Source code for netzob.Inference.Vocabulary.EntropyMeasurement

# -*- coding: utf-8 -*-

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#|          01001110 01100101 01110100 01111010 01101111 01100010            |
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#|               Netzob : Inferring communication protocols                  |
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#| Copyright (C) 2011-2017 Georges Bossert and Frédéric Guihéry              |
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#| @url      : http://www.netzob.org                                         |
#| @contact  : contact@netzob.org                                            |
#| @sponsors : Amossys, http://www.amossys.fr                                |
#|             Supélec, http://www.rennes.supelec.fr/ren/rd/cidre/           |
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#| Standard library imports
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import math

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#| Related third party imports
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#| Local application imports
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from netzob.Common.Utils.Decorators import NetzobLogger


@NetzobLogger
[docs]class EntropyMeasurement(object): """This utility class exposes various methods related to Entropy. This measure can be usefull to identify encrypted and compressed chunk of data accross various messages. By entropy we refer to the Shanon's one. >>> import binascii >>> from netzob.all import * >>> fake_random_values = [b"00000906", b"00110906", b"00560902", b"00ff0901"] >>> messages = [RawMessage(binascii.unhexlify(val)) for val in fake_random_values] >>> [byte_entropy for byte_entropy in EntropyMeasurement.measure_entropy(messages)] [0.0, 2.0, 0.0, 1.5] In the following example, 1000 messages are generated under a simple specification. In the specification, 5 bytes are randomly generated. This specificity can easily be spoited by the entropy measurement as illustred below. >>> f1 = Field(b"hello ") >>> f2 = Field(Raw(nbBytes=5)) >>> f3 = Field(b", welcome !") >>> s = Symbol(fields=[f1, f2, f3]) >>> messages = [RawMessage(s.specialize()) for x in range(1000)] >>> bytes_entropy = [byte_entropy for byte_entropy in EntropyMeasurement.measure_entropy(messages)] >>> min(bytes_entropy[6:11]) > 7 True You can also measure the entropy of the data that are accepeted by a specific field. >>> f1 = Field(Raw(nbBytes=2)) >>> f2 = Field(Raw(nbBytes=(10, 20))) >>> f3 = Field(Raw(nbBytes=2)) >>> s = Symbol(fields=[f1, f2, f3]) >>> s.messages = [RawMessage(s.specialize()) for x in range(1000)] >>> bytes_entropy = [byte_entropy for byte_entropy in EntropyMeasurement.measure_values_entropy(f2.getValues())] >>> print(min(bytes_entropy[:10]) > 7) True """ def __init__(self): pass @staticmethod
[docs] def measure_entropy(messages): """This method returns the entropy of bytes found at each position of the messages. >>> [x for x in EntropyMeasurement.measure_entropy(messages=None)] Traceback (most recent call last): ... Exception: Messages cannot be None >>> from netzob.all import * >>> [x for x in EntropyMeasurement.measure_entropy(messages=[RawMessage()])] Traceback (most recent call last): ... Exception: At least two messages must be provided """ if messages is None: raise Exception("Messages cannot be None") if len(messages) < 2: raise Exception("At least two messages must be provided") values = [m.data for m in messages] return EntropyMeasurement.measure_values_entropy(values)
@staticmethod
[docs] def measure_values_entropy(values): """This method returns the entropy of bytes found at each position of the specified values. >>> [x for x in EntropyMeasurement.measure_values_entropy(values=None)] Traceback (most recent call last): ... Exception: values cannot be None >>> from netzob.all import * >>> [x for x in EntropyMeasurement.measure_values_entropy(values=[])] Traceback (most recent call last): ... Exception: At least one value must be provided """ if values is None: raise Exception("values cannot be None") if len(values) < 1: raise Exception("At least one value must be provided") # computes longuest message longuest = max([len(value) for value in values]) for i_byte in range(longuest): dataset = [] for value in values: try: dataset.append(value[i_byte]) except Exception: pass yield EntropyMeasurement.__measure_entropy(dataset)
@staticmethod def __measure_entropy(values): if values is None: raise Exception("values cannot be None") if len(values) < 1: raise Exception("At least one value must be provided") entropy = 0 for x in range(256): p_x = float(values.count(x)) / len(values) if p_x > 0: entropy += -p_x * math.log(p_x, 2) return entropy