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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
54 lines
1.2 KiB
Python
Executable file
54 lines
1.2 KiB
Python
Executable file
#!/usr/bin/python2.7
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# -*-coding:UTF-8 -*
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from packages.lib_words import *
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from packages.imported import *
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from pubsublogger import publisher
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def main():
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"""Main Function"""
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parser = argparse.ArgumentParser(
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description = 'Analysis Information Leak framework',
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epilog = 'example : seq 2 | parallel ./Wordsranking_Populate.py -nbp 20')
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parser.add_argument('-nbp',
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type = int,
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default = 200,
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help = 'nbpaste',
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action = 'store')
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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('-min',
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type = int,
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default = 4,
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help = 'Minimum length of the inserted words (default 4)',
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action = 'store')
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parser.add_argument('-max',
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type = int,
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default = 200,
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help = 'Maximum length of the inserted words (default 200)',
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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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publisher.channel = "youpi"
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redis_words_ranking(p, r, args.nbp, args.min, args.max)
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if __name__ == "__main__":
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main()
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