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https://github.com/ail-project/ail-framework.git
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1379ef705a
AIL is a modular framework to analyse potential information leak from unstructured data source like pastes from Past ebin or similar services. AIL framework is flexible and can be extended to support other functionalities to mine sen sitive information
64 lines
1.6 KiB
Python
Executable file
64 lines
1.6 KiB
Python
Executable file
#!/usr/bin/python2.7
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# -*-coding:UTF-8 -*
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from packages.lib_refine import *
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from packages.imported import *
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def main():
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"""Main Function"""
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parser = argparse.ArgumentParser(
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description = '''This script is a part of the Analysis Information Leak
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framework. It create an histogram which display the occurency
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of the word category per days.''',
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epilog = '''The Redis database need to be populated by the script
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Classify_Paste_Token.py before.
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It's also usefull to launch Remove_longline_fp.py and Refine_with_regex.py
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to create a more accurate histogram.
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example: ./Bargraph_categ_by_day.py 2013 12 mails_categ''')
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parser.add_argument('-db',
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type = int,
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default = 0,
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help = 'The name of the Redis DB (default 0)',
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choices=[0, 1, 2, 3, 4],
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action = 'store')
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parser.add_argument('-f',
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type = str,
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metavar = "filename",
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default = "figure",
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help = 'The absolute path name of the "figure.png"',
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action = 'store')
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parser.add_argument('y',
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type = int,
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metavar = "year",
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help = 'The year processed',
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action = 'store')
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parser.add_argument('m',
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type = int,
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metavar = "month",
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help = 'The month processed',
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action = 'store')
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parser.add_argument('key',
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type = str,
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help ='name of the key to process in redis (the word_categ concerned)',
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action = 'store')
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args = parser.parse_args()
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r = redis.StrictRedis(
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host='localhost',
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port=6379,
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db=args.db)
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p = r.pipeline(False)
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graph_categ_by_day(r, args.f, args.y, args.m, args.key)
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if __name__ == "__main__":
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main()
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