def __init__(self, raw_text=None, text_title=None):
try:
# props for internal use
self._raw_text = raw_text
self._text_title = text_title
# props to store data
self._summary = str()
self._keywords = set()
self._iocs = dict()
self._tlp = None
self._debug = dict({'iocs': dict(), 'keywords': dict()})
if self._raw_text != None:
if not type(self._raw_text) is unicode:
self._raw_text = self._raw_text.decode('utf8')
self._tlpfilter = TLPFilter()
self._clean_text = self._tlpfilter.text(self._raw_text)
self._blob = TextBlob(self._raw_text)
self._clean_blob = TextBlob(self._clean_text)
except Exception as e:
import traceback
traceback.print_exc()
def __init__(self, raw_text=None, text_title=None):
try:
# props for internal use
self._raw_text = raw_text
self._text_title = text_title
# props to store data
self._summary = str()
self._keywords = set()
self._iocs = dict()
self._tlp = None
self._debug = dict({'iocs': dict(), 'keywords': dict()})
if self._raw_text != None:
if not type(self._raw_text) is unicode:
self._raw_text = self._raw_text.decode('utf8')
self._tlpfilter = TLPFilter()
self._clean_text = self._tlpfilter.text(self._raw_text)
self._blob = TextBlob(self._raw_text)
self._clean_blob = TextBlob(self._clean_text)
except Exception as e:
import traceback
traceback.print_exc()
class TLP:
def __init__(self, raw_text=None, text_title=None):
try:
# props for internal use
self._raw_text = raw_text
self._text_title = text_title
# props to store data
self._summary = str()
self._keywords = set()
self._iocs = dict()
self._tlp = None
self._debug = dict({'iocs': dict(), 'keywords': dict()})
if self._raw_text != None:
if not type(self._raw_text) is unicode:
self._raw_text = self._raw_text.decode('utf8')
self._tlpfilter = TLPFilter()
self._clean_text = self._tlpfilter.text(self._raw_text)
self._blob = TextBlob(self._raw_text)
self._clean_blob = TextBlob(self._clean_text)
except Exception as e:
import traceback
traceback.print_exc()
@property
def iocs(self):
'''returns a filtered list of iocs'''
try:
if len(self._iocs) > 0:
return self._iocs
# prime the dict
self._iocs = dict((k, set()) for k in regexs)
# parse iocs
data = self._tlpfilter.iocs(self._raw_text, mode='pre')
for w in data:
for name,pattern in regexs.iteritems():
if pattern.match(w):
self._iocs[name].add(w)
self._iocs = self._tlpfilter.iocs(self._iocs, mode='post')
for key in self._iocs:
self._debug['iocs'][key] = len(self._iocs[key])
return self._iocs
except Exception as e:
raise e
@property
def text(self):
'''returns the complete filtered text'''
try:
return " ".join([s.raw for s in self._clean_blob.sentences])
except Exception as e:
raise e
@property
def debug(self):
'''returns debug info - must run 'keywords' or 'iocs' to populate'''
return self._debug
@property
def summary(self):
'''returns document summary'''
try:
if len(self._summary) > 0:
return self._summary
sentences = self._clean_blob.sentences
slen = len(sentences)
sixth_pctl = int(math.floor(slen * .06))
if sixth_pctl < 8:
summ_len = sixth_pctl if sixth_pctl > 2 else 2
else:
summ_len = 8
return " ".join([s.raw for s in sentences[:summ_len]])
except Exception as e:
raise e
@property
def color(self):
'''returns tlp color (if present)'''
try:
bigrams = ngrams(self._raw_text.split(), 2)
colors = set()
for b in bigrams:
(one, two) = b
if re.search('(?:tlp|TLP)', one):
colors.add(two.lower())
return colors
except Exception as e:
raise e
@property
def keywords(self):
'''returns document keywords and occurance counts'''
try:
if len(self._keywords) > 0:
return self._keywords
#blob = TextBlob(self.summary)
blob = TextBlob(self._clean_text)
keywords = self._blob.words
keywords = self._tlpfilter.keywords(keywords)
keywords_counted = dict(Counter(keywords))
total_count = 0
keywords_dict = dict()
for word, count in keywords_counted.iteritems():
if len(word) == 0:
continue
# you're certainly not popular if you only occur once
# if you are popular, and you're longer than 3 chars, you win
total_count += count if count > 1 else 0
pos_array = nltk.pos_tag(nltk.word_tokenize(word))
w,pos = pos_array[0]
if re.search('.*[NN|NP]$', pos):
if len(w) > 3:
keywords_dict[word] = count
keyword_scores = [v for (k,v) in keywords_dict.iteritems()]
keywords_count = np.count_nonzero(keyword_scores)
keywords_mean = np.mean(keyword_scores)
keywords_std = np.std(keyword_scores)
self._debug['keywords']['total'] = sum(keyword_scores)
self._debug['keywords']['mean'] = keywords_mean
self._debug['keywords']['std'] = keywords_std
new_dict = dict([(k,v) for (k,v) in keywords_dict.iteritems() if v > (keywords_mean + (keywords_std * 4))])
self._keywords = sorted(new_dict.items(), key=operator.itemgetter(1), reverse = True)
return self._keywords
except Exception as e:
raise e
class TLP:
def __init__(self, raw_text=None, text_title=None):
try:
# props for internal use
self._raw_text = raw_text
self._text_title = text_title
# props to store data
self._summary = str()
self._keywords = set()
self._iocs = dict()
self._tlp = None
self._debug = dict({'iocs': dict(), 'keywords': dict()})
if self._raw_text != None:
if not type(self._raw_text) is unicode:
self._raw_text = self._raw_text.decode('utf8')
self._tlpfilter = TLPFilter()
self._clean_text = self._tlpfilter.text(self._raw_text)
self._blob = TextBlob(self._raw_text)
self._clean_blob = TextBlob(self._clean_text)
except Exception as e:
import traceback
traceback.print_exc()
@property
def iocs(self):
'''returns a filtered list of iocs'''
try:
if len(self._iocs) > 0:
return self._iocs
# prime the dict
self._iocs = dict((k, set()) for k in regexs)
# parse iocs
data = self._tlpfilter.iocs(self._raw_text, mode='pre')
for w in data:
for name, pattern in regexs.iteritems():
if pattern.match(w):
self._iocs[name].add(w)
self._iocs = self._tlpfilter.iocs(self._iocs, mode='post')
for key in self._iocs:
self._debug['iocs'][key] = len(self._iocs[key])
return self._iocs
except Exception as e:
raise e
@property
def text(self):
'''returns the complete filtered text'''
try:
return " ".join([s.raw for s in self._clean_blob.sentences])
except Exception as e:
raise e
@property
def debug(self):
'''returns debug info - must run 'keywords' or 'iocs' to populate'''
return self._debug
@property
def summary(self):
'''returns document summary'''
try:
if len(self._summary) > 0:
return self._summary
sentences = self._clean_blob.sentences
slen = len(sentences)
sixth_pctl = int(math.floor(slen * .06))
if sixth_pctl < 8:
summ_len = sixth_pctl if sixth_pctl > 2 else 2
else:
summ_len = 8
return " ".join([s.raw for s in sentences[:summ_len]])
except Exception as e:
raise e
@property
def color(self):
'''returns tlp color (if present)'''
try:
bigrams = ngrams(self._raw_text.split(), 2)
colors = set()
for b in bigrams:
(one, two) = b
if re.search('(?:tlp|TLP)', one):
colors.add(two.lower())
return colors
except Exception as e:
raise e
@property
def keywords(self):
'''returns document keywords and occurance counts'''
try:
if len(self._keywords) > 0:
return self._keywords
#blob = TextBlob(self.summary)
blob = TextBlob(self._clean_text)
keywords = self._blob.words
keywords = self._tlpfilter.keywords(keywords)
keywords_counted = dict(Counter(keywords))
total_count = 0
keywords_dict = dict()
for word, count in keywords_counted.iteritems():
if len(word) == 0:
continue
# you're certainly not popular if you only occur once
# if you are popular, and you're longer than 3 chars, you win
total_count += count if count > 1 else 0
pos_array = nltk.pos_tag(nltk.word_tokenize(word))
w, pos = pos_array[0]
if re.search('.*[NN|NP]$', pos):
if len(w) > 3:
keywords_dict[word] = count
keyword_scores = [v for (k, v) in keywords_dict.iteritems()]
keywords_count = np.count_nonzero(keyword_scores)
keywords_mean = np.mean(keyword_scores)
keywords_std = np.std(keyword_scores)
self._debug['keywords']['total'] = sum(keyword_scores)
self._debug['keywords']['mean'] = keywords_mean
self._debug['keywords']['std'] = keywords_std
new_dict = dict([(k, v) for (k, v) in keywords_dict.iteritems()
if v > (keywords_mean + (keywords_std * 4))])
self._keywords = sorted(new_dict.items(),
key=operator.itemgetter(1),
reverse=True)
return self._keywords
except Exception as e:
raise e