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Mastering Python Forensics

Chapter 1. Setting Up the Lab and Introduction to Python ctypes

Cyber Security and Digital Forensics are two topics of increasing importance. Digital forensics especially, is getting more and more important, not only during law enforcement investigations, but also in the field of incident response. During all of the previously mentioned investigations, it's fundamental to get to know the root cause of a security breach, malfunction of a system, or a crime. Digital forensics plays a major role in overcoming these challenges.

In this book, we will teach you how to build your own lab and perform profound digital forensic investigations, which originate from a large range of platforms and systems, with the help of Python. We will start with common Windows and Linux desktop machines, then move forward to cloud and virtualization platforms, and end up with mobile phones. We will not only show you how to examine the data at rest or in transit, but also take a deeper look at the volatile memory.

Python provides an excellent development platform to build your own investigative tools because of its decreased complexity, increased efficiency, large number of third-party libraries, and it's also easy to read and write. During the journey of reading this book, you will not only learn how to use the most common Python libraries and extensions to analyze the evidence, but also how to write your own scripts and helper tools to work faster on the cases or incidents with a huge amount of evidence that has to be analyzed.

Let's begin our journey of mastering Python forensics by setting up our lab environment, followed by a brief introduction of the Python ctypes.

If you have already worked with Python ctypes and have a working lab environment, feel free to skip the first chapter and start directly with one of the other chapters. After the first chapter, the other chapters are fairly independent of each other and can be read in any order.

Setting up the Lab

As a base for our scripts and investigations, we need a comprehensive and powerful lab environment that is able to handle a large number of different file types and structures as well as connections to mobile devices. To achieve this goal, we will use the latest Ubuntu LTS version 14.04.2 and install it in a virtual machine (VM). Within the following sections, we will explain the setup of the VM and introduce Python virtualenv, which we will use to establish our working environment.

Ubuntu

To work in a similar lab environment, we suggest you to download a copy of the latest Ubuntu LTS Desktop Distribution from http://www.ubuntu.com/download/desktop/, preferably the 32-bit version. The distribution provides a simple-to-use UI and already has the Python 2.7.6 environment installed and preconfigured. Throughout the book, we will use Python 2.7.x and not the newer 3.x versions. Several examples and case studies in this book will rely on the tools or libraries that are already a part of the Ubuntu distribution. When a chapter or section of the book requires a third-party package or library, we will provide the additional information on how to install it in the virtualenv (the setup of this environment will be explained in the next section) or on Ubuntu in general.

For better performance of the system, we recommend that the virtual machine that is used for the lab has at least 4 GB of volatile memory and about 40 GB of storage.

Figure 1: The Atom editor

To write your first Python script, you can use a simple editor such as vi or a powerful but cluttered IDE such as eclipse. As a really powerful alternative, we would suggest you to use atom, a very clean but highly customizable editor that can be freely downloaded from https://atom.io/.

Python virtual environment (virtualenv)

According to the official Python documentation, Virtual Environment is a tool to keep the dependencies required by different projects in separate places by creating virtual Python environments for them. It solves the "Project X depends on version 1.x, but Project Y needs 4.x" dilemma and keeps your global site-packages directory clean and manageable.

This is also what we will use in the following chapters to keep a common environment for all the readers of the book and not run into any compatibility issues. First of all, we have to install the virtualenv package. This is done by the following command:

user@lab:~$ pip install virtualenv

We will now create a folder in the users' home directory for our virtual Python environment. This directory will contain the executable Python files and a copy of the pip library, which can be used to install other packages in the environment. The name of the virtual environment (in our case, it is called labenv) can be of your choice. Our virtual lab environment can be created by executing the following command:

user@lab:~$ virtualenv labenv
New python executable in labenv/bin/python
Installing setuptools, pip...done.

To start working with the new lab environment, it first needs to be activated. This can be done through:

user@lab:~$ source labenv/bin/activate
(labenv)user@lab:~$

Now, you can see that the command prompt starts with the name of the virtual environment that we activated. From now on, any package that you install using pip will be placed in the labenv folder, isolated from the global Python installation in the underlying Ubuntu.

Throughout the book, we will use this virtual python environment and install new packages and libraries in it from time to time. So, every time you try to recap a shown example remember or challenge to change into the labenv environment before running your scripts.

If you are done working in the virtual environment for the moment and you want to return to your "normal" Python environment, you can deactivate the virtual environment by executing the following command:

(labenv)user@lab:~$ deactivate
user@lab:~$

This puts you back in the system's default Python interpreter with all its installed libraries and dependencies.

If you are using more than one virtual or physical machine for the investigations, the virtual environments can help you to keep your libraries and packages synced with all these workplaces. In order to ensure that your environments are consistent, it's a good idea to "freeze" the current state of environment packages. To do this, just run:

(labenv)user@lab:~$ pip freeze > requirenments.txt

This will create a requirements.txt file, which contains a simple list of all the packages in the current environment and their respective versions. If you want to now install the same packages using the same version on a different machine, just copy the requirements.txt file to the desired machine, create the labenv environment as described earlier and execute the following command:

(labenv)user@lab:~$ pip install -r requirements.txt

Now, you will have consistent Python environments on all the machines and don't need to worry about different library versions or other dependencies.

After we have created the Ubuntu virtual machine with our dedicated lab environment, we are nearly ready to start our first forensic analysis. But before that, we need more knowledge of the helpful Python libraries and backgrounds. Therefore, we will start with an introduction to the Python ctypes in the following section.

Introduction to Python ctypes

According to the official Python documentation, ctypes is a foreign function library that provides C compatible data types and allows calling functions in DLLs or shared libraries. A foreign function library means that the Python code can call C functions using only Python, without requiring special or custom-made extensions.

This module is one of the most powerful libraries available to the Python developer. The ctypes library enables you to not only call functions in dynamically linked libraries (as described earlier), but can also be used for low-level memory manipulation. It is important that you understand the basics of how to use the ctypes library as it will be used for many examples and real-world cases throughout the book.

In the following sections, we will introduce some basic features of Python ctypes and how to use them.

Working with Dynamic Link Libraries

Python ctypes export the cdll and on Windows windll or respectively oledll objects, to load the requested dynamic link libraries. A dynamically linked library is a compiled binary that is linked at runtime to the executable main process. On Windows platforms, these binaries are called Dynamic Link Libraries (DLL) and on Linux, they are called shared objects (SO). You can load these linked libraries by accessing them as the attributes of the cdll, windll or oledll objects. Now, we will demonstrate a very brief example for Windows and Linux to get the current time directly out of the time function in libc (this library defines the system calls and other basic facilities such as open, printf, or exit).

Note that in the case of Windows, msvcrt is the MS standard C library containing most of the standard C functions and uses the cdecl calling convention (on Linux systems, the similar library would be libc.so.6):

C:\Users\Admin>python

>>> from ctypes import *
>>> libc = cdll.msvcrt
>>> print libc.time(None)
1428180920

Windows appends the usual .dll file suffix automatically. On Linux, it is required to specify the filename, including the extension, to load the chosen library. Either the LoadLibrary() method of the DLL loaders should be used or you should load the library by creating an instance of CDLL by calling the constructor, as shown in the following code:

(labenv)user@lab:~$ python

>>> from ctypes import *
>>> libc = CDLL("libc.so.6")
>>> print libc.time(None)
1428180920

As shown in these two examples, it is very easy to be able to call to a dynamic library and use a function that is exported. You will be using this technique many times throughout the book, so it is important that you understand how it works.

C data types

When looking at the two examples from the earlier section in detail, you can see that we use None as one of the parameters for a dynamically linked C library. This is possible because None, integers, longs, byte strings, and unicode strings are the native Python objects that can be directly used as the parameters in these function calls. None is passed as a C, NULL pointer, byte strings, and unicode strings are passed as pointers to the memory block that contains their data (char * or wchar_t *). Python integers and Python longs are passed as the platform's default C int type, their value is masked to fit into the C type. A complete overview of the Python types and their corresponding ctype types can be seen in Table 1:

ctypes type	C type	Python type
c_bool (https://docs.python.org/2/library/ctypes.html#ctypes.c_bool)	_Bool	bool (1)
c_char (https://docs.python.org/2/library/ctypes.html#ctypes.c_char)	char	1-character string
c_wchar (https://docs.python.org/2/library/ctypes.html#ctypes.c_wchar)	wchar_t	1-character unicode string
c_byte (https://docs.python.org/2/library/ctypes.html#ctypes.c_byte)	char	int/long
c_ubyte (https://docs.python.org/2/library/ctypes.html#ctypes.c_ubyte)	unsigned char	int/long
c_short (https://docs.python.org/2/library/ctypes.html#ctypes.c_short)	short	int/long
c_ushort (https://docs.python.org/2/library/ctypes.html#ctypes.c_ushort)	unsigned short	int/long
c_int (https://docs.python.org/2/library/ctypes.html#ctypes.c_int)	int	int/long
c_uint (https://docs.python.org/2/library/ctypes.html#ctypes.c_uint)	unsigned int	int/long
c_long (https://docs.python.org/2/library/ctypes.html#ctypes.c_long)	long	int/long
c_ulong (https://docs.python.org/2/library/ctypes.html#ctypes.c_ulong)	unsigned long	int/long
c_longlong (https://docs.python.org/2/library/ctypes.html#ctypes.c_longlong)	__int64 or long long	int/long
c_ulonglong (https://docs.python.org/2/library/ctypes.html#ctypes.c_ulonglong)	unsigned __int64 or unsigned long long	int/long
c_float (https://docs.python.org/2/library/ctypes.html#ctypes.c_float)	float	float
c_double (https://docs.python.org/2/library/ctypes.html#ctypes.c_double)	double	float
c_longdouble (https://docs.python.org/2/library/ctypes.html#ctypes.c_longdouble)	long double	float
c_char_p (https://docs.python.org/2/library/ctypes.html#ctypes.c_char_p)	char * (NUL terminated)	string or None
c_wchar_p (https://docs.python.org/2/library/ctypes.html#ctypes.c_wchar_p)	wchar_t * (NUL terminated)	unicode or None
c_void_p (https://docs.python.org/2/library/ctypes.html#ctypes.c_void_p)	void *	int/long or None

Table 1: Fundamental Data Types

This table is very helpful because all the Python types except integers, strings, and unicode strings have to be wrapped in their corresponding ctypes type so that they can be converted to the required C data type in the linked library and not throw the TypeError exceptions, as shown in the following code:

(labenv)user@lab:~$ python

>>> from ctypes import *
>>> libc = CDLL("libc.so.6")
>>> printf = libc.printf

>>> printf("An int %d, a double %f\n", 4711, 47.11)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ctypes.ArgumentError: argument 3: <type 'exceptions.TypeError'>: Don't know how to convert parameter 3

>>> printf("An int %d, a double %f\n", 4711, c_double(47.11))
An int 4711, a double 47.110000

Defining Unions and Structures

Unions and Structures are important data types because they are frequently used throughout the libc on Linux and also in the Microsoft Win32 API.

Unions are simply a group of variables, which can be of the same or different data types, where all of its members share the same memory location. By storing variables in this way, unions allow you to specify the same value in different types. For the upcoming example, we will change from the interactive Python shell to the atom editor on our Ubuntu lab environment. You just need to open atom editor, type in the following code, and save it under the name new_evidence.py:

from ctypes import *

class case(Union):
        _fields_ = [
        ("evidence_int", c_int),
        ("evidence_long", c_long),
        ("evidence_char", c_char * 4)
        ]

value = raw_input("Enter new evidence number:")
new_evidence = case(int(value))
print "Evidence number as a int: %i" % new_evidence.evidence_int
print "Evidence number as a long: %ld" % new_evidence.evidence_long
print "Evidence number as a char: %s" % new_evidence.evidence_char

If you assign the evidence union's member variable evidence_int a value of 42, you can then use the evidence_char member to display the character representation of that number, as shown in the following example:

(labenv)user@lab:~$ python new_evidence.py

Enter new evidence number:42

Evidence number as a long: 42
Evidence number as a int: 42
Evidence number as a char: *

As you can see in the preceding example, by assigning the union a single value, you get three different representations of that value. For int and long, the displayed output is obvious but for the evidence_char variable, it could be a bit confusing. In this case, '*' is the ASCII character with the value of the equivalent of decimal 42. The evidence_char member variable is a good example of how to define an array in ctypes. In ctypes, an array is defined by multiplying a type by the number of elements that you want to allocate in the array. In this example, a four-element character array was defined for the member variable evidence_char.

A structure is very similar to unions, but the members do not share the same memory location. You can access any of the member variables in the structure using dot notation, such as case.name. This would access the name variable contained in the case structure. The following is a very brief example of how to create a structure (or struct, as they are often called) with three members: name, number, and investigator_name so that all can be accessed by the dot notation:

from ctypes import *

class case(Structure):
        _fields_ = [
        ("name", c_char * 16),
        ("number", c_int),
        ("investigator_name", c_char * 8)
        ]

Tip

Downloading the example code

You can download the example code files from your account at http://www.packtpub.com for all the Packt Publishing books you have purchased. If you purchased this book elsewhere, you can visit http://www.packtpub.com/support and register to have the files e-mailed directly to you.

Key benefits

Learn to perform forensic analysis and investigations with the help of Python, and gain an advanced understanding of the various Python libraries and frameworks

Analyze Python scripts to extract metadata and investigate forensic artifacts

The writers, Dr. Michael Spreitzenbarth and Dr. Johann Uhrmann, have used their experience to craft this hands-on guide to using Python for forensic analysis and investigations

Description

Digital forensic analysis is the process of examining and extracting data digitally and examining it. Python has the combination of power, expressiveness, and ease of use that makes it an essential complementary tool to the traditional, off-the-shelf digital forensic tools. This book will teach you how to perform forensic analysis and investigations by exploring the capabilities of various Python libraries. The book starts by explaining the building blocks of the Python programming language, especially ctypes in-depth, along with how to automate typical tasks in file system analysis, common correlation tasks to discover anomalies, as well as templates for investigations. Next, we’ll show you cryptographic algorithms that can be used during forensic investigations to check for known files or to compare suspicious files with online services such as VirusTotal or Mobile-Sandbox. Moving on, you’ll learn how to sniff on the network, generate and analyze network flows, and perform log correlation with the help of Python scripts and tools. You’ll get to know about the concepts of virtualization and how virtualization influences IT forensics, and you’ll discover how to perform forensic analysis of a jailbroken/rooted mobile device that is based on iOS or Android. Finally, the book teaches you how to analyze volatile memory and search for known malware samples based on YARA rules.

What you will learn

[*] Explore the forensic analysis of different platforms such as Windows, Android, and vSphere [*] Semi-automatically reconstruct major parts of the system activity and time-line [*] Leverage Python ctypes for protocol decoding [*] Examine artifacts from mobile, Skype, and browsers [*] Discover how to utilize Python to improve the focus of your analysis [*] Investigate in volatile memory with the help of volatility on the Android and Linux platforms

What do you get with Print?

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Mastering Python Forensics: Master the art of digital forensics and analysis with Python

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Mastering Python Forensics

Chapter 1. Setting Up the Lab and Introduction to Python ctypes

Setting up the Lab

Ubuntu

Python virtual environment (virtualenv)

Introduction to Python ctypes

Working with Dynamic Link Libraries

C data types

Defining Unions and Structures

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Mastering Python Forensics: Master the art of digital forensics and analysis with Python

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