1 files changed, 0 insertions, 73 deletions

diff --git a/modules/levenshtein.py b/modules/levenshtein.py
deleted file mode 100644
index 2e854e2..0000000
--- a/modules/levenshtein.py
+++ /dev/null
@@ -1,73 +0,0 @@
-'''
-This modules has a super-awful complexity (something along n^4),
-so I'm quite sure that you don't want to run it by default ;)
-
-Anyway, this modules computes the Levenshtein distance between samples of malwares
-and files to check, to find similarities.
-'''
-import os
-
-import scanmodule
-
-def main():
-    return Levenshtein()
-
-class Levenshtein(scanmodule.ScanModule):
-    name = 'levenshtein'
-    def populate(self, path):
-        ''' We can't really populate the database with Levenshtein scores,
-        but we can speedup the calculation by storing files lenghts
-        '''
-        for root, _, filenames in os.walk(path):
-            for filename in filenames:
-                full_path = os.path.join(root, filename)
-                with open(full_path, 'r') as f:
-                    self.samples[full_path] = [os.path.getsize(full_path), f.read().lower()]
-
-    def evaluate(self, path):
-        ''' Compare the hash of the given path to every samples one.
-        @ret A sorted list of the form [name, match_in_percent_superior_to_zero]
-        '''
-        file_to_test = path
-        file_size = os.path.getsize(file_to_test)
-
-        lst = list()
-        for sample_name, sample_intel in self.samples.iteritems():
-            if sample_name != file_to_test:
-                score = self.__levenshtein(file_to_test, sample_intel[1])
-                score = score / ((file_size + sample_intel[0]) / 2.0)  # mean value
-                if score > 25:  # if the match is under 10%, we don't care
-                    lst.append([sample_name, score * 10])
-        return sorted(lst, key=lambda lst: lst[1], reverse=True)
-
-    def __levenshtein_file(self, f, b):
-        ''' Computes the Levenshtein's distance between a file and a buffer
-        @param f1 File
-        @param fs2 Buffer
-        @return The levenshtein distance
-        '''
-        with open(f, 'r') as of:
-            return self.__levenshtein(of.read().lower(), b)
-
-    def __levenshtein(self, s1, s2):
-        ''' Computes the Levenshtein's distance between two strings
-        @param s1 First string
-        @param s2 Second string
-        @return The levenshtein distance
-        '''
-
-        if len(s1) < len(s2):  # Minimize computation
-            s1, s2 = s2, s1
-
-        previous_row = range(len(s2) + 1)
-        for i, c1 in enumerate(s1):
-            current_row = [i + 1]
-            for j, c2 in enumerate(s2):
-                insertions = previous_row[j + 1] + 1
-                deletions = current_row[j] + 1
-                substitutions = previous_row[j] + (c1 != c2)
-                current_row.append(min(insertions, deletions, substitutions))
-            previous_row = current_row
-
-        return previous_row[-1]
-