PyMISP/pymisp/tools/fileobject.py

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2017-07-21 18:47:10 +02:00
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from pymisp.tools import MISPObjectGenerator
import os
from io import BytesIO
from hashlib import md5, sha1, sha256, sha512
import math
from collections import Counter
try:
import pydeep
HAS_PYDEEP = True
except ImportError:
HAS_PYDEEP = False
try:
import magic
HAS_MAGIC = True
except ImportError:
HAS_MAGIC = False
class FileObject(MISPObjectGenerator):
def __init__(self, filepath=None, pseudofile=None, filename=None):
if not HAS_PYDEEP:
raise ImportError("Please install pydeep: pip install git+https://github.com/kbandla/pydeep.git")
if not HAS_MAGIC:
raise ImportError("Please install python-magic: pip install python-magic.")
if filepath:
self.filepath = filepath
self.filename = os.path.basename(self.filepath)
with open(filepath, 'rb') as f:
self.pseudofile = BytesIO(f.read())
elif pseudofile and isinstance(pseudofile, BytesIO):
# WARNING: lief.parse requires a path
self.filepath = None
self.pseudofile = pseudofile
self.filename = filename
else:
raise Exception('File buffer (BytesIO) or a path is required.')
MISPObjectGenerator.__init__(self, 'file')
self.data = self.pseudofile.getvalue()
self.generate_attributes()
def generate_attributes(self):
self._create_attribute('filename', value=self.filename)
self._create_attribute('size-in-bytes', value=len(self.data))
if getattr(self, 'size-in-bytes').value > 0:
self._create_attribute('entropy', value=self.__entropy_H(self.data))
self._create_attribute('md5', value=md5(self.data).hexdigest())
self._create_attribute('sha1', value=sha1(self.data).hexdigest())
self._create_attribute('sha256', value=sha256(self.data).hexdigest())
self._create_attribute('sha512', value=sha512(self.data).hexdigest())
self._create_attribute('mimetype', value=magic.from_buffer(self.data))
self._create_attribute('ssdeep', value=pydeep.hash_buf(self.data).decode())
self._create_attribute('malware-sample', value=self.filename.value, data=self.pseudofile)
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def __entropy_H(self, data):
"""Calculate the entropy of a chunk of data."""
# NOTE: copy of the entropy function from pefile
if len(data) == 0:
return 0.0
occurences = Counter(bytearray(data))
entropy = 0
for x in occurences.values():
p_x = float(x) / len(data)
entropy -= p_x * math.log(p_x, 2)
return entropy