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| .. | ||
| README.md | ||
| attribute_treemap.py | ||
| bokeh_tools.py | ||
| date_tools.py | ||
| pygal_tools.py | ||
| style.css | ||
| style2.css | ||
| tag_scatter.py | ||
| tag_search.py | ||
| tags_count.py | ||
| tags_to_graphs.py | ||
| test_attribute_treemap.html | ||
| tools.py | ||
README.md
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 occurences 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 occurences of a given tag in a fetched sample of Events in a given period of time.
- Events will be fetched from days days ago to today.
- begindate is the beginning of the studied period. If it is later than today, an error will be raised.
- enddate is the end of the studied period. If it is earlier than begindate, an error will be raised.
- tag_search.py allows research for multiple tags is possible by separating each tag by the | symbol.
- 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.
- The studied period can be either the 7, 28 or 360 last days
- accuracy allows to get smallers splits of data instead of the default values
- order define the accuracy of the curve fitting. Default value is 3
- It will generate two plots comparing all the tags:
- tags_repartition_plot that present the raw data
- tags_repartition_trend_plot that present the general evolution for each tag
- Then each taxonomies will be represented in three plots:
- Raw datas: in "plot" folder, named with the name of the corresponding taxonomy
- Trend: in "plot" folder, named taxonomy_trend. general evolution of the data (linear fitting, curve fitting at order 1)
- Curve fitting: in "plotlib" folder, name as the taxonomy it presents.
- In order to visualize the last plots, a html file is also generated automaticaly (might be improved in the future)
⚠️ These scripts are not time optimised