269 строки
7.3 KiB
Plaintext
269 строки
7.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Create AMITT incident visualisations\n",
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"\n",
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"Many thanks to https://python-graph-gallery.com/91-customize-seaborn-heatmap/"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import seaborn as sns\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import generate_amitt_ttps\n",
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"\n",
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"# Check that heatmap works\n",
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"df = pd.DataFrame(np.random.random((10,12)), columns=[\"a\",\"b\",\"c\",\"d\",\"e\",\"f\",\"g\",\"h\",\"i\",\"j\",\"k\",\"l\"])\n",
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"sns.heatmap(df, annot=True, annot_kws={\"size\": 7})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"amitt = generate_amitt_ttps.Amitt()\n",
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"redgrid = amitt.create_padded_framework_table('AMITT Red', 'technique_ids', False)\n",
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"\n",
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"techcounts = amitt.it[['id_incident','id_technique']].drop_duplicates().groupby('id_technique').count().to_dict()['id_incident']\n",
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"techlabels = redgrid[2:][:]\n",
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"nrows = len(techlabels)\n",
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"ncols = len(techlabels[0])\n",
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"techgrid = np.zeros([nrows, ncols], dtype = int)\n",
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"\n",
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"for row in range(nrows):\n",
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" for col in range(ncols):\n",
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" if techlabels[row][col] in techcounts:\n",
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" techgrid[row][col] = techcounts[techlabels[row][col]]\n",
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"\n",
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"sns.heatmap(techgrid, annot=True, annot_kws={\"size\": 7})\n",
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"techgrid"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"amitt.df_tactics"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"amitt.it"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"ct = amitt.cross_counterid_techniqueid\n",
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"ct[ct['technique_id'] != '']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"ct[(ct['id'] == 'C00197') & (ct['technique_id'].isin(['T0002', 'T0007']))]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"ct = ct[ct['technique_id'].isin(amitt.df_techniques['id'].to_list()) & ct['id'].isin(amitt.df_counters['id'].to_list())]\n",
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"ct"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"technique_id_list = ['T0007', 'T0008', 'T0022', 'T0023', 'T0043', 'T0052', 'T0036', 'T0037', 'T0038']\n",
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"counter_id_list = ['C00009', 'C00008', 'C00042', 'C00030', 'C00093', 'C00193', 'C00073', 'C000197', 'C00174', 'C00205']\n",
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"possible_counters_for_techniques = ct[ct['technique_id'].isin(technique_id_list)] \n",
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"possible_techniques_for_counters = ct[ct['id'].isin(counter_id_list)] \n",
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"coverage = ct[(ct['id'].isin(counter_id_list)) & (ct['technique_id'].isin(technique_id_list))]\n",
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"coverage"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"possible_techniques_for_counters"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"possible_counters_for_techniques"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"clicked button T0045 8 7\n",
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"clicked button T0046 9 7\n",
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"clicked button T0049 4 8\n",
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"clicked button T0057 2 9\n",
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"clicked button T0060 4 10\n",
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"clicked button T0029 2 6\n",
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"clicked button T0016 2 4\n"
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]
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}
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],
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"source": [
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"import tkinter as Tk\n",
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"import numpy as np\n",
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"import generate_amitt_ttps\n",
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"\n",
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"class Begueradj(Tk.Frame):\n",
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" def __init__(self,parent):\n",
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" amitt = generate_amitt_ttps.Amitt()\n",
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" self.redgrid = amitt.create_padded_framework_table('AMITT Red', 'technique_ids', False)\n",
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" self.bluegrid = amitt.create_padded_framework_table('AMITT Blue', 'counter_ids', False)\n",
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"\n",
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" Tk.Frame.__init__(self, parent)\n",
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" self.parent = parent\n",
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" self.button= ''\n",
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" self.initialize()\n",
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" \n",
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" def initialize(self):\n",
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" '''\n",
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" Draw the GUI\n",
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" '''\n",
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" self.parent.title(\"AMITT FRAMEWORK COVERAGE\") \n",
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" self.parent.grid_rowconfigure(1,weight=1)\n",
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" self.parent.grid_columnconfigure(1,weight=1)\n",
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"\n",
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" self.frame = Tk.Frame(self.parent) \n",
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" self.frame.pack(fill=Tk.X, padx=5, pady=5)\n",
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"\n",
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" # Create a 6x7 array of zeros as the one you used\n",
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" numrows = len(self.redgrid) - 1\n",
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" numcols = len(self.redgrid[0])\n",
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" self.buttons = {}\n",
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" for row in range(1,numrows):\n",
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" for col in range(0,numcols):\n",
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" button_id = self.redgrid[row][col]\n",
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" self.button = Tk.Button(self.frame, text = button_id, bg='blue', \n",
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" command= lambda bid=button_id, row=row, col=col: self.clicked(bid, row, col))\n",
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" self.button.grid(row=row, column=col)\n",
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" \n",
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" def clicked(self, bid, row, col):\n",
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" print('clicked button {} {} {}'.format(bid, row, col))\n",
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" self.find_in_grid(self.frame, row, col)\n",
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"\n",
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" def find_in_grid(self, frame, row, column):\n",
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" for children in frame.children.values():\n",
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" info = children.grid_info()\n",
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" #note that rows and column numbers are stored as string\n",
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" if info['row'] == str(row) and info['column'] == str(column):\n",
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" print('{}'.format(children.get()))\n",
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" return None\n",
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"\n",
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"root=Tk.Tk()\n",
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"app = Begueradj(root) \n",
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"root.mainloop()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"redgrid"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"for row in range(2,len(redgrid)):\n",
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" print(len(redgrid[row]))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"redgrid[1][2]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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