip install --upgrade msticpy\r\n", "```\r\n", "\r\n", "The matrix plot is designed to show interactions between two items stored\r\n", "in a pandas DataFrame in a x-y grid.\r\n", "\r\n", "To take an example, if you have a DataFrame with source and destination IP addresses\r\n", "(for example, a firewall log), you can plot the source IPs on the y axis and\r\n", "destination IPs on the x axis. Where there is an event (row) that links a given\r\n", "source and destination the matrix plot will plot a circle.\r\n", "\r\n", "By default the circle is proportional to the number of events containing a given\r\n", "source/destination (x and y).\r\n", "\r\n", "The matrix plot also has the following variations:\r\n", "- You can use a named column from the input data (e.g. bytes transmitted) to control\r\n", " the size of the plotted circle.\r\n", "- You can invert the circle plot size, so that rarer interactions are shown\r\n", " with a large intersection point.\r\n", "- You can plot just the presence of one or more interactions - this plots\r\n", " a fixed-size point and is useful if you only want to see the presence/\r\n", " absence of an interaction but don't care about the number of interactions.\r\n", "- You can use a count of distinct values to control the size (e.g. you might\r\n", " specify \"protocol\" as the value column and want to see how many distinct\r\n", " protocols the source/destination interacted over).\r\n", "- You can plot the log of any of the above counts/size - this is useful if\r\n", " the variance in the size is orders of magnitude.\r\n" ], "metadata": {} }, { "cell_type": "code", "execution_count": 1, "source": [ "# Imports\r\n", "\r\n", "from msticpy.common.utility import check_py_version\r\n", "MIN_REQ_PYTHON = (3,6)\r\n", "check_py_version(MIN_REQ_PYTHON)\r\n", "\r\n", "import pandas as pd\r\n", "\r\n", "from msticpy import init_notebook\r\n", "init_notebook(globals())\r\n" ], "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "

Starting Notebook initialization...

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Latest version is installed.

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" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "True" ] }, "metadata": {}, "execution_count": 1 } ], "metadata": { "ExecuteTime": { "end_time": "2020-01-30T20:46:28.835951Z", "start_time": "2020-01-30T20:46:26.259919Z" }, "scrolled": true } }, { "cell_type": "markdown", "source": [ "# Creating some sample data\r\n" ], "metadata": {} }, { "cell_type": "code", "execution_count": 2, "source": [ "all_df = pd.read_csv(\r\n", " \"data/az_net_flows.csv\",\r\n", " index_col=0,\r\n", " parse_dates=[\r\n", " \"TimeGenerated\",\r\n", " \"FlowStartTime\",\r\n", " \"FlowEndTime\",\r\n", " \"FlowIntervalEndTime\",\r\n", " ],\r\n", " )\r\n", "\r\n", "# Create some sample data to work with\r\n", "net_df = (\r\n", " all_df[[\"AllExtIPs\", \"L7Protocol\", \"TotalAllowedFlows\"]]\r\n", " .rename(columns={\"AllExtIPs\": \"SourceIP\"})\r\n", " .sample(100)\r\n", ")\r\n", "\r\n", "\r\n", "def get_dest_ip(row):\r\n", " dest_ip = None\r\n", " while dest_ip is None or row.SourceIP == dest_ip:\r\n", " dest_ip = net_df[~net_df[\"SourceIP\"].str.startswith(\"10.\")].sample(1)[\"SourceIP\"].values[0]\r\n", " return dest_ip\r\n", "\r\n", "net_df[\"DestinationIP\"] = net_df.apply(get_dest_ip, axis=1)\r\n", "net_df.head(3)" ], "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " SourceIP L7Protocol TotalAllowedFlows DestinationIP\n", "690 20.38.98.100 https 1.0 65.55.44.109\n", "544 13.67.143.117 https 1.0 13.71.172.130\n", "957 65.55.163.76 https 5.0 13.65.107.32" ], "text/html": [ "
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\n", "\n" ] }, "metadata": {}, "execution_count": 3 } ], "metadata": {} }, { "cell_type": "markdown", "source": [ "## Using the Bokeh interactive tools\r\n", "\r\n", "The Bokeh graph is interactive and has the following features:\r\n", "- Tooltip display for each event marker as you hover over it\r\n", "- Toolbar with the following tools (most are toggles enabling or disabling the tool):\r\n", " - Panning \r\n", " - Select zoom\r\n", " - Mouse wheel zoom\r\n", " - Reset to default view\r\n", " - Save image to PNG\r\n", " - Hover tool" ], "metadata": {} }, { "cell_type": "markdown", "source": [ "## Sorting the X and Y values\r\n", "\r\n", "You can use `sort` to sort both axes or `sort_x` and `sort_y` to individually sort the values.\r\n", "\r\n", "The sort parameters take values \"asc\" (ascending), \"desc\" (descending), `True` (ascending).\r\n", "`None` and `False` produce no sorting." ], "metadata": {} }, { "cell_type": "code", "execution_count": 25, "source": [ "net_df.mp_plot.matrix(\r\n", " x=\"SourceIP\",\r\n", " y=\"DestinationIP\",\r\n", " title=\"IP Interaction\",\r\n", " sort_y=\"asc\",\r\n", " sort_x=False,\r\n", ")" ], "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", "
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\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
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\n", "\n" ] }, "metadata": {}, "execution_count": 25 } ], "metadata": {} }, { "cell_type": "markdown", "source": [ "## You can also import and use the `plot_matrix` function directly\r\n", "\r\n", "Supply the input DataFrame as the first parameter (or as named\r\n", "parameter `data`)\r\n", "\r\n", "```python\r\n", "from msticpy.vis.matrix_plot import plot_matrix\r\n", "\r\n", "plot_matrix(data=net_df, x=\"SourceIP\", y=\"DestinationIP\", title=\"IP Interaction\")\r\n", "```" ], "metadata": {} }, { "cell_type": "markdown", "source": [ "## Plotting interactions based on column value\r\n", "\r\n", "Instead of a simple count of rows linking an X-Y pair of entities,\r\n", "you can use a numeric column in the input DataFrame to control\r\n", "the size of the plotted circle.\r\n", "\r\n", "In this example, we're using the \"TotalAllowedFlows\" column." ], "metadata": {} }, { "cell_type": "code", "execution_count": 13, "source": [ "all_df.mp_plot.matrix(\r\n", " x=\"L7Protocol\",\r\n", " y=\"AllExtIPs\",\r\n", " value_col=\"TotalAllowedFlows\",\r\n", " title=\"External IP protocol flows\",\r\n", " sort=\"asc\",\r\n", ")" ], "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", "
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x_range = FactorRange(id='1916', ...),
x_scale = CategoricalScale(id='1920', ...),
y_range = FactorRange(id='1918', ...),
y_scale = CategoricalScale(id='1922', ...))
\n", "\n" ] }, "metadata": {}, "execution_count": 13 } ], "metadata": {} }, { "cell_type": "markdown", "source": [ "## Log scaling the size column\r\n", "\r\n", "Note because of a few large values in the data many points are difficult to see in the previous plot.\r\n", "We can change this by plotting the log of the scalar values." ], "metadata": {} }, { "cell_type": "code", "execution_count": 14, "source": [ "all_df.mp_plot.matrix(\r\n", " x=\"L7Protocol\",\r\n", " y=\"AllExtIPs\",\r\n", " value_col=\"TotalAllowedFlows\",\r\n", " title=\"External IP protocol flows (log of size)\",\r\n", " log_size=True,\r\n", " sort=\"asc\",\r\n", ")" ], "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", "
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\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "metadata": {} }, { "cell_type": "markdown", "source": [ "## Inverting to show rare interactions as larger\r\n", "\r\n", "Where you want to highlight unusual interactions, we can plot the\r\n", "inverse of the `value_col` value or count of interactions using the `invert=True` parameter.\r\n", "\r\n", "This results in a plot with larger circles for rarer interactions." ], "metadata": {} }, { "cell_type": "code", "execution_count": 16, "source": [ "net_df.mp_plot.matrix(\r\n", " x=\"SourceIP\",\r\n", " y=\"DestinationIP\",\r\n", " value_col=\"TotalAllowedFlows\",\r\n", " title=\"External IP flows (rare flows == larger)\",\r\n", " invert=True,\r\n", " sort=\"asc\",\r\n", ")" ], "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", "
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\n", "\n" ] }, "metadata": {}, "execution_count": 16 } ], "metadata": {} }, { "cell_type": "markdown", "source": [ "## Showing interactions only\r\n", "\r\n", "Where you do not care about any value associated with the interaction\r\n", "and only want to see if there has been an interaction, you can use\r\n", "the `intersect` parameter" ], "metadata": {} }, { "cell_type": "code", "execution_count": 19, "source": [ "net_df.mp_plot.matrix(\r\n", " x=\"SourceIP\",\r\n", " y=\"DestinationIP\",\r\n", " title=\"External IP flows (intersection)\",\r\n", " intersect=True,\r\n", " sort=\"asc\",\r\n", ")" ], "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", "
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\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

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