mirror of https://github.com/MISP/PyMISP
39 lines
2.6 KiB
Markdown
39 lines
2.6 KiB
Markdown
## Explanation
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* treemap.py is a script that will generate an interactive svg (attribute\_treemap.svg) containing a treepmap representing the distribution of attributes in a sample (data) fetched from the instance using "last" or "searchall" examples.
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* It will also generate a html document with a table (attribute\_table.html) containing count for each type of attribute.
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* test\_attribute\_treemap.html is a quick page made to visualize both treemap and table at the same time.
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* tags\_count.py is a script that count the number of occurrences of every tags in a fetched sample of Events in a given period of time.
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* tag\_search.py is a script that count the number of occurrences of a given tag in a fetched sample of Events in a given period of time.
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* Events will be fetched from _days_ days ago to today.
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* _begindate_ is the beginning of the studied period. If it is later than today, an error will be raised.
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* _enddate_ is the end of the studied period. If it is earlier than _begindate_, an error will be raised.
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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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* [Pandas](https://github.com/pandas-dev/pandas)
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* [SciPy](https://github.com/scipy/scipy)
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* [PyTaxonomies](https://github.com/MISP/PyTaxonomies)
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* [Python3-tk](https://github.com/python-git/python/blob/master/Lib/lib-tk/Tkinter.py)
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