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https://github.com/autistic-symposium/sec-pentesting-toolkit.git
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86 lines
2 KiB
Python
86 lines
2 KiB
Python
#!/usr/bin/env python
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__author__ = "Mia Stein"
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'''
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This program calculate the frequency of letters in a files
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so we can use this for cryptoanalysis later.
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For example, the 10 most frequent words in english:
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e -> 0.104
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t -> 0.072
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a -> 0.065
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0 -> 0.059
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n -> 0.056
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i -> 0.055
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s -> 0.051
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r -> 0.049
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h -> 0.049
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d -> 0.034
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'''
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import chardet
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from collections import defaultdict
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# calculate the mean values from the table
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def taste_like_english(dict_mean, word):
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mean_word = 0
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counter = 0
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for c in word:
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if c in dict_mean.keys():
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mean_word += dict_mean[c]
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counter += 1
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return mean_word/counter
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# count number of letters
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def count_letters(FILE):
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dict_letters = defaultdict(int)
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with open(FILE) as file:
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for line in file:
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for word in line.lower().split():
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for c in word:
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if c!='!' and c!="," and c!="-" and c!="."\
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and c!=";" and chardet.detect(c)['encoding'] == 'ascii':
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dict_letters[c] += 1
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return dict_letters
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# calculate the frequency for the letters
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def calculate_mean(dict_letters):
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dict_mean = defaultdict(float)
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sum_all = sum(dict_letters.values())
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for letter in sorted(dict_letters.keys()):
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dict_mean[letter] = float(dict_letters[letter])/sum_all
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return dict_mean
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# first, test letters with official values
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def test_values():
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dict_letters_test = {'e':0.104, 't':0.072, 'a':0.065, 'o':0.059, \
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'n': 0.056, 'i': 0.055, 's':0.051, 'r':0.049, 'h':0.049, 'd':0.034}
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print('Test for "english": ', taste_like_english(dict_letters_test, 'english')) # == 0.045"
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# now, test with some file, creating dictionary of letters
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def test_file():
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dict_letters = count_letters(FILE)
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dict_mean = calculate_mean(dict_letters)
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for key in sorted(dict_mean, key=dict_mean.get, reverse=True):
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print(key + ' --> ' + str(dict_mean[key]))
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if __name__ == '__main__':
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test_values()
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FILE = 'Ariel_Sylvia_Plath.txt'
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test_file()
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