mirror of https://github.com/MISP/PyMISP
GitHub
commit
63270d6eaa
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@ -0,0 +1,25 @@
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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from pymisp import PyMISP
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from keys import misp_url, misp_key
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import argparse
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# For python2 & 3 compat, a bit dirty, but it seems to be the least bad one
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try:
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input = raw_input
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except NameError:
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pass
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def init(url, key):
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return PyMISP(url, key, True, 'json')
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Get a list of the sharing groups from the MISP instance.')
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misp = init(misp_url, misp_key)
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sharing_groups = misp.get_sharing_groups()
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print sharing_groups
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@ -12,8 +12,22 @@
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* tag\_search.py allows research for multiple tags is possible by separating each tag by the | symbol.
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* Partial research is also possible with tag\_search.py. For instance, search for "ransom" will also return tags containin "ransomware".
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* tags\_to\_graphs.py is a script that will generate several plots to visualise tags distribution.
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* The studied _period_ can be either the 7, 28 or 360 last days
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* _accuracy_ allows to get smallers splits of data instead of the default values
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* _order_ define the accuracy of the curve fitting. Default value is 3
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* It will generate two plots comparing all the tags:
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* tags_repartition_plot that present the raw data
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* tags_repartition_trend_plot that present the general evolution for each tag
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* Then each taxonomies will be represented in three plots:
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* Raw datas: in "plot" folder, named with the name of the corresponding taxonomy
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* Trend: in "plot" folder, named _taxonomy_\_trend. general evolution of the data (linear fitting, curve fitting at order 1)
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* Curve fitting: in "plotlib" folder, name as the taxonomy it presents.
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* In order to visualize the last plots, a html file is also generated automaticaly (might be improved in the future)
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:warning: These scripts are not time optimised
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## Requierements
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* [Pygal](https://github.com/Kozea/pygal/)
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* [Matplotlib](https://github.com/matplotlib/matplotlib)
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@ -29,11 +29,15 @@ table td
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{
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border-left: 1px solid #cbcbcb;
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border-width: 0 0 0 1px;
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width: 150px;
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width: 500px;
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margin: 0;
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padding: 0.5em 1em;
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}
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.test
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{
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width: 500px;
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}
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table tr:nth-child(2n-1) td
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{
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@ -0,0 +1,41 @@
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body
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{
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/*font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;*/
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font-family: Consolas, "Liberation Mono", Menlo, Courier, monospace;
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}
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h1
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{
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font-size: 16px;
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width: 290px;
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text-align:center;
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}
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/*** Stats Tables ***/
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table
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{
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border-collapse: collapse;
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border-spacing: 0;
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table-layout: fixed;
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width: 6000px;
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border: 1px solid #cbcbcb;
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}
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tbody
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{
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font-size:12px;
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}
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td
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{
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border-left: 1px solid #cbcbcb;
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border-width: 0 0 0 1px;
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margin: 0;
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padding: 0.5em 1em;
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}
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table tr td:first-child
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{
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font-weight: bold;
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}
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@ -0,0 +1,91 @@
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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from pymisp import PyMISP
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from keys import misp_url, misp_key, misp_verifycert
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import argparse
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import tools
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def formattingDataframe(dataframe, dates, NanValue):
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dataframe.reverse()
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dates.reverse()
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dataframe = tools.concat(dataframe)
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dataframe = tools.renameColumns(dataframe, dates)
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dataframe = tools.replaceNaN(dataframe, 0)
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return dataframe
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Show the evolution of trend of tags.')
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parser.add_argument("-p", "--period", help='Define the studied period. Can be the past year (y), month (m) or week (w). Week is the default value if no valid value is given.')
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parser.add_argument("-a", "--accuracy", help='Define the accuracy of the splits on the studied period. Can be per month (m) -for year only-, week (w) -month only- or day (d). The default value is always the biggest available.')
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parser.add_argument("-o", "--order", type=int, help='Define the accuracy of the curve fitting. Default value is 3')
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args = parser.parse_args()
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misp = PyMISP(misp_url, misp_key, misp_verifycert)
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if args.period == "y":
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if args.accuracy == "d":
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split = 360
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size = 1
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else:
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split = 12
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size = 30
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last = '360d'
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title = 'Tags repartition over the last 360 days'
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elif args.period == "m":
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if args.accuracy == "d":
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split = 28
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size = 1
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else:
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split = 4
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size = 7
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last = '28d'
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title = 'Tags repartition over the last 28 days'
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else:
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split = 7
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size = 1
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last = '7d'
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title = 'Tags repartition over the last 7 days'
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result = misp.download_last(last)
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events = tools.eventsListBuildFromArray(result)
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result = []
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dates = []
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enddate = tools.getToday()
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colourDict = {}
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faketag = False
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for i in range(split):
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begindate = tools.getNDaysBefore(enddate, size)
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dates.append(str(enddate.date()))
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eventstemp = tools.selectInRange(events, begin=begindate, end=enddate)
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if eventstemp is not None:
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tags = tools.tagsListBuild(eventstemp)
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if tags is not None:
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tools.createDictTagsColour(colourDict, tags)
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result.append(tools.getNbOccurenceTags(tags))
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else:
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result.append(tools.createFakeEmptyTagsSeries())
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faketag = True
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else:
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result.append(tools.createFakeEmptyTagsSeries())
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faketag = True
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enddate = begindate
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result = formattingDataframe(result, dates, 0)
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if faketag:
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result = tools.removeFaketagRow(result)
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taxonomies, emptyOther = tools.getTaxonomies(tools.getCopyDataframe(result))
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tools.tagsToLineChart(tools.getCopyDataframe(result), title, dates, colourDict)
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tools.tagstrendToLineChart(tools.getCopyDataframe(result), title, dates, split, colourDict)
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tools.tagsToTaxoLineChart(tools.getCopyDataframe(result), title, dates, colourDict, taxonomies, emptyOther)
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tools.tagstrendToTaxoLineChart(tools.getCopyDataframe(result), title, dates, split, colourDict, taxonomies, emptyOther)
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if args.order is None:
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args.order = 3
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tools.tagsToPolyChart(tools.getCopyDataframe(result), split, colourDict, taxonomies, emptyOther, args.order)
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tools.createVisualisation(taxonomies)
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@ -9,8 +9,13 @@ import pandas
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from datetime import datetime
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from datetime import timedelta
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from dateutil.parser import parse
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# ############### Errors ################
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import numpy
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from scipy import stats
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from pytaxonomies import Taxonomies
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import re
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import matplotlib.pyplot as plt
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from matplotlib import pylab
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import os
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class DateError(Exception):
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@ -20,30 +25,8 @@ class DateError(Exception):
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def __str__(self):
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return repr(self.value)
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# ############### Tools ################
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def buildDoubleIndex(index1, index2, datatype):
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it = -1
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newindex1 = []
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for index in index2:
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if index == 0:
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it += 1
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newindex1.append(index1[it])
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arrays = [newindex1, index2]
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tuples = list(zip(*arrays))
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return pandas.MultiIndex.from_tuples(tuples, names=['event', datatype])
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def buildNewColumn(index2, column):
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it = -1
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newcolumn = []
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for index in index2:
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if index == 0:
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it += 1
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newcolumn.append(column[it])
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return newcolumn
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# ############### Date Tools ################
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def dateInRange(datetimeTested, begin=None, end=None):
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if begin is None:
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@ -53,10 +36,6 @@ def dateInRange(datetimeTested, begin=None, end=None):
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return begin <= datetimeTested <= end
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def addColumn(dataframe, columnList, columnName):
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dataframe.loc[:, columnName] = pandas.Series(columnList, index=dataframe.index)
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def toDatetime(date):
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return parse(date)
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@ -86,6 +65,115 @@ def setEnddate(enddate):
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def getLastdate(last):
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return (datetime.now() - timedelta(days=int(last))).replace(hour=0, minute=0, second=0, microsecond=0)
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def getNDaysBefore(date, days):
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return (date - timedelta(days=days)).replace(hour=0, minute=0, second=0, microsecond=0)
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def getToday():
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return (datetime.now()).replace(hour=0, minute=0, second=0, microsecond=0)
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# ############### Tools ################
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def getTaxonomies(dataframe):
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taxonomies = Taxonomies()
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taxonomies = list(taxonomies.keys())
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notInTaxo = []
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count = 0
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for taxonomy in taxonomies:
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empty = True
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for it in dataframe.iterrows():
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if it[0].startswith(taxonomy):
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empty = False
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dataframe = dataframe.drop([it[0]])
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count = count + 1
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if empty is True:
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notInTaxo.append(taxonomy)
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if dataframe.empty:
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emptyOther = True
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else:
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emptyOther = False
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for taxonomy in notInTaxo:
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taxonomies.remove(taxonomy)
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return taxonomies, emptyOther
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def buildDoubleIndex(index1, index2, datatype):
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it = -1
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newindex1 = []
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for index in index2:
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if index == 0:
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it += 1
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newindex1.append(index1[it])
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arrays = [newindex1, index2]
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tuples = list(zip(*arrays))
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return pandas.MultiIndex.from_tuples(tuples, names=['event', datatype])
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def buildNewColumn(index2, column):
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it = -1
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newcolumn = []
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for index in index2:
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if index == 0:
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it += 1
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newcolumn.append(column[it])
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return newcolumn
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def addColumn(dataframe, columnList, columnName):
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dataframe.loc[:, columnName] = pandas.Series(columnList, index=dataframe.index)
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def concat(data):
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return pandas.concat(data, axis=1)
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def createFakeEmptyTagsSeries():
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return pandas.Series({'Faketag': 0})
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def removeFaketagRow(dataframe):
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return dataframe.drop(['Faketag'])
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def getCopyDataframe(dataframe):
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return dataframe.copy()
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def createDictTagsColour(colourDict, tags):
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temp = tags.groupby(['name', 'colour']).count()['id']
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levels_name = temp.index.levels[0]
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levels_colour = temp.index.levels[1]
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labels_name = temp.index.labels[0]
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labels_colour = temp.index.labels[1]
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for i in range(len(labels_name)):
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colourDict[levels_name[labels_name[i]]] = levels_colour[labels_colour[i]]
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def createTagsPlotStyle(dataframe, colourDict, taxonomy=None):
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colours = []
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if taxonomy is not None:
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for it in dataframe.iterrows():
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if it[0].startswith(taxonomy):
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colours.append(colourDict[it[0]])
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else:
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for it in dataframe.iterrows():
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colours.append(colourDict[it[0]])
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style = Style(background='transparent',
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plot_background='#eeeeee',
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foreground='#111111',
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foreground_strong='#111111',
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foreground_subtle='#111111',
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opacity='.6',
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opacity_hover='.9',
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transition='400ms ease-in',
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colors=tuple(colours))
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return style
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# ############### Formatting ################
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@ -129,15 +217,19 @@ def attributesListBuild(events):
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def tagsListBuild(Events):
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Tags = []
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for Tag in Events['Tag']:
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if type(Tag) is not list:
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continue
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Tags.append(pandas.DataFrame(Tag))
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Tags = pandas.concat(Tags)
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columnDate = buildNewColumn(Tags.index, Events['date'])
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addColumn(Tags, columnDate, 'date')
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index = buildDoubleIndex(Events.index, Tags.index, 'tag')
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Tags = Tags.set_index(index)
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if 'Tag' in Events.columns:
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for Tag in Events['Tag']:
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if type(Tag) is not list:
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continue
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Tags.append(pandas.DataFrame(Tag))
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if Tags:
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Tags = pandas.concat(Tags)
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columnDate = buildNewColumn(Tags.index, Events['date'])
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addColumn(Tags, columnDate, 'date')
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index = buildDoubleIndex(Events.index, Tags.index, 'tag')
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Tags = Tags.set_index(index)
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else:
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Tags = None
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return Tags
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@ -148,6 +240,8 @@ def selectInRange(Events, begin=None, end=None):
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inRange.append(Event.tolist())
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inRange = pandas.DataFrame(inRange)
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temp = Events.columns.tolist()
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if inRange.empty:
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return None
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inRange.columns = temp
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return inRange
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@ -160,6 +254,15 @@ def isTagIn(dataframe, tag):
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index.append(temp[i][0])
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return index
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def renameColumns(dataframe, namelist):
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dataframe.columns = namelist
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return dataframe
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def replaceNaN(dataframe, value):
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return dataframe.fillna(value)
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# ############### Basic Stats ################
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@ -212,7 +315,7 @@ def createTreemap(data, title, treename='attribute_treemap.svg', tablename='attr
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transition='400ms ease-in',
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colors=tuple(colors.values()))
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treemap = pygal.Treemap(pretty_print=True, legend_at_bottom=True, style=style, explicit_size=True, width=2048, height=2048)
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treemap = pygal.Treemap(pretty_print=True, legend_at_bottom=True, style=style)
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treemap.title = title
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treemap.print_values = True
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treemap.print_labels = True
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|
|
@ -222,3 +325,171 @@ def createTreemap(data, title, treename='attribute_treemap.svg', tablename='attr
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createTable(colors, categ_types_hash)
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treemap.render_to_file(treename)
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def tagsToLineChart(dataframe, title, dates, colourDict):
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style = createTagsPlotStyle(dataframe, colourDict)
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line_chart = pygal.Line(x_label_rotation=20, style=style, show_legend=False)
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line_chart.title = title
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line_chart.x_labels = dates
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for it in dataframe.iterrows():
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line_chart.add(it[0], it[1].tolist())
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line_chart.render_to_file('tags_repartition_plot.svg')
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def tagstrendToLineChart(dataframe, title, dates, split, colourDict):
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style = createTagsPlotStyle(dataframe, colourDict)
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line_chart = pygal.Line(x_label_rotation=20, style=style, show_legend=False)
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line_chart.title = title
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line_chart.x_labels = dates
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xi = numpy.arange(split)
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for it in dataframe.iterrows():
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slope, intercept, r_value, p_value, std_err = stats.linregress(xi, it[1])
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line = slope * xi + intercept
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line_chart.add(it[0], line, show_dots=False)
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line_chart.render_to_file('tags_repartition_trend_plot.svg')
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def tagsToTaxoLineChart(dataframe, title, dates, colourDict, taxonomies, emptyOther):
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style = createTagsPlotStyle(dataframe, colourDict)
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line_chart = pygal.Line(x_label_rotation=20, style=style)
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line_chart.title = title
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line_chart.x_labels = dates
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for taxonomy in taxonomies:
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taxoStyle = createTagsPlotStyle(dataframe, colourDict, taxonomy)
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taxo_line_chart = pygal.Line(x_label_rotation=20, style=taxoStyle)
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taxo_line_chart.title = title + ': ' + taxonomy
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taxo_line_chart.x_labels = dates
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for it in dataframe.iterrows():
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if it[0].startswith(taxonomy):
|
||||
taxo_line_chart.add(re.sub(taxonomy + ':', '', it[0]), it[1].tolist())
|
||||
dataframe = dataframe.drop([it[0]])
|
||||
taxo_line_chart.render_to_file('plot/' + taxonomy + '.svg')
|
||||
|
||||
if not emptyOther:
|
||||
taxoStyle = createTagsPlotStyle(dataframe, colourDict)
|
||||
taxo_line_chart = pygal.Line(x_label_rotation=20, style=taxoStyle)
|
||||
taxo_line_chart.title = title + ': other'
|
||||
taxo_line_chart.x_labels = dates
|
||||
for it in dataframe.iterrows():
|
||||
taxo_line_chart.add(it[0], it[1].tolist())
|
||||
taxo_line_chart.render_to_file('plot/other.svg')
|
||||
|
||||
|
||||
def tagstrendToTaxoLineChart(dataframe, title, dates, split, colourDict, taxonomies, emptyOther):
|
||||
style = createTagsPlotStyle(dataframe, colourDict)
|
||||
line_chart = pygal.Line(x_label_rotation=20, style=style)
|
||||
line_chart.title = title
|
||||
line_chart.x_labels = dates
|
||||
xi = numpy.arange(split)
|
||||
for taxonomy in taxonomies:
|
||||
taxoStyle = createTagsPlotStyle(dataframe, colourDict, taxonomy)
|
||||
taxo_line_chart = pygal.Line(x_label_rotation=20, style=taxoStyle)
|
||||
taxo_line_chart.title = title + ': ' + taxonomy
|
||||
taxo_line_chart.x_labels = dates
|
||||
for it in dataframe.iterrows():
|
||||
if it[0].startswith(taxonomy):
|
||||
slope, intercept, r_value, p_value, std_err = stats.linregress(xi, it[1])
|
||||
line = slope * xi + intercept
|
||||
taxo_line_chart.add(re.sub(taxonomy + ':', '', it[0]), line, show_dots=False)
|
||||
dataframe = dataframe.drop([it[0]])
|
||||
taxo_line_chart.render_to_file('plot/' + taxonomy + '_trend.svg')
|
||||
|
||||
if not emptyOther:
|
||||
taxoStyle = createTagsPlotStyle(dataframe, colourDict)
|
||||
taxo_line_chart = pygal.Line(x_label_rotation=20, style=taxoStyle)
|
||||
taxo_line_chart.title = title + ': other'
|
||||
taxo_line_chart.x_labels = dates
|
||||
for it in dataframe.iterrows():
|
||||
slope, intercept, r_value, p_value, std_err = stats.linregress(xi, it[1])
|
||||
line = slope * xi + intercept
|
||||
taxo_line_chart.add(it[0], line, show_dots=False)
|
||||
taxo_line_chart.render_to_file('plot/other_trend.svg')
|
||||
|
||||
|
||||
def tagsToPolyChart(dataframe, split, colourDict, taxonomies, emptyOther, order):
|
||||
for taxonomy in taxonomies:
|
||||
for it in dataframe.iterrows():
|
||||
if it[0].startswith(taxonomy):
|
||||
points = []
|
||||
for i in range(split):
|
||||
points.append((i, it[1][i]))
|
||||
color = colourDict[it[0]]
|
||||
label = re.sub(taxonomy + ':', '', it[0])
|
||||
points = numpy.array(points)
|
||||
dataframe = dataframe.drop([it[0]])
|
||||
|
||||
# get x and y vectors
|
||||
x = points[:, 0]
|
||||
y = points[:, 1]
|
||||
|
||||
# calculate polynomial
|
||||
z = numpy.polyfit(x, y, order)
|
||||
f = numpy.poly1d(z)
|
||||
|
||||
# calculate new x's and y's
|
||||
x_new = numpy.linspace(x[0], x[-1], 50)
|
||||
y_new = f(x_new)
|
||||
|
||||
plt.plot(x, y, '.', color=color)
|
||||
plt.plot(x_new, y_new, color=color, label=label + 'trend')
|
||||
|
||||
pylab.title('Polynomial Fit with Matplotlib: ' + taxonomy)
|
||||
pylab.legend(loc='center left', bbox_to_anchor=(1, 0.5))
|
||||
ax = plt.gca()
|
||||
ax.set_facecolor((0.898, 0.898, 0.898))
|
||||
box = ax.get_position()
|
||||
ax.set_position([box.x0 - 0.01, box.y0, box.width * 0.78, box.height])
|
||||
fig = plt.gcf()
|
||||
fig.set_size_inches(20, 15)
|
||||
fig.savefig('plotlib/' + taxonomy + '.png')
|
||||
fig.clf()
|
||||
|
||||
if not emptyOther:
|
||||
for it in dataframe.iterrows():
|
||||
points = []
|
||||
for i in range(split):
|
||||
points.append((i, it[1][i]))
|
||||
|
||||
color = colourDict[it[0]]
|
||||
label = it[0]
|
||||
points = numpy.array(points)
|
||||
|
||||
# get x and y vectors
|
||||
x = points[:, 0]
|
||||
y = points[:, 1]
|
||||
|
||||
# calculate polynomial
|
||||
z = numpy.polyfit(x, y, order)
|
||||
f = numpy.poly1d(z)
|
||||
|
||||
# calculate new x's and y's
|
||||
x_new = numpy.linspace(x[0], x[-1], 50)
|
||||
y_new = f(x_new)
|
||||
|
||||
plt.plot(x, y, '.', color=color, label=label)
|
||||
plt.plot(x_new, y_new, color=color, label=label + 'trend')
|
||||
|
||||
pylab.title('Polynomial Fit with Matplotlib: other')
|
||||
pylab.legend(loc='center left', bbox_to_anchor=(1, 0.5))
|
||||
ax = plt.gca()
|
||||
ax.set_facecolor((0.898, 0.898, 0.898))
|
||||
box = ax.get_position()
|
||||
ax.set_position([box.x0 - 0.01, box.y0, box.width * 0.78, box.height])
|
||||
fig = plt.gcf()
|
||||
fig.set_size_inches(20, 15)
|
||||
fig.savefig('plotlib/other.png')
|
||||
|
||||
|
||||
def createVisualisation(taxonomies):
|
||||
chain = '<!DOCTYPE html>\n<html>\n\t<head>\n\t\t<link rel="stylesheet" href="style2.css">\n\t</head>\n\t<body>'
|
||||
chain = chain + '<table>'
|
||||
for taxonomy in taxonomies:
|
||||
chain = chain + '<tr><td><object type="image/svg+xml" data="plot\\' + taxonomy + '.svg"></object></td><td><img src="plotlib\\' + taxonomy + '.png" alt="graph" /></td><td><object type="image/svg+xml" data="plot\\' + taxonomy + '_trend.svg"></object></td></tr>\n'
|
||||
|
||||
chain = chain + '<tr><td><object type="image/svg+xml" data="plot\other.svg"></object></td><td><img src="plotlib\other.png" alt="graph" /></td><td><object type="image/svg+xml" data="plot\other_trend.svg"></object></td></tr>\n'
|
||||
chain = chain + '</table>'
|
||||
chain = chain + '\n\t</body>\n</html>'
|
||||
|
||||
with open('test_tags_trend.html', 'w') as target:
|
||||
target.write(chain)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,28 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
from pymisp import PyMISP
|
||||
from keys import misp_url, misp_key, misp_verifycert
|
||||
import argparse
|
||||
import json
|
||||
|
||||
# For python2 & 3 compat, a bit dirty, but it seems to be the least bad one
|
||||
try:
|
||||
input = raw_input
|
||||
except NameError:
|
||||
pass
|
||||
|
||||
|
||||
def init(url, key):
|
||||
return PyMISP(url, key, misp_verifycert, 'json')
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='Get statistics from tags.')
|
||||
parser.add_argument("-p", "--percentage", help="An optional field, if set, it will return the results in percentages, otherwise it returns exact count.")
|
||||
parser.add_argument("-n", "--namesort", help="An optional field, if set, values are sort by the namespace, otherwise the sorting will happen on the value.")
|
||||
args = parser.parse_args()
|
||||
|
||||
misp = init(misp_url, misp_key)
|
||||
|
||||
stats = misp.get_tags_statistics(args.percentage, args.namesort)
|
||||
print json.dumps(stats)
|
||||
Loading…
Reference in New Issue