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Practical Windows Forensics

Book Image

Practical Windows Forensics

Overview of this book

Over the last few years, the wave of the cybercrime has risen rapidly. We have witnessed many major attacks on the governmental, military, financial, and media sectors. Tracking all these attacks and crimes requires a deep understanding of operating system operations, how to extract evident data from digital evidence, and the best usage of the digital forensic tools and techniques. Regardless of your level of experience in the field of information security in general, this book will fully introduce you to digital forensics. It will provide you with the knowledge needed to assemble different types of evidence effectively, and walk you through the various stages of the analysis process. We start by discussing the principles of the digital forensics process and move on to show you the approaches that are used to conduct analysis. We will then study various tools to perform live analysis, and go through different techniques to analyze volatile and non-volatile data.

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Practical Windows Forensics
Jun, 2016 | 322 pages
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Table of Contents (20 chapters)
Practical Windows Forensics
Practical Windows Forensics
Credits
Credits
About the Authors
About the Authors
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
The Foundations and Principles of Digital Forensics
The Foundations and Principles of Digital Forensics
What is digital crime?
Digital forensics
Digital evidence
Digital forensic goals
Analysis approaches
Summary
2
Incident Response and Live Analysis
Incident Response and Live Analysis
Personal skills
Security fundamentals
The hardware for IR and Jump Bag
Remote live response
Summary
3
Volatile Data Collection
Volatile Data Collection
Memory acquisition
Network-based data collection
Summary
4
Nonvolatile Data Acquisition
Nonvolatile Data Acquisition
Forensic image
Incident Response CDs
Live imaging of a hard drive
Linux for the imaging of a hard drive
Virtualization in data acquisition
Evidence integrity (the hash function)
Disk wiping in Linux
Summary
5
Timeline
Timeline
Timeline introduction
The Sleuth Kit
Super timeline – Plaso
Plaso architecture
Plaso in practice
Summary
6
Filesystem Analysis and Data Recovery
Filesystem Analysis and Data Recovery
Hard drive structure
The FAT filesystem
The NTFS filesystem
The Sleuth Kit (TSK)
Autopsy
Foremost
Summary
7
Registry Analysis
Registry Analysis
The registry structure
Backing up the registry files
Extracting registry hives
Parsing registry files
Auto-run keys
Registry analysis
Summary
8
Event Log Analysis
Event Log Analysis
Event Logs - an introduction
Event Logs system
Extracting Event Logs
Summary
9
Windows Files
Windows Files
Windows prefetch files
Windows tasks
Windows Thumbs DB
Windows RecycleBin
Windows shortcut files
Summary
10
Browser and E-mail Investigation
Browser and E-mail Investigation
Browser investigation
Microsoft Internet Explorer
Firefox
Other browsers
E-mail investigation
Summary
11
Memory Forensics
Memory Forensics
Memory structure
Memory acquisition
The sources of memory dump
Processes in memory
Network connections in memory
The DLL injection
API hooking
Memory analysis
Summary
12
Network Forensics
Network Forensics
Network data collection
Exploring logs
Using tcpdump
Using tshark
Using WireShark
Knowing Bro
Summary
Building a Forensic Analysis Environment
Building a Forensic Analysis Environment
Factors that need to be considered
Case Study
Case Study
Introduction
Scenario
Acquisition
Live analysis
Prefetch files
Browser analysis
Postmortem analysis
Summary

Network data collection

All data that can be retrieved from the network traffic can be divided into several levels:

  • Full Packet Capture 100%

  • Packet String Data 4%

  • Sessions 0.1%

  • Statistics

  • Logs

It is obvious that, from the point of view of a forensics analyst, the most preferred method is to collect full traffic, as in this case, we obtain the most complete dataset.

However, along with the obvious advantages, this approach has a number of drawbacks. A large amount of data for storage and subsequent analysis requires a lot of time and resources.

At the same time, other forms of data, such as NetFlow, in many cases is a reasonable alternative, and it requires fewer resources for the collection and storage and to process.

Compared to other forms of full traffic, data altogether constitutes only a few percent. It require less space for storage and, therefore, can be stored for a longer time period.

For clarity, consider the following example. Let's suppose an organization has a daily volume of network...

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