├── vocab.py ├── README.md └── LICENSE.txt /vocab.py: -------------------------------------------------------------------------------- 1 | # coding: utf-8 2 | # Copyright (C) 2017 FireEye, Inc. All Rights Reserved. 3 | # 4 | # Reads source files (*.{c,h,cpp,hpp,txt}) as code page 1251 5 | # Writes a prioritized vocabularly list in UTF-8. 6 | # 7 | # For details, see README.md 8 | 9 | import os 10 | import re 11 | import codecs 12 | import string 13 | import fnmatch 14 | import argparse 15 | import operator 16 | from collections import defaultdict 17 | 18 | def parse(f, word_frequencies, encoding='cp1251'): 19 | with codecs.open(f, 'r', encoding) as infile: 20 | for line in infile.readlines(): 21 | # Omit ASCII characters (between space (0x20) and tilde (0x7e)) 22 | words = re.split('[ -~]+', line) 23 | for word in words: 24 | word = word.strip(' \t\r\n\0') 25 | if len(word): 26 | word_frequencies[word] += 1 27 | 28 | def parse_args(): 29 | desc = 'Vocabulary scraper' 30 | parser = argparse.ArgumentParser(description=desc) 31 | parser.add_argument('outfile', help='Output file') 32 | parser.add_argument('--startdir', default='.', help='Directory to recurse') 33 | parser.add_argument('--ienc', default='cp1251', help='Input encoding') 34 | parser.add_argument('--oenc', default='utf-8', help='Output encoding') 35 | return parser.parse_args() 36 | 37 | def main(): 38 | args = parse_args() 39 | 40 | word_frequencies = defaultdict(int) 41 | 42 | files = [] 43 | masks = ['*.c', '*.h', '*.cpp', '*.hpp', '*.txt', '*.cs'] 44 | for root, dirnames, filenames in os.walk(args.startdir): 45 | for mask in masks: 46 | for filename in fnmatch.filter(filenames, mask): 47 | files.append(os.path.join(root, filename)) 48 | 49 | for file in files: 50 | try: 51 | parse(file, word_frequencies, args.ienc) 52 | except UnicodeDecodeError: 53 | pass 54 | 55 | sorted_words = sorted(word_frequencies.items(), key=operator.itemgetter(1)) 56 | sorted_words.reverse() 57 | with codecs.open(args.outfile, 'w', args.oenc) as outfile: 58 | for s, n in sorted_words: 59 | outfile.write(str(n) + ': ' + s + '\r\n') 60 | 61 | if __name__ == '__main__': 62 | main() 63 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Vocabulary Scraper 2 | 3 | Vocabulary Scraper is meant to aid analysis of foreign-language codebases. It 4 | reads source files (`*.{c,h,cpp,hpp,txt}`) and writes a prioritized vocabularly 5 | list in UTF-8. It was written and used by FLARE to [analyze the Carbanak source 6 | code](https://www.fireeye.com/blog/threat-research/2019/04/carbanak-week-part-one-a-rare-occurrence), 7 | and accordingly, the default setting is to read files in code page 1251 8 | (Cyrillic). 9 | 10 | ## Usage 11 | 12 | Vocabulary Scraper accepts the following arguments: 13 | 14 | ``` 15 | usage: vocab.py [-h] [--startdir STARTDIR] [--ienc IENC] [--oenc OENC] outfile 16 | 17 | Vocabulary scraper 18 | 19 | positional arguments: 20 | outfile Output file 21 | 22 | optional arguments: 23 | -h, --help show this help message and exit 24 | --startdir STARTDIR Directory to recurse 25 | --ienc IENC Input encoding 26 | --oenc OENC Output encoding 27 | ``` 28 | 29 | The default input encoding is `cp1251` and the default output encoding is 30 | `utf-8`. Any character encoding name recognized by the Python standard 31 | libraries should work. 32 | 33 | ## Limitations relating to Linguistic Concerns 34 | Vocabulary Scraper is best for languages like Russian where words are 35 | conveniently delimited by spaces. In languages where spaces are not mandated 36 | between words (like Chinese and Japanese), Vocabulary Scraper might still 37 | extract all the foreign language text, however it will not be able to break the 38 | symbol sequences out into individual vocabulary words to study if there are no 39 | spaces. 40 | 41 | Vocabulary Scraper does not aggregate or coalesce entries with common word 42 | roots due to plural, different verb conjugations, etc. In a sufficiently large 43 | corpus, it may help to groom the list manually to coalesce like terms. 44 | 45 | ## Limitations relating to Character Encoding 46 | Vocabulary Scraper currently does not coalesce words having alternate encodings 47 | of the same character(s), meaning it could produce a list containing one or 48 | more sets of words are visually identical but are counted as distinct based on 49 | their differing Unicode code points. This has not yet been observed in 50 | operation, but if you observe this, this might explain the cause. 51 | 52 | As of this writing, Vocabulary Scraper is ignorant of the real encoding of its 53 | input files (use the command line argument --ienc to specify the encoding, 54 | default cp1251). Heuristic-based sniffing of the character encoding is 55 | possible via libraries like `chardet`. 56 | 57 | This script is also ignorant of the erroneous "words" that can arise from 58 | incorrectly assuming the character encoding. If you do not add any heuristic 59 | character encoding detection, then you may need to observe results and adjust 60 | by performing a survey of the files in your corpus and partitioning them 61 | according to character encoding to run them in separate runs of this tool 62 | (with different character encodings assumed), then merge and sort the text 63 | numerically (and once again coalesce duplicates and near-duplicates). 64 | 65 | The book "Fluent Python" (Luciano Ramalho, O'Reilly, 2015) has a whole chapter 66 | that treats Python character encoding topics in great detail if you are looking 67 | for a reference. 68 | -------------------------------------------------------------------------------- /LICENSE.txt: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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