def compare(self, hash1, hash2):
if hash1 == '-' or hash2 == '-':
return '-'
distance = tlsh.diffxlen(hash1, hash2)
#if distance > TLSH_MAX_SIMILARITY:
# return '-'
return distance
def match_hash(self, signal_str: str) -> t.List[signal_base.SignalMatch]:
matches = []
if len(signal_str) == EXPECT_TLSH_HASH_LENGTH:
for x in TEMP_MATCH_IMPLEMNTATION_CHECK_DB:
if tlsh.diffxlen(x[0],
signal_str) <= TLSH_CONFIDENT_MATCH_THRESHOLD:
matches.append(signal_base.SignalMatch(x[1][0], x[1][1]))
return matches
def compare_hash(
cls,
hash1: str,
hash2: str,
distance_threshold: t.Optional[int] = None
) -> signal_base.HashComparisonResult:
if distance_threshold is None:
distance_threshold = TLSH_CONFIDENT_MATCH_THRESHOLD
dist = tlsh.diffxlen(hash1, hash2)
return signal_base.HashComparisonResult.from_dist(
dist, distance_threshold)
def TO_OVERWRITE_compute_distance(self, pic1: picture_class.Picture,
pic2: picture_class.Picture):
dist = None
if self.conf.ALGO == configuration.ALGO_TYPE.TLSH:
dist = tlsh.diff(pic1.hash, pic2.hash)
elif self.conf.ALGO == configuration.ALGO_TYPE.TLSH_NO_LENGTH:
dist = tlsh.diffxlen(pic1.hash, pic2.hash)
else:
raise Exception(
"Invalid algorithm type for TLSH execution handler during distance computing : "
+ str(self.conf.ALGO.name))
return dist
def tlsh_score(response, site, alert):
"""
Caculate TLSH Score.
:param response: Http response.
:param site: Site Object.
:param alert: Alert Integer.
:return: alert, score
:rtype: int, int
"""
fuzzy_hash = tlsh.hash(bytes(response.text, 'utf-8'))
score = tlsh.diffxlen(site.content_fuzzy_hash, fuzzy_hash)
if score > 160:
alert += 4
Site.objects.filter(pk=site.pk).update(content_fuzzy_hash=fuzzy_hash)
return alert, score
def match_hash(self, signal_str: str) -> t.List[signal_base.SignalMatch]:
matches = []
try:
import tlsh
except:
warnings.warn(
"Matching a tlsh hash requires additional libraries already be installed; install threatexchange with the [pdf] extra",
category=UserWarning,
)
return []
if len(signal_str) == EXPECT_TLSH_HASH_LENGTH:
for tlsh_hash, signal_attr in self.state.items():
if (tlsh.diffxlen(tlsh_hash, signal_str) <=
TLSH_CONFIDENT_MATCH_THRESHOLD):
matches.append(
signal_base.SignalMatch(
signal_attr.labels,
signal_attr.first_descriptor_id))
return matches
def compare(self, h1, h2):
return tlsh.diffxlen(h1, h2)
for dico_name in dico_range_list:
opened_dico.append([dico_name, dico_redis[dico_name]])
# retrieve hash from paste
paste_hashes = PST._get_p_hash()
# Go throught the Database of the dico (of the month)
for curr_dico_name, curr_dico_redis in opened_dico:
for hash_type, paste_hash in paste_hashes.items():
for dico_hash in curr_dico_redis.smembers('HASHS_'+hash_type):
try:
if hash_type == 'ssdeep':
percent = 100-ssdeep.compare(dico_hash, paste_hash)
else:
percent = tlsh.diffxlen(dico_hash, paste_hash)
if percent > 100:
percent = 100
threshold_duplicate = threshold_set[hash_type]
if percent < threshold_duplicate:
percent = 100 - percent if hash_type == 'ssdeep' else percent #recovert the correct percent value for ssdeep
# Go throught the Database of the dico filter (month)
r_serv_dico = dico_redis[curr_dico_name]
# index of paste
index_current = r_serv_dico.get(dico_hash)
index_current = index_current
paste_path = r_serv_dico.get(index_current)
paste_path = paste_path
paste_date = r_serv_dico.get(index_current+'_date')
print('Across ' + str(txn_count) + ' transactions (occurrences).')
else:
print('An error has occurred while summing amounts by account:' + n)
print(response.status_code)
'''Everything we just did, is now a API Endpoint: pivot/by/btc'''
response = requests.get(
'https://ransomcoindb.concinnity-risks.com/api/v2/pivot/by/btc/13AM4VW2dhxYgXeQepoHkHSQuy6NgaEb94?currency=usd&limit=1000',
headers={
'accept': 'application/json',
'x-api-key': api_key
},
)
print('Everything we just did, is now a API Endpoint: pivot/by/btc')
trx_function_data = pd.DataFrame.from_dict(response.json())
print(trx_function_data)
'''Let us go back to TLSH though to show you some magic'''
wcry_bindist = []
for i in df.tlsh:
for j in df.tlsh:
if i != j:
wcry_bindist.append(tlsh.diffxlen(i, j))
print('Let us look for new information with binary distance tricks.')
print('A good threshold for this set of binaries would be: ' +
str(sum(wcry_bindist) / len(wcry_bindist)))
print('Min distance is: ' + str(min(wcry_bindist)))
print('Max distance is: ' + str(max(wcry_bindist)))
print(
'Thus further work could identify similar malware based on the TLSH distance, even if it does not have the BTC addresses within it.'
)
opened_dico = []
for dico_name in dico_range_list:
opened_dico.append([dico_name, dico_redis[dico_name]])
# retrieve hash from paste
paste_hashes = PST._get_p_hash()
# Go throught the Database of the dico (of the month)
for curr_dico_name, curr_dico_redis in opened_dico:
for hash_type, paste_hash in paste_hashes.iteritems():
for dico_hash in curr_dico_redis.smembers('HASHS_'+hash_type):
try:
if hash_type == 'ssdeep':
percent = 100-ssdeep.compare(dico_hash, paste_hash)
else:
percent = tlsh.diffxlen(dico_hash, paste_hash)
threshold_duplicate = threshold_set[hash_type]
if percent < threshold_duplicate:
percent = 100 - percent if hash_type == 'ssdeep' else percent #recovert the correct percent value for ssdeep
# Go throught the Database of the dico filter (month)
r_serv_dico = dico_redis[curr_dico_name]
# index of paste
index_current = r_serv_dico.get(dico_hash)
paste_path = r_serv_dico.get(index_current)
if paste_path != None:
hash_dico[dico_hash] = (hash_type, paste_path, percent)
print '['+hash_type+'] '+'comparing: ' + str(PST.p_path[44:]) + ' and ' + str(paste_path[44:]) + ' percentage: ' + str(percent)
except Exception,e:
def simTLSH(h1,h2): return tlsh.diffxlen(h1,h2)
def detector(inputDict, inputRepo):
componentDB = {}
componentDB = readComponentDB()
fres = open(resultPath + "result_" + inputRepo, 'w')
aveFuncs = getAveFuncs()
for OSS in componentDB:
commonFunc = []
repoName = OSS.split('_sig')[0]
totOSSFuncs = float(aveFuncs[repoName])
if totOSSFuncs == 0.0:
continue
comOSSFuncs = 0.0
for hashval in componentDB[OSS]:
if hashval in inputDict:
commonFunc.append(hashval)
comOSSFuncs += 1.0
if (comOSSFuncs / totOSSFuncs) >= theta:
verPredictDict = {}
allVerList, idx2Ver = readAllVers(repoName)
for eachVersion in allVerList:
verPredictDict[eachVersion] = 0.0
weightDict = readWeigts(repoName)
with open(initialDBPath + OSS, 'r', encoding="UTF-8") as fi:
jsonLst = json.load(fi)
for eachHash in jsonLst:
hashval = eachHash["hash"]
verlist = eachHash["vers"]
if hashval in commonFunc:
for addedVer in verlist:
verPredictDict[
idx2Ver[addedVer]] += weightDict[hashval]
sortedByWeight = sorted(verPredictDict.items(),
key=lambda x: x[1],
reverse=True)
predictedVer = sortedByWeight[0][0]
predictOSSDict = {}
with open(repoFuncPath + repoName + '/fuzzy_' + predictedVer +
'.hidx',
'r',
encoding="UTF-8") as fo:
body = ''.join(fo.readlines()).strip()
for eachLine in body.split('\n')[1:]:
ohash = eachLine.split('\t')[0]
opath = eachLine.split('\t')[1]
predictOSSDict[ohash] = opath.split('\t')
used = 0
unused = 0
modified = 0
strChange = False
for ohash in predictOSSDict:
flag = 0
for thash in inputDict:
if ohash == thash:
used += 1
nflag = 0
for opath in predictOSSDict[ohash]:
for tpath in inputDict[thash]:
if opath in tpath:
nflag = 1
if nflag == 0:
strChange = True
flag = 1
else:
score = tlsh.diffxlen(ohash, thash)
if int(score) <= 30:
modified += 1
nflag = 0
for opath in predictOSSDict[ohash]:
for tpath in inputDict[thash]:
if opath in tpath:
nflag = 1
if nflag == 0:
strChange = True
flag = 1
if flag == 0:
unused += 1
fres.write('\t'.join([
inputRepo, repoName, predictedVer,
str(used),
str(unused),
str(modified),
str(strChange)
]) + '\n')
fres.close()